When it comes to growing a career as an analyst, soft skills might be pretty low on the hierarchy of what someone might imagine it takes to succeed. After all, thinking of an analyst position, the technical skills are what come to mind first. Technical skills are vital in being an effective analyst. Having the hard skills to do your job effectively cannot be understated.
However, the need for soft skills in this position (or any position really) are very important as well. As an analyst, you serve a vital role in an organization’s effectiveness. But if you are unable to effectively communicate your work, or ask the questions that will lead you to the right answers, then you will find your hard skills will lag behind as well.
Soft skills are important not only because they will help you do your job better, but because they will help you to advance your career.
In this article, we will be looking at 5 soft skills that are essential to being an effective analyst. These skills will work in conjunction with your technical capabilities to make you the most effective analyst possible.
Top 5 Soft Skills For an Analyst
We will start with what is perhaps the most important soft skill for an analyst – communication. Outside of your technical capabilities, you might find that communication is the foundation of anything you do.
Whether it is sharing your work with others, making recommendations, or simply just being respectful and friendly, communication is essential to fit into just about any work environment. As an analyst, it becomes especially important because your job is technical in nature, so knowing how to communicate your findings to others is very important in making sure that they know the value you contribute.
And at the end of the day, people want to work with people they like. It really is that simple. In order to advance in a career, you need to be respectful, you need to be kind, and you need to work well with others. Fitting into an organization is sometimes just as important as the quality of your work, and communication is a vital part of your fit.
Explanation and Recording
The next important step that an analyst should take is to ensure that they explain what they are doing. You can think of this as an offshoot of the communication skill. It is the simple fact that most analysts do tasks that are not well understood by the rest of the organization. Therefore, a good analyst will do their best to keep others in the loop as to what they are doing and be sure to keep detailed records that document their work.
While it is important to get your work done effectively and efficiently, it is also important that your co-workers and managers know what you are up to. This will keep people happy with your performance and will improve your chances of advancement.
As an analyst, your job is technical, by definition. Your skills allow you to interpret areas that others can’t. However, your work is still meant to help the organization achieve something, and to work toward an ultimate goal. This is why you need to know how to effectively share your findings to other areas of the organization.
Effectively translating means that you need to know how to take the complex things that you do and convey it in terms that anyone can understand.
As an analyst, you are in charge of areas that others in the organization may not understand. People may come to you with questions that they don’t understand how to answer. You must be open and willing to help. Even if the answer is simple to you, you should always be sure you are willing to help in a non-condescending manner.
Analysts need to work in harmony with the rest of the organization, and your willingness to help others with their problems is a major part of this harmonization. So approach any problem with an open mind, and recognize that your role in the organization is sometimes to help clear up the confusion.
One of the most important things you can learn to do in any role is to ask questions. Often, people are deterred from asking questions because they feel it represents that they do not understand their tasks, or that they struggle with their role.
The reality is that any manager’s top concern is whether you can get your job done correctly. So you need to learn not to hesitate when you have a question that you think will determine your performance on a certain task.
Of course, you don’t want to be the person that asks unnecessary questions. A good practice to eliminate the number of questions you need to ask is to listen attentively and take notes.
Carrying a note pad is an excellent strategy to ensure that you retain any and all important information, and it also shows your co-workers and managers that you are taking your task seriously. Once they know this, they will be more than happy to help you with any questions you may have.
As you can see, soft skills are very important as an analyst in growing and progressing your career. You can think of communication as the bedrock for all of your soft skills. Knowing how to effectively communicate with others will improve all aspects of your work. It will give others more efficient access to your findings and will help the organization overall.
It is important to take these skills and make them a habit. Because of the technical nature of an analyst position, many do not always make time for soft skills as much as they should. As an analyst, you should dedicate time every day to grow these skills. It might involve going outside of your comfort zone, but you will find the rewards well worth the extra effort.
I love to engage with my readers and learn about their top concerns and questions when it comes to the technical interview process. In this article, I have included a question that I received from a reader about the SQL interview process.
The question that I will focus on in this article cover what you can expect in the SQL technical interview process, as well as how you should communicate this information with the interviewer.
Let’s get started by learning how you can prepare for an SQL interview, with some specific examples.
Reader's question: How should I prepare for SQL interviews?
In this section, I will give you some quick tips for preparing for SQL interview questions, with some specific examples from Facebook’s interview process.
As far as practice questions, I love to use Strata Scratch because the questions are directly from real companies. I would do medium and hard problems from all the educational modules they have available. There's over 500 SQL questions and I would seriously do as many as possible so that writing queries become second nature. I want the technical part to be the easy part of the interview process, as I would rather focus and spend my energy on communication and whiteboarding a solution with the interviewer.
I’ve gone through a few of the data scientist interviews at Facebook so I can help you with that and give you some insight into what type of questions they asked me.
