With advancements in technology and most of the population having access to the internet, there is no denying that data analytics has become a hot topic in recent years. The data analytics job opportunities landscape looks promising and ranges to several industries, in fact, the nature of work often allows flexibility of working remotely or even self-employment. Besides that, even at the entry-level jobs the median salary for a data analytics job is quite high. According to a study, it is predicted that in the US alone 2.7 million job postings are estimated by 2020.
As more and more organizations are recognizing the importance of Big Data as a source to gain insights and make strategic decisions, the demand for skilled people in data analytics is increasing manifolds. Keeping this in mind we have compiled here some of the best data analytics job opportunities in the market today.
However, before we begin, let us take a brief look at the skills that will be required for a job in data analytics.
Most of the positions in the field of data analytics require the same foundation skills so the key is to master these before you start posting your resume to potential employers.
Python- This is one of the most commonly used programming languages. You may look up for an online Python tutorial to learn the basics of Python. It is important to have a solid understanding of how to use Python for data analytics even if is not a required skill in any job position since it will give you an upper hand in the job market. Though most of the online Python tutorial gives basic knowledge, however, you should also look for advanced programming proficiency to learn analyzing and manipulation of data. Also, understand the concept of data collection, web scraping and start building web applications.
SQL- Having a basic knowledge of SQL is often required for data analytics job roles. When going for an interview always go through SQL interview questions which are often asked by hiring managers. You can get the basic knowledge of SQL through a SQL tutorial online. Just like Python, SQL is also a relatively easy language to learn and the basic knowledge of SQL will take you a long way in your career.
Here are the few job opportunities worth looking into-
IT Systems Analyst
The required level of expertise differs in these positions thus creating opportunities for specialization by personal interest and industry. The system analysts design and use systems to solve problems in IT.
As a data engineer, you will be designing and implementing data infrastructure. This position is a step up in complexity, however, it’s your skill, knowledge, and preference that will be the deciding factor.
Healthcare Data Analyst
The healthcare data analysts can help doctors, scientists by finding answers to the problems and questions they encounter on a daily basis. With the growing amount of data in the health industry coming from the popularity of Apple watch or advanced medical testing in clinics, labs, and hospitals, the demand of data analysts in the sector is on the rise.
If you like working with big data frameworks or creating dashboards or like analysis or querying of data then this is a perfect job role for you.
Operation Analysts are typically found internally at big organizations but they may also work as consultants. The key responsibility is to focus on the internal process of a business. The professionals need to need to be general business savvy and also need to possess technical knowledge of the systems they work with. So from large grocery chains to military services, opportunities are many.
The professionals need to have fluency in programming to statistical and querying capabilities to managing, extracting, and designing to conducting initial exploratory analysis. Data Scientists also need to figure out the machine learning algorithm that will help in performing predictive analysis, visualizing the results and presenting it to the management.
This is one of the most sought-after job roles, especially in the financial sector. Data analytics is used to seek out risk management problems or potential financial opportunities. A quantitative analyst can venture out on his own to create trading models to predict the price of commodities, stocks, etc.
Digital Analytics Consultant
The primary responsibility is to deliver insights into a company to help in business. As a consultant, you can work for different companies in a small period of time.
Besides the above, you can also work as Project Manager, Digital Marketing Manager or Transportation logistics specialist if you have a data analytics background.
These were just a few of many high-paying jobs, the salaries may differ according to the city/country and the general cost-of-living expenses.
With a boom in artificial intelligence, data science, and machine learning applications, the demand for Python developers has also increased. Python’s ease of access and readability has made it one of the most popular programming languages today. Switching over to Python can unleash endless possibilities for developers. Here, we have identified some of the most important Python concepts which you should know.
Most online tutorial courses assume that you need to learn all syntaxes in Python before you start doing anything interesting. This may lead to spending months only on Python syntax when what you want to be doing is to analyze the data or build a website or maybe create a drone!So here are the 7 Python concepts that you need to focus on besides the Python syntax while you take up an online Python tutorial-
Variables- object types and scope
Information that can be used for a program is stored in variables and they typically have a name so that they can be referenced in the code. Python supports strings, numbers, lists, sets, tuples, and dictionaries which are standard data types. If you check any online Python tutorial you can read in detail about these data types.
In Python, if you have to declare a variable, you only have to assign a value to it. There is no need for any additional commands. Variables can have local or global scope. One of the most common Python questions asked in an interview is – Mention what are the rules for local and global variables in Python?
Therefore, ensure that you know this concept thoroughly as it forms the basis of your programming.
It allows the user to perform computation on variables. The following are the different types of python operators.
Comments are used to make the code more readable. It helps in explaining the Python code and can also help in preventing execution when testing code. Comments in Python start with a ‘#’. It can be placed at the end of a line, and the rest of the line will be ignored in Python. You can refer to online Python tutorial for usage of comments in detail.
Loops in Python
Repetitive commands or redundant codes can be a nightmare for any programmer. Python uses loops to overcome this problem. The loops allow you to execute a group of statements numerous times. Loops in Python are categorized as –
Sets and dictionaries in Python are almost identical, except that sets do not contain values actually, it is just a collection of unique keys. Sets are used in doing set operations whereas Python dictionary is a collection that is changeable, unordered, and indexed. The items in a Python dictionary are accessible by referring to its key name.
Therefore, learning the concept of Python dictionaries and sets is essential. If you are taking up an online python tutorial then do learn about Python dictionary with methods, functions, and operations. There are a few in-built dictionary methods in Python which can help you in programming.
Classes and Functions
Python is an object-oriented language, therefore it is important to know the concept of classes thoroughly. A Python class is like a blueprint of an object that provides all the standard features of object-oriented programming. The classes can have custom attributes/ properties and functions. The object-oriented design allows the programmers to define their business model as objects with their required functions and properties.
On the other hand, functions in Python are a sequence of statements that you can execute in your code. It helps in eliminating the repetition of code and make it simpler to debug or find issues. Most importantly, functions make the code more understandable and simpler to manage.
Slicing in Python is most commonly applied to lists and strings. It is a process of taking a subset of any data. To put it simply, slicing enables the programmer to choose what to see and focus on thus aiding in implementing abstractions and readability.
We hope that you find the above useful. To understand these concepts in detail do go through Python problem sets while you undertake a full-fledged online python tutorial.
Are you preparing for a SQL developer interview?
Then read on as we give you the 7 crucial concepts in SQL that you must know thoroughly to help you sail through the interview.
Getting to know SQL
Structured Query Language or SQL is one of the most common languages for organizing and extracting data that is stored in relational databases. This language is a mainstay for most of the people working with data since most of the databases are managed relationally, thus making this language indispensable. SQL is used by data analysts to query tables of data and derive insights from it. It is used by data scientists to load data into their models. Similarly, data engineers and database administrators use SQL to ensure that everyone in their organization has easy and intuitive access to the data they need.
Interviews always depend on your knowledge and experience. However, there are some important concepts in SQL which you must cover. These topics will help you in basic as well as advanced SQL interview questions.
Tables are one of the most basic concepts in SQL that you need to understand as you may find SQL interview questions based on this. For instance- What do you mean by tables in SQL? This is one of the most common question asked by the interviewers, hence knowing what SQL table means is important. A table is a unique set of data with a consistent number of columns or typed data attributes. Each table should have a primary key i.e. a column that uniquely identifies a row.
SQL interview questions based on relationships in SQL are also frequently asked. For instance- What are relationships? Relationships are the links or relations between entities that have something to do with each other. So when two tables are joined, one is always considered as the ‘parent’ in the relationship and the other one as a ‘child’. Relationships and tables are the basic knowledge of SQL that an aspiring SQL developer should have.
Once you understand the basic knowledge of SQL tables and relations, you will be ready to build an understanding of what relationship means. To begin with, you need to understand the modality or the Ordinality of the relationships which specify whether relationship from the parent table to the child table is mandatory or optional.
The next important concept is cardinality or multiplicity of relationships. The SQL interview questions are often based around relationships in SQL to understand if the candidate has the basic knowledge of SQL. Cardinality is either one-to-one or one-to-many or many-to-many.
Both Ordinality and cardinality only scratch the surface of a database structure. Once you are clear with these concepts you can move on to more advanced concepts in SQL such as normalization and identifying relationships. For instance – one of the frequently asked SQL interview questions is – What is normalization and what are the advantages of it? Or explain the different types of normalization?
The concept of index also needs to be learnt thoroughly as one or two SQL interview questions are often based on this. For instance- What is an Index? Or explain the different types of index?
An index is a performance tuning method that allows faster retrieval of records from the table. It basically creates an entry for each value thus making it faster to retrieve data.
DROP, DELETE and TRUNCATE statements
One of the top SQL interview questions is- Explain the difference between TRUNCATE and DELETE statements? Or what is TRUNCATE, DROP and DELETE statements?
DELETE is a Data Manipulation Language or DML command whereas TRUNCATE is a Data Definition Language or DDL command. DELETE statement is used to delete rows from a table whereas to delete all rows from the table and to make the space free, TRUNCATE command is used. The DROP command is used to remove an object from the database.
To understand this concept you should know different subsets of SQL. This is explained in various SQL tutorials that are available online.
Query and Subquery
A query is a request for information or data from a database table or combination of tables whereas subquery is a query within another query.
One should know the concept of query and subquery in SQL very well as it is one of the frequently asked SQL interview questions. For instance, from a simple question like- What is a query? Or what is a subquery? What are its types? To questions which may ask you to write an SQL query for a given data can be asked in an interview.
Hope you will find the above information useful.
A step-by-step approach to answering any question in a technical interview
As anyone job-hunting knows, the most stressful part of the whole process is almost certainly the dreaded job interview! If you are pursuing a career in analytics, then the interview process can present its own unique set of trials and tribulations. But as with anything in life, the best thing you can do is to be prepared.
This article will help you with preparation - we are going to explain what to expect from an analytics interview and how you can best prepare.
What Can I Expect From a Job Interview for a Career in Analytics?
For most careers in analytics, companies expect you to be able to code well or at least know the syntax well enough that it’s not a barrier for you day-to-day. Therefore, while these skills will generally be put to the test, it’s not the only skill interviewers will focus on. In addition to the technical portion (i.e., the coding portion), you will likely need to solve a “use case”, which is a problem that they have experienced, a hypothetical problem, or one they are actively trying to solve.
They are testing you not only for your solution to the problem but they expect you to walk them through how you got there.
Steps to Success
1. Focus on Methodology Not on the Code
It is important to note here that they aren’t just looking for your solution. They want to see your approach to the problem and that your technical foundation related to the subject matter is strong. Even with the wrong solution, they could be impressed if you walk them through how you got there.
You need to show them that you understand the methodology and the underlying assumptions that you need to make to reach the solution. Therefore, you need to walk them through the assumptions that you made, and why you made them. For example, what are you assuming about the population of users?
You also must think about and explain the math that underlies your methodology. Think about what could affect the metrics that you are working with in this situation, and communicate that you understand what would cause these changes.
If you can’t see it already, communication is the key variable that will run through all of this advice. In explaining your methodology, you need to show a full grasp of the situation. Explain what you assume about the problem, and what you assume it will take to reach a solution.
2. Be Detail Oriented On The Code But Only When Asked
In a job interview problem, you will often be presented with a piece of code, and be expected to analyze it or correct the mistakes which may solve the problem. This is where it is extremely important to show that you are detail oriented. Before this part, however, you’re most likely focusing on methodology and approach to the question, so refer to tip #1 above first.
You are expected to walk the interviewer through each part of this problem. Look at the syntax and explain to the interviewer what each block of code is achieving. From here, you will be able to come up with a “big picture” of what this code is achieving, and understand what could be added (or removed) to reach a proper solution.
Once you have properly explained the entirety of the code, as well as your approach to the solution, walk the interviewer through what you believe that solution could be.
As you can see, the solution was important, but how you got there was equally important. An interviewer will be much more willing to forgive mistakes if they can see your thought process and see that you are mostly on the right track, with a solid understanding of the methodology involved.
3. Think About Edge Cases
In coding, it is always important to understand the edge cases, and a job interview is no different.
Think about situations where you think the code could break, and communicate that to your interviewer. It is especially helpful if you can relate these edge cases to specific scenarios that they would actually encounter in their business. This is a great opportunity to show not only your coding knowledge, but your understanding of their business.
Then, once you have identified these potential edge cases, suggest ways that you could account for them so that the problems don’t occur. A solution is always easier to reach once you have identified the potential problems clearly. This is your chance to show your interviewer that you are always thinking about potential problem areas, and able to solve them as well.
4. Don’t Accept the Obvious!
In any problem that is presented in an interview, always remember to not accept the obvious answer! If it were obvious, it probably wouldn’t be given to you as a question in a job interview.
That’s why it’s so important to consider the advice above. Consider every detail presented, look for holes in the code, and consider real business edge cases. By communicating all of this, you will likely be able to identify where the problem lies, and from there you can build a solution.
Remember, this is a complex problem that needs solving, otherwise they wouldn’t be showing it to you. If you are struggling at first, just take your time and walk the interviewer through it, they want to see your thought process anyways.
We can’t tell you exactly what problem you will encounter in your job interview. But by considering all the advice above, you can develop a reliable strategy to solving any problem you may encounter.
If you are interviewing for an analytics position that involves coding, the coding aspect should be almost second-nature by that point. The interviewer is more interested in how you break down the problem, how you identify the areas that need work, and how you work toward a solution. They also want to see that you know their business, which means considering specific edge cases and relevant factors that might be relevant to the competitive environment in which they operate.
So there you have it, take your time and be thorough, but most importantly communicate your thought process the entire way. And if you want some extra practice on your coding, check out my article here on the best niche platforms to learn SQL and Python! Good luck!
SQL, python, R, or Tableau? With so many tools to choose from, which ones do I need to know?
When it comes to the world of analytics, you probably wouldn’t be surprised to learn that it can get quite complicated. One thing that is typical of most analytical jobs is that you will likely need to learn how to code, which generally requires learning a programming/scripting language.
Which Programming Language is Best?
If you are getting into analytics, and considering it as a career, it’s not long before you can become pretty overwhelmed with all the technical platforms and languages you might need to learn to start your career. Therefore, if you are considering a career in analytics, one of the first questions you will probably have is — what coding languages do I need to absolutely learn? And which languages are “nice to haves”.
In this article, we’ll give you a rundown of our recommendations for the top programming languages to learn for a career in analytics. These are the languages that recruiters most often look for, and your best bet if you are trying to break into the world of analytics whether it be data science or business analytics. Let’s get started by outlining my top picks.
SQL (a must know)
SQL is a scripting language that is used for accessing data within databases. Databases are powerful tools for storing large amounts of data, and SQL is what is used to access and pull out that data, to manipulate the data, or to clean it up and reorganize it.
Basically, data that is accessed by SQL is stored in a relational database. Each kind of data is stored in a table. A table has columns and rows to represent different properties about different things. With SQL, you can access these tables, find information that is relevant, compare information, or even manipulate it. Of course, all of these commands go deeper than the span of this article, but just know that this is an essential tool for many careers in analytics.
One other important consideration with SQL is that different companies use different types of databases. For example, you have HIVE, MySQL, postgres, and many others, all of which have different nuances to their syntax. The good news is that if you have a good grasp of SQL in general, you should have no problem adapting to the differences in these databases.
There are many great online SQL resources. For example, if you're looking for a guide to teach you SQL from scratch, I like Mode Analytics. If you already know SQL (even if you're just a beginner) and are looking for real-world practice problems, Strata Scratch, provides over 500+ SQL practice problems taken from real interviews from companies.
Python or R (a must know, if you're going into a career in data science)
Two very popular programming languages for data science and analytics jobs are Python and R. These are very adaptable languages, and as such can serve similar purposes, which may make it tough to decide between the two. Depending on which you are familiar with, both can be quite helpful, but it is important to be aware of the differences depending on which specific area of analytics you want to go into.
R is primarily used in research and has developed to be very useful for the purposes of statistics. As such, it is widely used by data scientists and statisticians for a variety of features related to statistics and data analysis. There is basically an option for almost any type of data analysis you want to do. R stores its data in a wide variety of ways (tables, matrices, vectors, etc.) which allow for objects such as regressions, coordinates, and more.
Python is more of an all-purpose programming language. It is a very large language and as such it has libraries to perform almost all the tasks that R can. Python is also a very powerful tool for machine learning and artificial intelligence, with libraries built specifically to perform these tasks.
I like to use python over R because of python’s great automation libraries and functions.Of course, all of this may sound very complicated to a beginner. So just know that if you are considering a career in data science or analytics, Python and R can both be extremely helpful. They are both open-source languages with large and growing communities supporting them.
Datacamp.com provides great resources for both R and python.
BI Tools like Tableau (a nice to have)
Business Intelligence tools (or BI Tools) are types of software that basically help you visualize your data. These platforms help you visualize and identify trends, to understand patterns, and develop implications based on those patterns. These tools essentially take the outputs from SQL and or python/R and adds an interactive graphics component to help you serve up insights to your stakeholders and business partners.
One of the most popular BI Tools is Tableau. Tableau helps you to understand key business data points and make insights based on that data. It can connect to almost any data sources, including Salesforce, Google Analytics, and SQL databases. It presents all its information in a handy interactive dashboard, which also allows you to control and generate new information and insights.
So there you have it, our top choices of coding languages to learn if you are considering getting into a career in analytics. Of course, analytics and data science are very broad fields. For this reason, before you go all-in on a certain programming language, consider more specifically which part of analytics and data science you are most interested. Do some research on the types of roles you really want to pursue, and then identify which of the programming languages above would be most valuable.
The languages above all have extremely powerful capabilities within data science and analytics. All would be quite valuable for a career in analytics. No matter which direction you choose, knowing any of these languages would certainly open a lot of doors.
Resources To Jump Start A Career In Analytics
As a prominent characteristic in the application of Information Technology, data science has managed to disrupt several industries in the virtual space as well as the real world. Though the improvements it has made for the virtual sector is vast, it is also rather apparent that it would hugely disrupt those industries.
Data Science companies are flourishing due to the demand for their services. However, all sectors require experts in the field. With the right training in data science applications and Python tutorials, anyone can exploit this demand to a certain extent.
As a branch of science that is traditionally speculative, Meteorology uses the existing data available to them to create reasonably accurate forecasts. This branch of science relies on vast amounts of data to be analyzed quickly and accurately, ranging from the readouts of instruments to the climate patterns of the past. The advent of modern equipment has allowed them to get more accurate readings of parameters such as wind speed, temperature and humidity, but the scientists find it hard to take all essential factors into account. This is a primary reason for inaccuracies that prevail in meteorology.
Data scientists can create programs powerful enough to gather and analyze all this data to create accurate simulations of the next probable weather. Even global occurrences like climate change can be accounted for while doing this, which makes it a revolutionary addition to the sector. Data science can disrupt meteorology to a large extent, creating both short term and long term charts that can produce accurate prediction models.
The medical care sector is a fundamental part of any working community. It is, therefore, necessary for this sector to keep up with the growing demands of the public.
The daily operation of a hospital relies on doctors making accurate diagnoses and prescribing the correct treatment. For this, precise patient data must be kept and updated regularly. Modern technology enables the staff to take numerous scans and test results to help them, but this data can be a headache to store and protect. Data Scientist can develop various means to store as well as transfer this data without much hassle.
Data Science also becomes crucial in medical research, where gigabytes of information about the patient or a drug becomes vital. For example, the Human Genome Project and various other studies in genetics rely on machines for collecting and sorting through the data. Data science powered by ML/DL/AI algorithms and python applications is crucial in this aspect, and without the advancements in that field, studying our genes would be a pipe dream.
The retail sector is growing fast as the consumers and their consumption increases. This field is a gold mine of revenue, for all goods and in all places. In this situation, keeping track of sales for various products are getting more troublesome.
With the help of data analytics, retailers can now keep tabs on their sales, calculate the turnover and profit, and even find out what sells more at a specific time of the year. Measures like this help the store-owner to maximise their profit and optimise sales tactics, improve marketing, and get customer feedback. This results in an improved quality of services and hence, more gain.
The share market is another sector that thrives on accurate predictions and speculations. It also forms the backbone of the economy for many developed as well as developing countries. The stock market also faces a tremendous influx of data, mainly as numbers and names of various trades that occur daily. The trend in the market also affects how future trades are made. In this scenario, there is a necessity to get the information and analyse it as fast as possible to make investments or jump ship as quickly as possible. Accuracy and speed are the vital elements required to make a successful trade.
Data Science in this scenario becomes crucial as the scientific discipline that can analyse the stock market. Unlike the chaotic storm that is the trading of the bygone era, the firms of today use data science to their advantage. By analyzing the vast amounts of data flowing in daily, as well as previous patterns and outcomes under similar situations, the scientist can give the possible projections which will help make trading more profitable.
Logistics involve transport and delivery of goods from location to location. In the past, the primary concern for logistics companies was the bulk transfer, import and export of various goods and products. However, with the rising popularity of online shopping, the distance between the consumer and the seller increases every day. This has rejuvenated Logistics and has taken them in another direction altogether.
Data Science allows companies to keep track of their deliveries and centralize the process of data collection. This is beyond just schedules and billing. The right framework and scientific expertise can allow them to work ahead of schedule, finding faster routes and optimising their delivery at every stage.
Targeted advertising is frequent nowadays. Each user gets advertisements based on the data collected from them, and the advertised goods range from items on offers to necessities. The data collected from users is analysed to draw up the list of possible things they will be interested in.
Data Science is used to analyse the data generated by the individual and give out the most probable suggestions that the target is likely to buy. A similar principle is also behind apps such as Spotify and YouTube, suggesting content to users.
Air transport can be quite a hassle under the wrong circumstances. With hundreds of planes taking off from and landing in airports, each plane carrying tens of lives as well as expensive cargo, one mishap could lead to disastrous consequences.
Data Science is used in air travel to chart courses as well as schedules. A large airport expects hundreds of domestic and international arrivals each day and about the same refuelling and taking off. A machine is capable of scheduling them without clashing and charting safe courses for all. It can also be used to calculate parameters such as flight time, speed, fuel consumption, and optimal path by avoiding turbulence, undesirable weather and oncoming aircraft.
As the branch of science that deals with data, Data Science is dominant at any and every field that involves using information. Be it problem-solving, optimization or predictive analysis; all sectors require the services that a data scientist provides.
Languages like python form the backbone of data science and is in great demand in the market. Grab the best python tutorials today and start your data science journey today.
Even science is not free from myths and legends. There are some beliefs that people tend to take to heart, and data science has a few of them. These beliefs can cause problems like stereotypes that give people the wrong impression or ideas that will discourage prospective ones from pursuing the field. Here are some of the biggest myths in the sector.
1. Data Science is all Science
Data science is not just science, but an art as well. The field of data science can go beyond numbers and tables and test your reasoning ability, aptitude and creative ability. It is not a science, strictly speaking, but a combination of scientific principles and a level of artistic thinking. Each problem is a unique conundrum which cannot be solved by assigning values to a variable and solving an equation. It is not a skill to be learned but a process.
2. Data Science requires a doctorate
This assumption is not entirely correct, but a partial truth. The job role and preferences decide the level of education one needs. However, one does need a firm grasp on statistics and mathematics and fundamental coding skills. The rest depends on the type of job.
In entry-level data science jobs, such as in applied data science and analytics, you do not need a PhD. Your work requires you to use packages and algorithms built in the workplace and apply the principles for clients. However, research jobs require a PhD as you will be creating your algorithm and writing a paper on it.
3. It’s all about the tools
It is another misconception about data science. It is a field that uses various tools and applications, with computers doing most of the heavy work as far as computation goes. However, just like every other job, a device is only as good as the person using it.
The focal point of your learning should be about the practical application of the tools. Many people who enter the trade end up learning the tools only, which cannot help them succeed in this path. It is always good to know the various platforms, software and packages used in data science, but not as important as knowing when and how to use them. Therefore, the learning should be work-oriented.
4. Coding is a must-know
Coding is a versatile weapon, and knowing a programming language will do wonders for your CV no matter which career you pursue. However, It is not something to rack your brains over. Due to the widespread use of programming, ready-made codes are available on the Internet, for almost every purpose.
This is not implying that knowing coding is useless. It is always handy to know to programme, and it can also help you progress faster in the field. But not being an expert programmer must not discourage you from pursuing data sciences.
5. Predictive Modelling is Data Science
Predictive Modelling, in simple terms, is the process of using data from various sources, analysing them and establishing a possible pattern or trend that can give us an accurate picture of the future. Although this picture is not always correct, most of these predictions help find the most probable outcome. This is used in many fields, and data scientists play a crucial part in building these predictive models.
However, to categorise all of data science as predictive modelling is a terrible misconception. Yes, this is one of the more popular applications of data science, from the weather to the stock market, but it is not the whole thing.
Data Science is also about many other things, starting from data mining and data cleaning, to visualisations, anomaly detections, and yes, predictive modelling. The miracle of data science is not mere divination, but all of what happens online today.
6. AI will take over Data Science
This belief comes in different forms, from AI doing the job of humans for them to AI assimilating data science as a part of it. None of them is correct.
AI systems are capable of using Big Data to their advantage. They can do number crunching better than human beings, and have shown promise in pattern recognition and provide suggestions, targeted advertising among performing other tasks. However, there are still human beings present in that network somewhere, especially for functions such as verifying the results, maintaining the program and many more.
7. Data Scientists are rare in the market
This is not as much a misconception as it is an outdated belief. Data scientists used to be a rarity and hence was in demand. However, that situation has changed. There are more data scientists now than there was, and these are not necessarily all freshmen straight out of college. Some are experienced in their respective fields and took up lessons in different aspects of data science. This may include mathematicians, analysts, programmers, among others. In any case, they are more common nowadays. So companies realise their potential.
For a realm of science that rose from the modern age of the internet, data science sure has a lot of myths and legends around it. Some of these myths are harmless, but others are not. Believing in some of these can destroy your confidence in pursuing your dreams. As an aspiring data scientist, having the right information is key to your survival.
So you’re looking to learn SQL and Python but you don’t know where to start. Or maybe you already know a little about these topics and you want to grow your skills. There are so many online resources out there claiming to be the best place to learn that it can be difficult to know who to trust.
You probably have heard about the big, online learning centers like Udemy, Codeacademy, and Khan Academy. These programs can be great for certain topics, but sometimes they are so large it can be difficult to know what they are best at.
The focus of this article will be on 6 smaller, more niche platforms to learn or hone your skills on SQL and Python. This article will give a small overview of each, as well as what makes them different and/or better.
The most important thing you can do to pick the best platform for you is to identify your needs. What is your skill level? What do you need these skills for? What concepts are you trying to learn? By identifying what skills and concepts you want to learn, you can effectively match your needs to one of the platforms in this article and pick the right one for you. So let’s get started:
6 Niche Platforms to Learn SQL and Python
Mode Analytics offers tutorials and courses for SQL and Python, and might be a great resource for you if you are a beginner looking to learn these skills. Mode offers separate training courses for each of these topics.
Each course is broken down in terms of more basic knowledge to more advanced knowledge. Beginners can start from scratch in each area, or can pick up in an area they are most comfortable. The courses are basic and straightforward, designed to teach you progressing skills related to analytics with SQL and Python. The courses are based around learning the syntax of each.
Mode is a valuable resource if you are looking for a basic, straightforward method to learn these concepts. However, it is somewhat limited in terms of offering users somewhere to grow and test their skills.
Their SQL courses are quite a bit more extensive, and do offer additional exercises after the course to grow skills. These exercises were based on interviews with Analytics managers, and meant to recreate some of the problems that they face regularly. However, the Python tutorial lacks these exercises, and instead points you to various external resources that you can use to test and grow your skills.
Overall, Mode offers a good, albeit basic place to learn and grow your skills, while lacking sufficient applications to test your skills. If you are just wanting a basic resource to learn syntax and concepts, then the simple layout might work great for you. However, if you are looking for something more extensive with more hands-on testing and problem-solving, you may want to keep reading through this list.
While the previous resource was quite lacking in methods and applications to test your skills, Strata Scratch is quite the opposite. They advertise themselves as providing the “building blocks” for growing your SQL and Python knowledge. So Strata Scratch is best suited for those who have a basic knowledge and are looking to grow it.
Strata Scratch provides over 500 SQL and Python practice questions to help improve your analytical skills in these areas. These questions were heavily researched and based on real industry problems, and many interview questions from various tech companies. This is built to be the ideal resource for prepping an interview or for advancing your career.
Strata Scratch also places great importance on explaining answers clearly. Each question provides in-depth video and article explanations of proper technical solutions, as well as clean approach and syntax. They focus on proper solutions, but also ensuring that the solutions are clean.
There are also beginner resources to teach you the basics of SQL and Python, along with the primary programs you need to be familiar with in these industries.
Overall, Strata Scratch is a great resource if your goal is to grow and test your skills. Often you will find that the best way to learn something is to have it continually tested. Strata Scratch offers many ways to test your knowledge in scenarios that are meant to mimic real-life scenarios and interview questions. If your aim is to grow your knowledge and advance your career, and you already have a basic knowledge of SQL and Python, then Strata Scratch is aimed toward you.
DataCamp is meant to be a streamlined way to learn skills from many topics, including SQL and Python. DataCamp structures their courses in a very simple way, from basic to most advanced. You can pick up a lesson from anywhere, depending on your skill level, and grow your understanding of various concepts.
DataCamp offers lessons in bite-sized lengths. This is intended so that you can pick up and learn a new topic in a short period of time, and perhaps do it while you are on-the-go or while you are busy with other things. It is intended to be a tool for growing your skills in your spare time, and investing as much time into it as you want.
The lessons are quite useful and numerous. We find that DataCamp offers more niche and focused subject areas than platforms like Mode. You will also have an easier time finding subjects that cover more specific use cases, or learning how to code for specific problems or workflows.
DataCamp also offers many ways to test and practice your skills. There are many mini practice challenges offered on each subject. Additionally, if you are looking for a more extensive way to test your skills, there are hands-on Data Projects offered. These are more extensive problems and projects which are based on real-world scenarios. However, we have found the sheer number of problems and projects to be limited in certain subject matters.
Overall, DataCamp is a streamlined, well-organized platform that is meant to teach you skills, and test them through various problems and projects. If you are looking for a platform that is organized, intuitive, and organizes each subject area into highly focused, bite-size lessons, then this could be the platform for you.
w3schools is a simple, no-frills tool for learning web development skills, including SQL and Python. Depending on your preferences, you will probably either love or hate w3school’s approach to learning. w3schools claims to be the world’s largest web developer site, so their methods clearly work for many people.
Essentially, the method of teaching here is simple and basic. The course is outlined from simplest to most advanced concepts, with each page providing a description of the concepts, with everything from syntax to important functions and keywords. So if you prefer to learn in a simple way, by having the topic outlined and explained clearly and succinctly, then you might really like this method of learning.
Additionally, w3schools makes heavy use of examples and interactivity throughout its lesson. Once a concept is explained, it will generally provide an immediate example of how it looks with proper syntax. Often, it even includes a customizable field where you can solve an example problem yourself.
Overall, w3schools is perhaps so popular because of its simple, straightforward approach to teaching SQL, Python, and many other programming languages. Clearly marked topics, combined with straightforward explanations, and a place to test your skills make up most of the lessons plans. However, if you are looking for a resource with more extensive exercises or real-world problems to solve, you probably are best to look elsewhere.
Perhaps most importantly to many people, the entire course is available free, without even so much as registration!
Hacker Rank is another valuable platform for teaching skills and testing knowledge as it pertains to SQL and Python. Their educational portion of their service is clearly geared toward people who already have at least a basic knowledge of SQL and Python, and are looking to hone their skills for an interview or for real-world applications.
Within Hacker Rank’s “Practice” portion of their service, there are offered many practice questions and problems to test your skills. You can choose from a variety of languages, including SQL and Python. The challenges are ranked according to Easy, Medium, and Hard, and the system tracks your progress as you go, with a “Leaderboard” for the highest ranking problem-solvers. You can also organize the challenges according to subject areas and subdomains, to narrow in on certain syntax issues or specific problems or workflows.
Overall, these are valuable tools if you are looking to test your skills in these areas and prepare for the problem solving you might see in a job interview or on the job. These are great if you are a hands-on learner and like to see how concepts are applied, rather than simply being explained how they work.
Additionally, there are discussion forums offered for you to chat, share, and help others with the problems and share insights into the interview process.
Guru99 is a free resource meant to teach a variety of skills, including sections on SQL and Python.
Guru99 offers lessons in SQL and Python, sorted from the basics to the more advanced concepts. Classes are either written or in video form.
Similar to w3schools, this is a simple, no-frills approach to teaching that attempts to lay out all the relevant concepts and explain them in a simple, straight-forward manner. One of the major advantages of Guru99 is that it covers a broad range of concepts and functions, and is useful to build your knowledge in many areas of SQL and Python.
Whether you are a beginner or are looking to learn more advanced concepts, the subject areas are clearly marked so you can choose a lesson that is in-line with your skill level.
While Guru99 is an easy, simple tool to build your knowledge, it doesn’t have much in the way of testing your knowledge. Courses are mainly non-interactive, with no areas to write your own code or solve sample problems. If you are looking to test your skills, you may want to supplement the concepts you learn here with another service such as Hacker Rank.
Overall, this might be a great program for you if you are looking for a well-organized resource where you can learn concepts at your own pace. And of course, it is free so you can try it out and see whether it is the resource for you.
There you have it, 6 lesser known resources for learning SQL and Python skills. As you can see, these resources vary pretty significantly in their goals and how they teach and test various subject matters. These resources focus on varying skill levels, and are meant to serve various purposes. Whether it be to prepare for an interview, or to build up basic skills, there is generally a primary focus for each online resource.
A key difference that will be most relevant to you is what kind of learning style each online resource focuses on. Many, such as w3schools and Guru99, simply focus on straightforward learning and explanation of concepts. However, others, such as Hacker Rank and Strata Scratch, aim to offer resources to build your understanding through practice and the solving of real world problems and scenarios.
Ultimately, the best resource for you depends on what you are looking to learn and also your learning style. Evaluate what you already know about these topics, and which resources offer the lessons that are most in line with your skill level. Also, consider how you like to learn. Are you more hands-on or do you like concepts to be explained to you in a simple way?
By understanding your needs and your goals, we are confident that you will find a great resource among the above listed to expand your knowledge in SQL and Python. As a bonus, a few of them are free and you can try them out risk-free. Learning new skills is a rewarding experience, and it is all the more satisfying if you find a learning resource that matches your specific preferences.
Technology is advancing at a breakneck pace especially when it comes to innovation. With each passing year, data science has been consistently increasing its impact across different sectors.
The impact of data revolution can be seen across the globe. It has not only given a steady rise in job opportunities but brought developments in artificial intelligence and machine learning. A specialist in these fields or those who are making their debut in it, have so many industries that are interested in such skills and want to integrate them into their workforce. Moreover, big companies are now leveraging data science to firmly automate their systems to deliver valuable customer experience.
Data science is profoundly changing the way business is conducted across the world. There are data science companies that have made their mark in the industry. Google, for instance, continues to take big steps in NLP (natural language processing) and sentiment analysis. In fact, it is being believed that NLP will shake up the complete service industry in a way that we have never imagined.
Similarly, Microsoft, a leader in the tech market is introducing new technologies and offers that will help companies navigate their digital transformation. Another top data science company, Salesforce focusses on the future of small business innovation. Its feature-loaded cloud-based CRM gives access to data from anywhere, anytime, and from various devices.
So to understand the power of data science applications, we have compiled a list of 7 revolutionary companies that are leveraging data science to enhance their processes and performance.
Personalized customer service is highly valuable in today’s world, as it means faster service, better all-round experience, and more relevant options. Consumer-metrics and big data including real-time information have made it possible to deliver better and targeted service options. Starbucks is at the forefront of it. The company uses its vast data stores and mobile app to display preferred orders to customers even before they visit the counter. As a result, significant improvement in performance, besides speeding up order and service time, especially during rush hours.
How data science is making it work for Starbucks?
Members of the Starbucks mobile app and rewards program often use it to order beverages and take advantage of exclusive benefits. One the other hand, the company uses this service to collect information about their customers’ habits and preferences. This is precisely how Starbucks offers preferred order information.
Besides that, the company also uses this data to build more relevant marketing promotions and campaigns, finalize a location for new stores and even decide future menu updates.
Amazon is amassing data not only on its wide product range but also on people buying those products. Since its inception, the company has been working diligently towards making itself a customer-centric platform. It hugely relies on predictive analytics to increase customer satisfaction.
How Amazon is transforming e-commerce with data science?
Amazon uses the personal recommendation system, which is a hybrid system that includes collaborative filtering which is comprehensive. The company uses data science applications to analyze the historical purchases of the user to recommend new or more products. This is also derived through suggestions that are taken from other customers who use similar products or give similar ratings. The company uses Big Data for envisaging the products that are most likely to be bought by its users. They also use data to optimize prices on its website while keeping in mind the user activity, product availability, prices offered by competitors, and order history.
Uber has been leveraging data science for its consistent success. The company makes an extensive use of Big Data since it has to maintain a huge database of drivers, consumers, and other records. It uses Big Data to derive insights to provide best services to its users.
How Uber is using data science to make rides better?
Uber uses Big Data along with crowdsourcing, i.e. registered drivers in that area can help anyone who wants to go somewhere. Since the company has the database of its drivers, so whenever a user books a cab, it matches your profile with the most suitable driver near your location. Moreover, the company charges the consumers not on the basis distance but the time taken to cover the distance. The time taken is calculated through various algorithms that include data related to traffic activity and weather conditions. In fact, Uber makes the best use of data science to calculate its product pricing, depending on the demand the rates are adjusted. Their pricing process is rooted in Big Data, thus making excellent use of data science to calculate fares based on various parameters.
Data science has played a pivotal role in the success of this international hospitality company that allows its users to host accommodations as well as find them through its website and mobile app. Airbnb contains enormous Big Data of host information and consumers, lodge records and homestays, besides the website traffic.
How Airbnb is using data science applications to make stays more comfortable?
The company uses data to provide better search results to its users. To analyze the bounce rate from its websites, Airbnb uses demographic analytics. A couple of years back when Airbnb discovered that users from some countries were clicking the neighborhood link, browse photos and page but don’t make any bookings. To alleviate this issue, the company replaced neighborhood links with top travel destinations and released a different version for the users from such countries which resulted in a 10% improvement in booking rate for those users.
Additionally, Airbnb uses knowledge graphs that helps them in matching user’s preferences with various parameters to provide best-suited localities and lodgings.
This online music streaming giant uses data science to provide personalized music recommendations based on its users’ browsing and listening history. Spotify contains a massive amount of Big Data and uses a large chunk of daily data generated to build its algorithms to enhance its user experience.
How Spotify is using data to revolutionize music streaming?
This data-driven company has been leveraging Big Data to offer personalized playlists to its consumers. The company has brought different analytical features for its artists through its Spotify for Artist Application. This data science application allows the artists to analyze their stream and the hits they are making through different Spotify playlists.
Spotify has used data science to get insights about which universities had a maximum percentage of party playlist. The findings were published on their page “Spotify Insights” to highlight the latest ongoing trends in music. The company also uses an API based product, Niland that uses machine learning to provide better recommendations and searches to the users. Furthermore, the company also analyzed the listening habits of users to predict the Grammy Awards Winners.
Mc Donald’s, the world-famous fast food joint has embraced modern technology in many ways. The company uses artificial intelligence and Big Data to boost its user experience.
How Mc Donald is making the user experience more enjoyable by using AI and Big Data?
Mc Donald’s mobile app allows users to order and pay through their mobile devices. Besides that, the users have access to exclusive deals. At the same time, the company collects data about its consumers like the food and service requested, how often they visit the drive-thru or visit the restaurant. Furthermore, the Data collected helps them to make more targeted offers and promotions.
Facebook with millions of users across the globe uses quantitative research through data science to get insights about the social interactions of its users. The company has become a hub of innovation as it has been using advanced applications of data science to understand user behavior and study insights to improve their product.
How Facebook is using data to revolutionize Social networking and advertising?
The company uses deep learning, which is an advanced branch of data science. With deep learning, the company makes use of text analysis and facial recognition. Facebook uses neural networks for facial recognition to enable the classification of faces in photographs. The company uses “Deep Text”, a text understanding engine of Facebook to understand user sentences. This engine is also used by the company to understand user interest and align photographs to the text.
Furthermore, the company uses deep learning for targeted advertising. The Big Data is used to gain insights about consumer preferences and advertisements are displayed according to users’ interest.
Data science has become widely rooted in several industries like banking, e-commerce, transport, healthcare and more. With data science companies providing tools for business analytics, machine learning, Big Data, and data management, organizations are embracing this technology to make better products. Those companies that have already used data science applications have seen an enormous growth pattern. Data science is a vast field and those who aspire to become data scientists can undertake data science tutorials that are easily available online.
The bottom line is that the industries need data to move ahead and therefore, data science has become an essential aspect of all the industries today.
SQL is the base of data analytics and Python is the base of data science. Stratascratch helps you attain mastery in both.
Data science is attracting a broad audience from a range of backgrounds because of its novelty, popularity, and all the perks involved. Unlike most fields, data science is not restricted to the holders of a particular degree. As long as one has the skills which the companies are looking for in a data scientist, one can make it big in the field. Here are the essential skills that can make you a desirable candidate for a data scientist job.
The Right Knowledge - An Absolute Must!
A data science degree is only one of the things that make you a Data Scientist. An aspiring professional needs a different set of learned skills to thrive in the field. These skills include coding, Mathematics, especially statistics, SQL, big data computation frameworks like Hadoop, and so on. Though these skills can be learned independently and separately, that does not imply that a data sciences degree is irrelevant or useless. People who have completed their higher education in data sciences or related fields such as mathematics have a critical advantage when it comes to the sector. Another vital element is your expertise over data structures as well as unstructured data, which are both significant elements in the work of a data scientist.
Big Data Computation Frameworks - Highly Necessary
These are frameworks that manage and analyse big data. Data scientists are required to know the ins and outs of big data frameworks, as it is a growing sector that offers employment to many each day. The most popular ones requested by companies are Hadoop and Apache Spark.
Hadoop is a popular computation platform that allows the user to handle large volumes of data, even beyond the capacity of the system being used. The platform is also used to convey this data to different points of the system.
Apache Spark performs functions similar to Hadoop but is faster. It uses the system memory cache to store computations, whereas Hadoop physically writes them on the hard drive. The platform is specially built for data science to enable the execution of complex algorithms faster and also to prevent loss of any data. You can use it on a cluster of machines. It also saves time by disseminating large data sets while working with them, making computation easier. It is also capable of handling unstructured data.
SQL - Basics Are Vital
Structured query language is used to handle data in a database. Even though it is used mostly in business applications, data scientists are also required to be able to execute complex codes in it. SQL is a tool that can make extracting and operating on data from databases easier; hence, it is indispensable. There are many resources available online that can teach you SQL, help you solve SQL problems and exercises to improve your proficiency level.
Mastery over Analytics tools
Unless the name did not make it clear enough, data science means the study of data. Data analytics play a significant role in this study, and therefore, all aspirants should have mastered the standard tools of data analytics. The most popular one is R, and a large portion of data scientists prefers it. However, R has a steep learning curve, which means the more progress you make, the harder it gets. It also means that it will be challenging to learn even if you have learned computer programming up to a certain level.
Coding in Python
Python is a computer language with a growing fan-base in all sectors, and data science is no different. It is easy to use, convenient, flexible and runs on all platforms. Python has many salient features that make it the go-to language for coding. In data science, the part of it which attract programmers is the presence of several libraries, which are pre-existing and free to use functions. Many commonly used tasks and roles are present as libraries, which makes it convenient for coders. Learning Python online, practising python exercises and python mini projects are the best way to improve your coding skills.
AI and ML - The Hotspots
As sectors disrupting everything around them, it is no surprise that AI and machine learning made this list. It might not be as crucial an addition to this list, but knowing it is guaranteed to make one stand out from the rest. AI is capable of data analytics better than humans, and most data scientists are not experts in the areas of machine learning, neural networks, and artificial intelligence techniques. Therefore, knowing this puts you in an advantageous position.
Data Visualisation Is Essential
In business applications, data visualisation is essential due to one primary reason: Not everyone can make sense out of numbers. charts, graphs and plots have been an unavoidable part of presentations since the beginning of businesses. To be able to use the information obtained, it is essential to visualise the data first. Therefore, data visualisation is a valuable skill in the arsenal of a data scientist. One must know how to use visualisation tools such as Matplotlib(Python Library), Tableau, among others.
Non Technical Skills - Cannot Be Ignored
Every job has its requirement of technical proficiency. However, every position in the world has a list of non-technical specifications that affect your value as an employable person. These factors include language and communication skills, business acumen, team spirit and a passion for the job. These factors determine your chance of success in the profession you choose.
The field of data science is full of promise. With the right set of skills and the right spirit, anyone can be successful. What matters is how much you want the job, and how far you are willing to push yourself for it.
All the best!
Job hunting can be a challenging task for many people, yet we all need to go through that process in order to build a career. A large proportion of the most desirable jobs on the job market right now are jobs related to analytics, like data scientists, data engineers, or even a data analyst.
As these jobs like being a data scientist become more and more desirable, they can become more and more competitive. Competitive job markets mean that the most skilled people are often the most employable. Employers are looking for data scientists that can tackle any problems thrown at them. So how does one actually get a job as a data scientist?
When it comes to getting a job as a data scientist, many people do not know where to start. The path to building a great career as a data scientist does not need to be complicated. Here are 7 actionable tips on how to get a job as a data scientist.
1. Know The Most Important Skills
Data scientists are a blend of a programmer, statistician, software engineer, and many more rolled into one. A data scientist needs to be able to run a project from start to finish. As such, a person who wants to get a job as a data scientist needs to have a versatile skill set in order to do the job competently.
Having a strong skill set is something that employers can put to good use. Knowing the most important skills within data science and analytics is the first thing any prospective data scientist should have down. Some of the most important skills for becoming a data scientist are:
Knowing these skills and being able to use them effectively are core components of getting a job as a data scientist. If you do not feel competent quite yet in your ability to competently use any of the above skills, then try focusing on upskilling, which is also our next tip.
2. Keep Learning
In data science, you have to stay on top of skills development in order to stay ahead in your field. The field of data science and analytics is always adapting and the problems change each time. As a result, upskilling and honing your skillset is essential to building a career as a data scientist.
Building real industry knowledge through practice in educational resources can do a long way. Having a strong technical foundation in analytics is something that can be built in the comfort of your own home.
Make Use Of Educational Resources
Knowing the basics is not going to cut it if you want to get a job as a data scientist. In-depth knowledge and problem-solving skills are needed to succeed in the analytics field. Making use of educational resources like online exercises, boot camps, and modules can go a long way in mastering analytics skills.
Trying out exercises, reading case studies, and doing tutorials like those from Strata Scratch can go a long way in keeping you on top of your game. Continuous learning is necessary to stay abreast of the analytics field, so take time to keep learning if you want to get a job as a data scientist.
3. Build Up Your Communication Skills
Getting a job as a data scientist is not only about having a strong analytical toolset, soft skills like communication are crucial too. Be able to describe how you would solve a problem and why you chose that route to a solution is a critical part of being a data scientist.
Data scientists need to be able to communicate each step of a project and the reasoning behind it. Try making notes of your thoughts as you solve a problem or tackle a project so that you can learn to explain each step to others in the future.
4. Practice Makes Perfect
Landing the perfect job when building a career as a data scientist takes time. Behind every successful data scientists is a large number of job applications and several interviews. The fact of the matter is that getting a job as a data scientist takes time and effort.
Stay positive, learn from the positions you did not land, and learn from the interviews that did not go as smoothly as you had hoped. Eventually, practise will make perfect and you will land a job as a data scientist.
Getting a job as a data scientist is not only about having the strongest skill set, it is also about meeting people within the industry who may help guide you to a great job. Making use of social networking sites like LinkedIn and attending industry meetups can go a long way in landing you your dream position as a data scientist.
6. Build A Portfolio
Employers come across hundreds or even thousands of CVs claiming that that candidate is capable of doing what they need, but not every job candidate has much to show for it. It is well and good to say that you are able to do something, but showing that you can do it and more will go a lot further.
Building a portfolio of past work and project shows an employer that you are competent and capable and have something to show for it. Standing out with a diverse and interesting portfolio can be the defining part of your application that will get you a job as a data scientist.
Bonus: all of the learning you’ve done on the side can go a long way in building up a portfolio. Save your best solutions to problems and exercises done on Strata Scratch to beef up your portfolio.
7. Find A Mentor
On top of regular networking, one of the best tips you can follow for getting a job as a data scientist is to find a mentor. A mentor can guide you through projects and educational resources and can even help you figure out exactly what employers are looking for in a data scientist candidate.
Artificial Intelligence is a booming sector of Information Technology. AI has gained a lot of popularity over the years and now finds itself a part of various sectors even outside the IT industry. Smartphones have also helped it insert itself into the daily lives of most people.
Python is a popular language that is preferred by computer scientists and data scientists worldwide. The language offers many advantages that make it ideal for tasks involving large amounts of codes and is also very easy to master as well as reliable in execution.
Advantages of Python - At A Glance
Python is simpler than most languages and requires less code to be written for executing any program or building platforms. As compared to more popular languages like C and, using python saves a significant amount of code, and directly translates to less time spent in building and execution. Therefore, even though python is slightly slower than languages like C, the fact that it is easier and takes less time to build more than makes up for it.
Python is also one of the most flexible languages out there. It works across a variety of platforms and can adapt to be suitable for various situations and tasks. Python is platform-independent, which makes it convenient to work with across all platforms and add to mobility as well. Parts of the same program can be coded and executed across different platforms and operating systems, ensuring their suitability for all users. It also allows the programmer to choose between object oriented programming as well as scripting, which saves a lot of time and effort, and IDE is available to check for most programs, which helps coders a lot.
Diverse List Of Python Libraries
This is perhaps the most attractive feature that Python has to offer. It has a plethora of inbuilt libraries which are a boon for programmers struggling with lines and lines of code. These libraries perform many functions that are useful across all fields in IT, such as data sciences, app building and web development.
There are libraries fit for every task, and Artificial Intelligence is no exception. It even has a specific library for Machine Learning functions, called Pybrain. Other popular libraries include Numpy for scientific computation and Scipy for advanced computation.
The Vibrant Community
This is a general advantage that Python has due to its popularity. Python enjoys a lot of support online, and most of this support is from online communities of programmers and computer scientists. Python has a large fanbase of helpful individuals who are all eager to learn and are helpful enough to support to clear doubts, or even fix codes. This vibrant community is the heart and soul of python and helps bring more people into it.
Python for AI - A perfect Combo!
The role of python as the go-to language for AI/ML applications have been growing for years. Python is a strong language overall and offers unparalleled ease and flexibility as a programming language. Though it is not perfect, it’s advantages have attracted many engineers to use python for the following applications.
Python Libraries and AI
The large number of open source libraries that python boasts of is a key player in many AI programs using python. There are many examples of this, and the most notable ones are as follows.
NumPy is perhaps the single most important and useful library in python and finds its application in almost all Python programs. It can be a storage space for generic data and help call some very frequently used functions. It is the most versatile tool for scientific computation, it has N-Dimensional arrays, fourier transforms, random number capabilities and many other functions.
Other equally useful libraries are pandas, which provide easy to use data structures and analytic tools, and matplotlib which, as the name would imply, assist in creating graphs and mathematical plots of high quality via a Graphical User Interface.
Certain python libraries used exclusively for AI applications are AIMA, pyDatalog and SimpleAI among others. This shows that Python is well equipped to handle tasks and create AI solutions. Any AI expert can vouch for the benefit of having a large number of pre-existing libraries at their disposal.
Python in Practice - Diverse Practical Applications
Python is used in building many popular websites and applications such as Instagram, Facebook, YouTube and Gmail. The versatility of the language makes it easier to use it to cover multiple aspects in the same code. Since python is a glue language, it also makes it easier to add parts of code from other languages to it.
Most websites today which is built using Python uses some form of Machine Learning or the other. Whether it is suggesting new content or targeted advertising, they all involve steps of data analytics. The same tool that is used to perform this function can also be used for building and integrating AI into a platform.
TensorFlow, designed by the members on the Google Brain team, is a system that facilitates ML in Research, to help make a smooth transition from research prototype to production. Scikit-learn contains simple, open-source tools for data mining and analytics. Theano is essentially a calculator that can help the user define, solve and optimize complex mathematical expressions using multidimensional arrays. All these make use of Python as the foundation, the language used to build them. More examples can be found here.
The Verdict - Python Amplifies The Power of AI
Python has disrupted almost all fields of Information Technology, and has proved to be capable of keeping up with the times, no matter what new applications arise for it. This refreshingly simple, yet powerful language shows promise and infinite capabilities, which are still being explored. AI is yet another crown jewel for python. Proficiency in the language is a must have for all aspirants in the field.
Python has been growing as a popular choice among Data Scientists. The language itself has merits that make it a great choice for all kinds of programming as well as analytic applications. As far as Data science goes, Python shines in that arena as well.
Python in Data Sciences - A Wide Array Of Application
Python in itself is a very flexible and user-friendly language. It can be adapted for any function and is also a glue language, which means lines of codes written in other languages can directly be added into the Python code. There are certain traits of Python that can be exploited in the Data Science field, which is the reason behind it being such a necessity for every data scientist.
For starters, Python is much simpler than conventional languages which makes it easy to study, write as well as troubleshooting. Coding and compiling in Python is an easy process, and the simplicity makes it easier for programmers to find and fix errors. This has helped its growth and now enjoys support from a large community of Data Scientists, which makes working with it easier.
Python also has a large number of libraries for Data Science applications, making it easier to produce results with it. These libraries are bundles of pre-existing functions that can be directly imported into your project to save time. Python has an ever-evolving collection of packages makes work less tedious and more productive. This is what makes the language a blessing.
Learning Python: For Data Scientists
Python is a flexible language with infinite capabilities, which makes it almost impossible to learn fully. However, this also means that as a Data Scientist, there are parts of Python that you need not concern yourself with, and you can selectively learn the part of the language which suits your work and the current project. Learning Python as a whole is a good thing, but to save time, its easier to start with the general concepts and learn as you go.
Start with the Basics - An Absolute Must
First things first; Learning to code is the basic part of learning any language. And coding in Python is quite easy due to how simplistic the language is. The short and crisp syntax makes it looks less of a test of typing and is quite refreshing as compared to other languages. You can start with basic commands and functions, move on to fundamental concepts like loops and reach reasonably high levels quite fast. Some applications and websites teach Python to users by having them practice python exercises of increasing complexity, helping them learn as they go.
Mini Python Projects For The Much Needed Experience
As it is with everything else, Practice makes you perfect. For Python, reading the code and learning the commands aren’t the key to learning it, but the practice is. Python mini-projects can help to understand how the language works by using it to solve problems and perform operations. The more you practice, the more you improve.
You can also improve by looking at codes written by other programmers, solving Python Exercises online and in the same ways as you learn any other subject.
Well-equipped Python Library
This is the most important part of Python that attracts coders to it. The language itself is growing in popularity due to its large and well-equipped Library. It has additions that you can directly attach to your code, which contains functions that would otherwise require you to manually add to the program. This makes it easier to execute them and obtain results faster.
Python Library is a package that has tools for every application, so not every addition is meant for every program. It is vital to recognise the ones that will serve your purpose, and hence a waste of time to learn about the ones that do not v=concern you. Therefore, learning about Python Libraries should be more of a continuous process, where you come across new and useful ones as you progress and start working. However, there are important libraries that you can learn about right off the bat, like NumPy and Pandas for Data Manipulation and Matplotlib for Data Visualisation and Plotting.
It is easier to learn Python nowadays due to the vast availability of experts and resources online. Forums like Quora and StackOverflow can help you interact with others, clear your doubts and learn easier.
Advanced Data Science in Python
As you progress, you will be able to do Advance Data Science applications like Regression models and k-means clustering on Python, which will be symbolic of how far you’ve progressed. Your ability as a Data Scientist will increase with your proficiency in Python, and you will even be able to start with higher tier aspects like Machine Learning.
Python as a Subject - Go For It
Learning a computer programming language is like any other subject, as it takes time and effort. It all depends on your skill, proficiency and dedication. However, once mastered, Python can be a strong point on your CV and an indispensable tool in your arsenal. It’s growing popularity is a sign of it becoming a no brainer as a programming language, and its simplicity, flexibility and large library contributes to it.
The business sector is a field which embraces technology. The sector is constantly in flux because one needs to make the most of novel opportunities to finish first. In such a competitive sector, certain skills are high in demand due to their wide range of applications, and SQL is one of them. And out of the various SQL Developers available, Oracle offers a powerful yet versatile platform suitable for SQL programming.
Structured Query Language - A Widely Accepted Programming Language!
SQL is called so because it uses queries as part of its code. Queries are commands used to manipulate data. Another salient feature of SQL is that it is a structured language, and all data is assumed in a tabular format. These features make it the perfect tool for business purposes, and it has found rising popularity in the same.
SQL Developer is the platform on which you can code, compile and execute various programs in SQL.
Oracle SQL Developer - A Robust Platform
Oracle SQL Developer is an IDE for programming in SQL on Oracle Databases. It is made available by the Oracle Corporation for free and is one of the most popular Relational Database Management Systems today.
Even though all such platforms use SQL, many differences make some better than the other. SQL itself comes in different types, so these programs are diverse in their capabilities. This is because different platforms use different forms of SQL and follow different protocols and mechanisms.
Oracle and PL/SQL - A Powerful Integration
Oracle SQL Developer uses a version of SQL called Procedural Language or PL/SQL, whereas most others, especially Microsoft SQL Server uses T-SQL. This itself gives Oracle an edge over its counterparts, as PL/SQL has many advantages over other formats.
For starters, PL/SQL is different from T-SQL in its syntax as well as capabilities, since they both handle variables, stored procedures and built-in functions differently. PL/SQL can also create packages of grouped procedures, unlike T-SQL.
This also makes it easier to convert applications to a different database without posing many challenges in editing or reworking the code. PL/SQL also has much more DBMS system packages than T-SQL and is better at error exception handling.
Organizing Database objects in Oracle - Highly Structured
Another major feature of Oracle which makes it more desirable is the way Database objects are organized in Oracle. Oracle has a subgroup of collection of database objects under a schema. There are many such Schemas, and they are all shared with the users. The sharing is universal but can be regulated through permissions.
Oracle works across all platforms and operating systems. This makes it a viable option for enterprises running on custom operating systems or freeware.
Transactions are a group of tasks that must be treated as a single unit. These are executed differently in different platforms, and Oracle is more in control of its transactions. Oracle treats each new database connection as a new transaction. As each query is executed, the changes made are only in memory until an explicit statement is given. Upon issuing the COMMIT statement explicitly, the changes are permanently made. This offers great flexibility as you can easily roll back changes and correct errors.
Oracle also has a wider variety of options to choose from in DBMS packages. Other options like Microsoft SQL Server does not contain provisions to declare some object types like public and private synonyms, independent sequence objects and so on. Therefore, Oracle is a more comprehensive option as it covers all the bases.
A cluster of servers refers to a connected group of physically separate servers that act in harmony and are perceived as a single system by networks. This helps in up-scaling by increasing computing power. Oracle can take advantage of Clustered systems, unlike their peers. With the new parallel servers in Oracle, you can place any application on a cluster without affecting the application, and it can be up-scaled by adding another server. This puts it miles ahead of its competing platforms.
Working with computers on anything can be quite a hassle. Even in simple cases such as writing this article, one mistake can cause you to lose all your progress. Therefore, applications have to be reliable in all aspects.
Oracle has many features that ensure a smooth work-flow and contains safeguards against any unexpected issues. It allows you to mirror transaction log files, which show exactly what programs were executed and when. It also prevents crashes occurring as a result of less space on the hard disk, and saves the server from downtimes and rebooting. This makes Oracle a safe option when compared to other DBMS platforms.
Oracle: A Powerhouse
The above-mentioned features show exactly why Oracle is a more desirable option over its competitors. As a freeware, it is readily available. It also has a wider assortment of DBMS packages and options to choose from and is much more flexible. SQL Programming in Oracle works with every platform and OS. It is also more reliable and versatile as a platform, and the Clustering feature alone puts it at the top of the table.
Python is now regarded as a must-have skill for most of the data analytics and data science job roles. And even though its seeming popularity is not the primary reason for it, it seems to be a major contributing factor. Due to its popularity, Python exercises, Python programming proficiency, and Python interview questions form an important aspect of getting a job in the data analytics sector. Almost every recruiter looks for Python as a necessary skill, instead of just one that gives the recruit brownie points. And this is because of how widely accepted it is as a programming language.
Python-based websites and apps are the frontrunners of their sector, and this is a list of some of those.
The tech giant Google which has evolved into a household name even has a saying about it, “Python where we can, C++ where we must.” Quite a bit of Google infrastructure is built using Python, especially YouTube. The largest video sharing platform in the world uses Python for almost everything, most notably their targeted advertisements and suggestions.
Instagram is another popular site that is almost entirely built on Python. The social media platform revolutionized the sharing of pictures on such levels that Google was practically throwing money at it for acquiring it. This all started as a simple website with a Django backend running on just one server. Django is an open-source developer used by Instagram to this day, and it runs on, you guessed it, Python.
The front page of the internet is a massive online society that, if you know, you know. For the uninitiated, Reddit is a place where you can find a community (called a subreddit) or everything. If you don’t know about this site, chances are you’re living under a virtual pile of rocks. This website is also reliant on Python, and cannot survive without its simplicity, and endless libraries.
For those of who are done with seeing examples of social media, here is a breath of fresh air. IBM, which has been and still is a big name in the IT industry uses python for many things, most notably using a Python SDK for IBM’s big data and AI service called Watson, and a free Python tutorial that they have released. And when a company that has been a huge contributor to the tech environment uses Python, that’s a tell-tale sign of Python being capable.
Spotify is the music streaming service that has revolutionized the Music Industry as we know it. Millions of users trust the platform to not only listen to their favorite songs but also put them on new ones with their incredibly personalized and accurate suggestion feature. Spotify uses Python for many backend functions and Analytics, which means that Python is responsible for the suggestion algorithms Spotify is popular for. Netflix, the global Media streaming service also follows in Spotify’s footsteps by using Python for a similar purpose. When you see these two giants using the same language for their exceedingly well-reputed work, that is a testament to the relevance and capability of the language.
Dropbox is a popular online data storage service that makes use of cloud computing to safely store your data. It is one of the most widely used platforms for this purpose across all operating systems, both personal and enterprise-related and has a total value of over $8 billion. Dropbox also uses Python for various purposes, most notably its well-sculpted Desktop version.
Uber has disrupted Taxi services and has brought this part of the transportation sector into the Cyberspace. They use Python as their go-to programming language, helping them with their Analytics and algorithms.
Python is widely used for a plethora of applications in various sectors, from social media to various services. This is even though it is not the fastest computer language out there. Even though computers are preferred for their speed, Python, despite being slower, is used more than faster languages such as C. And the reason behind this trend, simply put, can be summed up as follows.
Python is easy, reliable, and manageable. The script itself is easier to understand, and this makes coding and maintenance of the program easier. And even though it is slower, Python still gets the job done. Since most of these programs are run on a huge scale, the difference of milliseconds or seconds in speed does not matter.
Also, being a glue language makes Python more flexible and easier to write. This means that you can write part of the program in one language, and simply attach that part to the Python language. This is an important feature, as many other languages possess features better suited for certain applications, and they can all be executed in Python.
Taking Advantage Of The Python-philia
When a language is in such a coveted position, learning it can give you an edge over other candidates that do not possess the level of skill as you. Practicing Python exercises online and solving Python Practice sets gives you a grasp over the language, that can put you ahead. Python is overwhelmingly popular, which means knowing it makes you a favorite with the recruiters. In addition to the bigwigs in the Cyberspace, almost all websites and apps are initially written in Python. The ease of handling and its flexibility as a glue language has turned it into a Universal language for Coding.
To take your data analytics career to the next level, visit https://www.stratascratch.com/.
SQL is a prerequisite skill for all those who are aiming for positions in the Data analytics domain. And like any other core topic, the SQL knowledge of a candidate is tested via written examinations, coding tests as well as interviews. Tackling them requires mastery over the subject, but SQL interview questions pose a different kind of threat than conventional SQL exercises or problems.
Interviews, in general, are more stringent than conventional exams because of the nature of the process. Interviews are one on one interactions between the candidate and the examiner, and hence are harder to crack. More often than not, the person may be facing an interviewing board where you never know what's coming next. The level of stress in these situations is much higher and harder to handle. This is why many fail in cracking interviews.
SQL Interview Questions
SQL interviews come under the technical interview category and hence are purely based on the job and the expectations. Markers like personality and spirit take a back seat, and the interviewers judge the technical ability of the candidate first and foremost. Therefore, SQL interview questions are designed to test your knowledge on the subject, and also your ability to correlate the principles with field work, checking your practical application skills.
Types Of Questions
Since it is impossible to find what the interviewers might ask, it is easier to try and figure out what they look for in an employee. The apparent answers are knowledge of the subject and programming skills. So, SQL interview questions are set to measure those markers.
The “Theory” Questions
These are questions that can be considered as bookish knowledge, as these questions are generally about theory, definitions, classifications, and so on. Knowing this might not have a lot of practical value, but it shows that you have sufficiently broad knowledge about the subject as a whole.
Questions such as definitions of standard terms, terminology, the concepts can be considered as this type.
The “Problem” Questions
These are a little more complicated, as they are not as straightforward as the theory questions. These questions test the true mettle of a programmer and are based on realistic situations. Answering these types of SQL interview questions correctly is crucial, as these will set you apart from the rest. They not only reveal your knowledge, but also showcase your awareness about using it in a professional capacity.
Preparing for SQL Interview questions
SQL is just like any other subject, so preparing for SQL interview questions has the same necessary steps as any other subject. One must have a firm grasp of the basics of the subject and the expected questions. Awareness about the latest developments and current trends are also necessary to nail the interview. Each question is designed to test you in different aspects. Therefore every answer counts.
Here are some of the common questions asked in SQL interviews.
More questions can be found in online resources.
SQL interviews can seem daunting at first, and prove to be impossible to conquer if unprepared. The subject has grown to be so important that it is now deemed as a must have for all Data analysts and scientists. Therefore, mastering it is of paramount importance for all aspirants. SQL interviews can easily be nailed with ample preparation and confidence.
All the best!
To take your data analytics career to the next level, visit https://www.stratascratch.com/.
Structured Query Language or SQL is the backbone of data analytics and data science. SQL assumes data in the form of tables similar to spreadsheets. The language is based on relational algebra, which allows it to sort, filter, and recall data. The language has also undergone many modifications to add new functions and capabilities. This has caused SQL to evolve and branch into many different versions, each of them distinct but all based on the same relational mathematics that forms the skeleton of SQL.
SQL in Data Analytics
SQL has assumed a position of industry standard within the field of Data Analytics. This is because the data structure considered within the program is a spreadsheet format, which has the most amount of applications in businesses. This is also because of the salient features of SQL, which make it easier to use for all.
This has also resulted in SQL being an unavoidable skill for data analysts and developers. Basic SQL knowledge is now tested during the recruitment process of these jobs. SQL and Python problem solving, SQL Interview questions and SQL Exercises are given to aspirants in this sector to measure their proficiency in the language.
SQL Proficiency Testing
As SQL has become an essential subject in the industry, the SQL proficiency of possible recruits is also being put to the test. All companies concerned with data science hire people who are well versed in SQL, among other things.
As it is with coding, tests are conducted by companies to measure the proficiency of possible recruits, and only those who do well are selected for the jobs. SQL exercises, interview questions and SQL problem-solving are the main methods used by these companies to choose the cream of the crop.
Practice results in perfection is a tried and tested principle. Since their early schooling of elementary mathematics, students have been encouraged to practice using the theoretical knowledge that they gain, not only to excel in the subject but also to improve problem-solving and analytical skills.
SQL exercises fulfil the same purpose. The activities are the same as any other exercises for any other subjects. They are available on various online platforms and come in a variety of difficulty levels. There are many benefits to practising SQL Exercises online and solving them.
SQL exercises are designed with two main goals in mind.
Correlating With Theory
SQL exercise questions and answers have to be related to all the main theory parts of the programming language. A good SQL exercise set will have a variety of problems touching upon every part of the language and in various difficulty level. Practising with them as you study helps the student to master the practical applications of the language and hence stay up to date.
Practical Application Of SQL Programming
SQL tutorials will also have problems that mimic the real-life usage of SQL in various sectors. As the course in itself aims at enabling the learner to master SQL at a professional capacity, these problems are necessary. SQL problems and answers will be similar to what a Data scientist will have to encounter in the line of their work.
SQL Exercises Online: Difficulty Levels
As it is with all exercises, this subject also comes with a variety of questions in different tiers of difficulty. Depending on the knowledge and skill of the student, they can choose from varying levels of difficulty of the items, that check the test taker's knowledge on any one specific topic or in the broad sense. This range of difficulty allows the user to not only measure their progress but also work to attain the next tier of their skills.
SQL Exercises Online: The Benefits
It makes sense for SQL problem sets to be available online, as SQL is first and foremost a programming language. Even if a person learns all the theory in the world about SQL, it is still useless without solving SQL problem sets. With that being said, these exercises help the student to improve their skill set in a way that can't be satisfied with conventional schooling.
The obvious reason for this is that SQL is a computing language. The fundamental process is coding, the raw material is digitally structured data, and the application lies in Data Analytics. Therefore, it is only logical for SQL exercises to be done on a digital platform.
Another reason why one should opt for SQL exercises online is due to the nature of the subject. As an essential tool in the digital world, SQL is fluid and ever-evolving. The language itself is changing, and the quality of tasks are becoming more complex. The volume of data handled is increasing, and the queries are becoming more and more complex. The ecosystem is in a constant state of flux, and any professional worth their salt has to keep up with these changes. Therefore, using online resources that provide SQL problem sets make more sense.
SQL is growing in significance and is now an irreplaceable tool for every aspirant in the field. The scope of this subject keeps expanding, and one must be able to keep up with it to reach the top. SQL exercises are available online to enable the technicians to be better at the trade. Practice makes perfect.
To take your data analytics career to the next level, visit https://www.stratascratch.com/.
This blog post follows our “SQL Interview Questions From Real Companies” video which can be found at https://www.youtube.com/watch?v=n6gM265zG68.
In this post we’ll go through 4 SQL questions you’re bound to encounter during a technical interview. While these problems are on the easy side, it’s still important that you bring along the interviewer. You want to show your interviewer your thought process. It’s okay if you don’t have enough time to solve the problem. Interviewers care more about how you solve problems in general than whether you can solve this specific problem. So during an interview, remember to take your time and describe each step to your interviewer.
We’ll use a three-step approach to problem-solving that you can use during your technical interviews. First, remember to build up a query step by step and explain each step to the interviewer. Your interviewer wants to see that you know what you’re doing and why you’re doing it. Second, you should be looking for edge cases throughout an interview. By asking your interviewer questions about edge cases you’ll show the interview your attention to detail. Finally, you should be able to explain to the interviewer the effect of every clause and expression.
We’ll be using Strata Scratch for our SQL exercises. Strata Scratch is a platform that helps you prepare for technical interviews. Every problem in this post is available to you on Strata Scratch.
Question 1: Find the drafts that contain the word optimism.
For our first interview question, we’re given a table called google_file_store and asked to find all the draft files containing the word optimism.
We’ll start every problem by looking at the table. I can pull keywords out of the question and use them to understand the table. Looking for ‘draft’, I see a few file names start with ‘draft’. All draft files must follow the format of the word ‘draft’ followed by a number. I also see that some of the contents contain the word ‘optimism’. Now is a good chance to ask the interviewer some questions. It’s always important that you ask the interviewer questions because helps you solve the problem and it helps the interviewer understand your thought process.
A good question would be, where in the content is the word ‘optimism’ located? Is it in the beginning, middle, or end of the string? It might seem obvious from looking at the table that the position of optimism doesn’t matter for this question, but asking questions can still benefit you. Currently, we’re making assumptions about the problem, and by asking questions we guarantee we’re solving the problem correctly and showing the interviewer our attention to detail. For this problem, the position and the case of ‘optimism’ do not matter.
Let’s start writing our query. Every interview problem starts with writing out the basic query. For these problems, we’re using the SELECT * statement. The SELECT * statement is used whenever you want to return all columns of information from a table. The next clause in the basic query is the FROM clause. This clause is used to choose which table we’re getting information from. We’ll add ‘FROM sql_interviews.google_file_store’. As expected, when you run this query, it returns all the information from the table.
To solve this problem, we need a way to filter the results. We do that using a WHERE clause. Understanding the WHERE clause is critical when going into a technical interview. Almost every question will involve understanding the context of the problem, and describing that context as a WHERE clause. The WHERE clause works by taking an expression, which is something that returns true or false. Each row is evaluated using the expression; if it’s true the row is returned, and it’s false it gets filtered out. We’ll need to write an expression that matches the two conditions of our problem so that we can filter out what we need. Each file has to be a draft and the contents must contain the word optimism.
Let’s deal with the first condition first. We need an expression that can do simple pattern matching. In this case, the ILIKE expression is perfect. This expression takes the name of a column and a pattern string, and only returns rows which match the pattern. Pattern strings have two special characters. The % character represents 0 or more of any character, and the _ character which represents exactly 1 of any character. Using those 2 characters we can use pattern matching to match many strings. We need to write a pattern that can match any string starting with ‘draft’. The pattern is ‘draft’ followed by 0 or more of any character so we’ll use ‘draft%’. This pattern string will match any string starting with 'draft'.
Now that we have a pattern string we can write our expression. After our WHERE clause, we’ll as a tab, and write “filename ILIKE ‘draft%’”. Now when we run the query it only returns drafts.
Now we can deal with the second condition. The second condition is that the contents contain the word ‘optimism’. We’ll add an AND expression. AND expressions allow us to have two conditions. They will check the expression before and after, and only return true if both expressions are true. Now we can add an expression for the second condition. This condition requires more pattern matching so we can use ILIKE again. The only difference is that optimism is located in the middle of the contents so we need a different pattern string. There can be zero or more characters before and after optimism so we use ‘%optimism%’ as our pattern string. We write ‘contents ILIKE ‘%optimism%’ after the AND expression.
Now that we’re filtering based on all conditions, we’ve solved the problem. I can run the query and I get the expected results.
Side note on the ILIKE expression. ILIKE has a sister expression called the LIKE expression. Both expressions work the same with one exception, the LIKE expression is case sensitive and the ILIKE expression is not. For this problem, the case of ‘draft’ and ‘optimism’ do not matter so we used ILIKE.
Question 2: Print all workers who are also managers
For our second question, we’re given two tables, worker and title, and asked to write a query that lists all of the managers.
As always, we’ll look at the table first. You’ll see that the worker table has all the information about each worker, but doesn’t list their job title. The title table lists the job title of each worker, but it only has a reference to the worker. For this problem, we need information from both tables so we will need a JOIN.
We’ll write our basic query first. SELECT * FROM sql_interviews.worker table, because we don’t know which columns we want to return yet.
Now I need to combine this table with the title table. To combine tables we use the JOIN clause. The JOIN clause is another clause that you need to know. Your interviewer will want to see that you have a solid understanding of how JOIN clauses work. JOIN clauses work by creating a table containing every possible pair from both tables and filtering that table with an ON clause. An ON clause is like a WHERE clause for JOINs. So to combine these tables we’ll add ‘JOIN sql_interviews.title’.
Now that we’re working with two tables we want to name each table with an AS clause so we can directly reference them. It’s common when writing queries to name a table after the first character in its original name. I’ll be naming the worker title ‘w’ and the title table ‘t’ so I can directly reference later in the query.
Now when we run the query we have a table that has the information about the worker and their title. We have to filter this table with an ON clause. If we could run this query without an ON clause, we would get a table that had every possible pair of worker and job title combined. Obviously, this isn’t what we want; we want every worker to be paired with their job title. We can get this table by adding our ON clause. ON clauses work the same as WHERE clauses so all we need is the correct expression. In this case, the worker_id of the worker should equal the worker_ref_id of the job title. By adding ‘ON w.worker_id = t.worker_ref_id’ we get the table we want. Now we have a table where each row has the information of a worker and their job title.
Finally, we need to filter the table such that it only contains the managers. We have two choices for filtering out the managers. We can expand our ON clause. The ON clause works the same as a WHERE clause so we can add an AND expression followed by our condition. Then we can add our second condition, “t.worker_title = ‘Manager’” and the resulting table will only have managers. That works, but we can also add a WHERE clause. The new table created by a JOIN clause works just like the original table. That means we can filter it with a WHERE clause. Just add the WHERE clause with our expression. I prefer adding a WHERE clause because it makes your ON clause simple and easy to understand.
To finish the problem I’ll choose which columns to SELECT. For this problem, I only want each manager’s first name and job title. Running this query returns all of the managers, and solves the problem.
Question 3: List employees with the same salary
For our third interview question, we’re given the worker table and we’re asked to write a query that lists all the workers with the same salaries.
To solve this problem we need to use a self-join.
As always, we first look at the table. Looking at the table we see that each row has all the information we need for the problem. It lists their worker id, name, and salary. We need to find some way to select all the pairs of workers who have the same salary.
I’ll start by writing a basic query. We’re going to SELECT * FROM sql_interviews.worker because I don’t know which columns I need. Now we’ll compare this table to itself.
Comparing a table to itself doesn’t require a new clause. If we look back to problem 2, this isn’t any different from comparing the worker table to the title table. We want every pair of workers that have the same salary. To get that we can JOIN this table with itself. To start the join we’ll add ‘JOIN sql_interviews.worker’ to our query. Now that we have two tables we need to name them. I choose to name my tables w1 for worker 1 and w2 for worker 2. Next, we’ll add our ON clause. The conditions of this problem are that the salary of the workers should be equal and we can describe that using an expression. I’ll add ‘ON w1.salary = w2.salary’ to the query. Running this query will give us a table containing every pair of workers with the same salary.
There is one issue. If we run this query, it returns more rows than expected. If you’re paying careful attention you’ll see the problem. Obviously, every worker shares the same salary as themselves. That means there is an additional row in the table for every time a worker is compared with themselves. We need to expand our ON clause so each pair must have different workers. I’ll add an AND expression followed by the new condition ‘w1.worker_id != w2.worker_id’. Now when we run this query we get a table that only contains pairs of different workers with the same salary.
Finally, we can finish our SELECT statement. For this problem, we’re interested in who has the same salary. We should return the columns for each worker’s name and the column for their salary. Running this query should return Amitah and Vivek, and Vivek and Amitah, which is correct. We’ve solved the problem.
Question 4: Find the first 5 entries of joined contacts and searches
For our fourth interview question, we’re given the tables airbnb_contacts and airbnb_searches and told to merge the tables on an appropriate key and display the first 5 results.
This problem can be more challenging because we aren’t told what to match on. It’ll require quickly forming an understanding of the tables and making a judgment based on that.
It should be noted that we’ve changed schemas from sql_interviews to datasets for this problem.
Let’s look at the tables. Immediately we see a problem, while these tables do have shared columns, the ds_checkin column, and ds_checkout column, those columns are not uniquely identifying. If a column isn’t uniquely identifying, then we can’t join on it alone. We need to understand the table better to solve this problem.
In a situation like this, it’s best to ask the interviewer questions so we don’t make incorrect assumptions. You’ll never be penalized for asking questions about the problem, so don’t worry. First, we can ask what each row represents in airbnb_contacts. The interviewer will say, each row in this table represents a contact between a guest and a host about a listing. It’s important to note that this table has the unique user id of the guest and the host. Then, we can ask what each row represents in airbnb_searches. They’ll say, each row represents a search performed by a user trying to find a listing to stay in. Given that information, we can start to form a solution. In this case, we know the users that perform the searches found in airbnb_searches are also the guests in airbnb_contacts. We can ask the interviewer if id_user column represents the same user as the id_guest column. They do, and that’s something we can JOIN on.
We’re going to write our basic query. Like before we’ll SELECT * FROM datasets.searches. Because we know we’re going to need two tables we should name our table now. I choose to name it ‘s’ for search.
This problem requires information from two tables like in previous. We’ll be using the JOIN clause again. This join is just like the JOINs we used in question two and three. Based on the questions we asked the interviewer, we know that id_user from the searches table represents the same user as id_guest in the contacts table. That’s what we’ll be joining on. We’ll JOIN with the datasets.airbnb_contacts table, naming it with an AS clause, and add ‘ON id_user = id_guest’. When we run this query it returns a table where each row represents a search that leads to contact. This is technically a solution, but we can do better.
To improve our query we need to ask our interviewer what questions they’re trying to answer with this data.
One way to improve our query is to choose the correct type of JOIN. There are two types of JOINs in SQL. We’ve been using an INNER JOIN which only returns matching pairs. We can also use an OUTER JOIN if we want information on rows that have no match. If the interviewer is only interested in matching searches where users contacted their host, then an INNER JOIN is appropriate for this query. If instead, the interviewer wanted to know the ratio of searches that resulted in contact, then an OUTER JOIN would be more appropriate. We would want to return all searches that don’t result in contact so we would use a LEFT JOIN in this case. That is the case so we’ll replace our JOIN clause with a LEFT JOIN clause.
Another way to improve our query is to make our ON clause more specific. The ds_checkin column and ds_checkout column are common across the two tables. It makes sense that adding them to the ON clause will create a more accurate representation of a user’s search intention. Some users will have multiple searches and contacts, and if we only want to return searches that lead to a contact, then each search should be for the same day as the contact. If we don’t check for this condition, then one contact in the table could result from multiple searches. We’ll improve the ON clause by adding the conditions that ds_checkin and ds_checkout are equal across the tables. Improving your ON clause in this way will show the interviewer that you have good table comprehension.
During an interview, it should be your goal to continuously improve your solutions. By asking the interviewer questions you can solve a problem exactly how they want. Even if your initial assumptions are correct and you don’t change your query, you’re still showing your interviewer that you’re through.
We only want 5 results for this problem, so we add a LIMIT clause. LIMIT clauses work by taking a number, and only returning that number of rows. For this problem, we want all the information so SELECT * works. Running this query gives us our valid solution.
Watch Our Youtube Video On These Four Questions
The last three of these problems show how important understanding JOIN clauses are. It can be challenging understanding what’s going on. If you want to practice writing SQL, I recommend joining Strata Scratch. You’ll have access to over 450 questions taken directly from real companies and you can use them to prepare yourself for an interview.
If you’ve made it this far, and you still haven’t seen the related video, then I highly recommend watching it now. The visuals will make understanding this material easier.
The twenty-first century is the age of information. The internet is now an essential part of human life, and some countries even see Right to Internet Access as inalienable. Knowledge is power, and information is the lifeblood of today’s world.
This level of connectivity has drastically changed the lifestyle of our generation. People are now more accustomed to using services online. Work, Shopping, Banking, and even social interactions are now ruled by the internet. In this day and age, an online presence is part and parcel of a healthy and interactive lifestyle.
The Growing Demand For Data Scientists
Data Scientists are in huge market today in all sectors. As computers have disrupted every primary industry in the world, experts on the subject are sought after in all areas. The same goes for Data Analytics, including but not restricted to Big Data.
All industries work based on and generate some amount of information regarding their products or their customers — for example, the healthcare industry. Terabytes of data related to innovations, medication, and patients are generated every year in this industry from research as well as day to day operations of establishments. The same can be extrapolated to any trades. Hence, Data Analysts and Scientists are needed in all fields in some capacity.
The Hottest Job Profile
It is no surprise that Data Scientist is a profession that is in demand in all fields. Every sector has its own share of digitization, and Data Analysts and Scientists are needed to look after their online presence and also make the most out of the virtual resources they have at their disposal. This is why Data Science has blown up as a hot topic in all sectors.
As far as the statistics go, all jobs that fit this profile demand basic computer programming skills as a prerequisite. It used to be that knowing SQL served as an added bonus point that put you above the competition. But the times have changed, and SQL has gone from an additional skill to a pre-requisite. It is widely accepted as the industry standard in domain-specific coding and is an unavoidable tool in the arsenal of every analyst.
SQL in Data Sciences
With Data science disrupting every industry, the role of a Data Scientist is no longer just restricted to Computer Science. There is more demand for analysts, and their work is more oriented towards a practical purpose than research or programming. Their role is to work with the data generated by their respective industry, and this is where SQL programming proves to be useful.
SQL belongs to a class of programming languages called Declarative Programming, a language that uses declarations for coding and commands. The writing itself is easy to learn and understand, and there are not many commands to learn. It is a language that is more practical than others and can be mastered by those from non-technical backgrounds as well.
“Structured” Query Language
Basic SQL proficiency is required for Data Analysts due to the nature of the job. Most businesses find it easy to perceive data in a spreadsheet or table format, which calls for a language that works based on that structure. SQL fits this description and hence is widely accepted as the industry standard.
The spreadsheet format in which SQL structures data is similar to MS Excel or other spreadsheet programs, that are popular in business as well as management circles for storing and analyzing data manually. This popularity is exploited in SQL.
Data Analytics In Other Industries
As explored earlier, the rising demand for Data Analysts in other industry is because of the disruption caused by the IT Industry in these sectors. Computers here are mainly used for storing and processing data that are harder to document manually. It also performs tasks like monitoring, tracking, performing simulations, designing, billing and so on. Almost all processes that are done using computers involve some form of Data generation, which has to be stored for later study.
In any case, most businesses end up generating and storing data in a tabular format. This is how SQL penetrated these industries, as it was created for this very purpose, of analyzing and handling such data.
SQL Proficiency: Staying A Step Ahead
SQL is a tool that has been relevant in the industry for decades and is not about to go obsolete anytime soon. Therefore, mastering it is in the best interest of all hopefuls that aspire to be a Data Scientist. SQL can be learned like any other subject, and the resources are all available online. SQL courses, SQL Problem sets, and SQL exercises are available online for studying and practice purposes.
SQL is like any other tool, and hence it serves best when it is at maximum sharpness. Therefore, regular practice is necessary to become proficient in the subject. Also, it is essential that every aspirant learns about the latest developments, and keeps expanding their knowledge on the subject.
To take your data analytics career to the next level, visit https://www.stratascratch.com/.
Structured Query Language or SQL is a domain specific language that is used to code and manage information held in a relational database management system. The Language is based on relational algebra and was developed by Edgar. F. Codd at IBM. SQL has now grown in importance, and basic knowledge of SQL is now a prerequisite for those who aim for a job in Data Analytics.
Job Opportunities In Data Science
Data Science has been blowing up in the past decade, creating thousands of jobs all over the world. Database Management Systems are used in many industries and not just core IT jobs. All sectors have an IT division which acts as the anchor to cyberspace for these firms, and this anchor is becoming more critical as the Internet as a realm of business continues to grow.
SQL In Data Science
SQL is very much in demand for jobs in the Data Science sector. It is one of the basic skills necessary, and one of the things that make you employable in this sector. SQL has been an irreplaceable tool for Data Analytics, and there are a few reasons that make it more preferable over other similar languages.
SQL is a relatively simple language and is suited for business purposes. To put it in layman’s terms, SQL is similar to MS Excel, which makes it “good for business.”
The reason for this simplicity is because SQL is a language that structures data in similar to a tabular or spreadsheet format. This is a relatively uncomplicated form of Data arrangement and makes it the natural language for businessmen, analysts, and even data scientists.
Not just the Data Structure, but the coding done in SQL is also in a simple format. The primary operations in SQL are Projections, Filters and Joins, and Aggregations. These are respectively for selecting, filtering and grouping data. The code in itself is understandable and hence can be studied from simple reading.
SQL was designed to be the industry standard. In the 1970s, there were a lot of platforms with their own compatible operating systems. This made migration a nightmare until SQL was developed. SQL today has many different versions, as not all problems can be solved by relational databases. All these versions have their applications and are all based on SQL.
SQL is also easy to learn and a popular choice as a first step towards programming. Unlike other languages, the program does not require the coder to understand the mechanics of the commands. Each query is simplistic, and this type of programming is called “Declarative Programming.”
SQL uses declarative statements as commands in the language, which are simple words or phrases that call data or perform a function. These commands either work or don’t, which means the user does not need intricate knowledge about coding. This is also the reason why even non-technical personnel are encouraged to study SQL as a part of broadening their CV.
SQL is also more optimized than other languages. As the language itself is simple, the platform does all the heavy lifting and hence can optimize the query in any way necessary. This saves a lot of effort for the developer and time for running the program.
SQL Queries are faster than others because of the structured data and optimized searching. The entire data is organized under appropriate headings and tags, so quick filtering and selection are also enabled. This also makes SQL capable of handling large volumes of data in a short time.
SQL has been relevant for over half a century because it has managed to evolve with the times. The core of SQL is still based on relational algebra, but many functions have been added to it over time. Statistical function calculations, pattern matching capabilities, and approximations. This has made it a popular language and a fundamental skill for all data analysts and developers.
Due to its popularity and ease of usage, SQL is adopted by many companies. Even big names like Amazon uses SQL for providing suggestions for users, and also provides SQL usage as a part of AWS. The search is input as a simple SQL query that pulls all data of subsequent searches by previous users and suggests the most similar and common ones.
SQL In Data Analytics Jobs
All these qualities make SQL the ideal choice to act as the universally accepted platform for Data Analytics. SQL is the go-to language for many websites, applications, and platforms for data management. The language itself requires only necessary coding skills to develop programs on and is easy enough to understand. This makes this a popular choice for even non-technical personnel to learn. This is where SQL becomes a crucial part of your CV.
As a result, SQL Interview questions and SQL problems are pervasive in Data Analytics job selection processes, as basic SQL knowledge is demanded these jobs. The language is an industry standard, and learning as well as practicing SQL online is necessary to nail these jobs. Therefore one must strive to learn advanced SQL skills to get ahead. This can be done by using online resources, SQL practice, and SQL problem set. A firm grasp on SQL is now a necessity for these jobs.
To take your data analytics career to the next level, visit https://www.stratascratch.com/.
So you’ve just managed to score an interview with an aspiring silicon valley company that you’ve had in your eyesight for years? But now the anxiety is starting to set in, and you may be afraid you oversold yourself in your resume and cover letter? Well, you should be nervous, as large tech companies are notorious for asking tough, adrenaline squeezing questions to force you out of your comfort zone. So why not go out of step out of your comfort zone right away voluntarily?
Below you’ll find a quick breakdown of potentially employment-saving advice ranging from cortisol-killing habits to ideal factual preparation. It is time to channel your nervosity into motivation to make yourself a bullet-proof candidate. Ideally, your job interview lies a couple of weeks ahead. If this is the case, start with the bottom of this list and work your way down. It is never too early to prepare for such a life-changing appointment.
Early preparations (1 month to 1 week before the interview)
Read up about the company
Reading up does not mean just scrolling through their Wikipedia page and memorize their CEO, Net Income and recent controversies. This research process is one of the best ways you can shine as a stand-out candidate during the hiring process.
When researching your (hopefully) future employer, try to answer the following questions:
If you are still wondering, why this intricate research may make or break your application. Read this overuses Art of War-quote: “If you know the enemy and know yourself, you need not fear the result of a hundred battles. If you know yourself but not the enemy, for every victory gained you will also suffer a defeat. If you know neither the enemy nor yourself, you will succumb in every battle.”
Checking your CV
Chances are if you’ve already scored an interview, you most likely sent them your CV. If not, make sure the CV you bring with you flawless. A great CV will do most of the talking for you. Besides, you may want to tailor your CV for this particular employer. There may be some qualities the employer values, that you didn’t think were necessary to include in your CV. Check the answer to question 2 mentioned above, and dig out relevant experience from your past that reflects these skills.
Refreshing Your Skills
Whether its hard or soft skills, brief but continuous refresher a couple of weeks before the interview will do you right. Especially if your previous job didn’t necessarily demand the same skillset. If you’re a developer, you’d most likely be doing light SQL. If you haven’t used SQL in a while, it would be highly advisable to find out which SQL database your employer uses, as they all have a specific syntax. Refresh your skills on how to get, insert and update data, as well as basic table creation. An excellent way to refresh these skills and prepare for potential SQL-related questions is through our dedicated service. While refreshing your skill-based knowledge usually doesn’t take more than a couple of hours, it would be preferable to get it out of the way weeks ahead and then take a glimpse of your work the day before.
The physical aspect of what causes nervosity should not be underestimated. It is no secret that exercise lowers your cortisol (stress) and is another excellent way to prove yourself, that you can step out of your comfort zone if necessary. Ideally, you should already be exercising routinely anyways, as part of a healthy lifestyle. If you are not, get off the couch as soon as possible.
Short term preparation: (1 week to 1 day before)