Here are two examples of the type of questions you can expect:
In my experience across a few interviews at FB, the biggest focus they're testing for is an understanding of what the code is doing and the implications to the results.
Technically, the SQL portion is rather easy. You are either given 1 or 2 tables and asked to create a SQL query that requires doing a join or a self-join, or you are given SQL code and asked to debug it.
So long as you understand JOINS and slightly advanced functions like COALESCE, you'll be fine technically. What makes the interview difficult is linking how the code is written to the implications of the question they're asking you. Here’s an example:
For example, you are given 1 table that has user friend requests, acceptances, and dates all on 1 table. How do you write a query such that you'll get % of friend acceptances over time?
This query is easy to create, but you'll be additionally tested on the trade-offs of how you wrote the query. How do you deal with a friend acceptance that happened days later? Which day do you count the acceptance? Do you count it on the day the friend sent the request or do you count it on the day the request was accepted? There's no right or wrong answer here but you need to talk about the trade-offs between those two choices.
Another quick example: If you ran an AB experiment and saw a 2x increase in friend acceptance, with a p < 0.05, due to a new feature, do you deploy it to production? Most people would say yes, but in an interview, the most obvious answer is probably not the correct one. The correct one in this case is -- it depends. Then you talk about why it depends and what additional information you would need to make a decision.
These are all examples of the types of considerations you will be expected to mention to formulate a complete answer.
For a second example, you have a master table that contains a user ID and their latest login date, and you have a 2nd table that contains all the users that logged in for that day (there could be multiple logins on that day by the same user). Write a query that will update the master table with the user ID and their latest login date.
Simple enough, but you need to go through the exercise of understanding all the different scenarios. In this case, you have the scenario of a new user that just signed up that day, as well as a scenario where you have multiple logins from the same user for that day. How do you deal with these cases?
I'm sure you're thinking these questions are simple as you are reading this article. But what makes it hard is that you're not expecting these questions during the interview but you're being asked to dissect and solve it on the fly in front of an interviewer. It can be a stressful situation.
Main Advice for SQL Interviews
My main advice to you in preparing for an SQL interview is to understand why and how code is being written to solve a specific problem. Be prepared to communicate why you're writing specific lines of code, what logic you're adding to solve specific edge cases and scenarios, and what the output will yield. Your explanation is just as important (if not more important) than the code itself.
You'll need to know the trade-offs for every metric you're asked to create and how it may impact the question. You'll need to understand all the different scenarios and edge cases and how you can solve them in your code.
Also, be sure to talk with the interviewer as you're building the solution, and keep them in the loop about your thought process as much as possible.
I hope this one example summarizes the main ideas of what you can expect in an SQL technical interview. As you can see, it's mainly testing your understanding and application of the code in various scenarios.
It is perhaps most important that you effectively walk the interviewer through your thought process. Keep them in the loop about what you are thinking at all times. They are mainly testing your approach and your understanding of the technical aspects, and these things can be demonstrated even without a correct answer.
SQL interviews are meant to test your technical skills. Zero-in on the skills that are going to be included in your interview and be sure to do as many practice questions as possible.
Here, we’re going to focus on a very important part of the analytics interview process: what the hiring manager is looking for in an analytics candidate. We know how stressful the interview process can be so we hope that, by knowing a little more about the hiring process itself, you can adjust your approach to the interview to get the best chance of success.
First, we are going to explain the general types of questions you can expect in an analytics job interview. Then, we’ll take a look at five crucial factors that a good candidate should have.
What Can I Expect From an Analytics Job Interview?
In an analytics job interview, they expect you to be able to code well, obviously. By this point, you should have experience with the type of coding language they are evaluating and have the data analysis skills to handle whatever they throw at you.
You will likely encounter a use case during the interview, which is basically a scenario that can sometimes be complex, requiring multiple steps and solutions for you to solve. Often, this is based on a problem the company has experienced or is currently experiencing.
Obviously, they expect you to attempt to solve this problem but it’s equally important for you to explain how you got your solution. They are evaluating your process and approach. How you arrive at your solution is just as important as the solution itself.
5 Factors that Analytics Interviewers Look For In Candidates
We mentioned that the main thing to expect during an analytics job interview is that it is about the process. There are so many ways to solve a problem in analytics that keeping open communication with the interviewer and explaining how you reached each decision and how you are going to handle each use case and edge case is crucial.
Keeping an open mind is a key factor in this interview process. You must evaluate each question with the willingness to consider each available option. This is crucial to even identify the problem you are trying to solve.
Your goal is to have a healthy conversation with questions and answers that demonstrates to the interviewer that you have considered all the options. By doing this, you also have a better chance of putting yourself in the position to choose the best option.
2. Structured Thinking
As explained above, analytics interviewers are looking for someone open-minded and willing to consider all available alternatives before choosing the best one. This shows an understanding of the math and the theory behind what you are trying to do. Communicating your process and explaining how you approach the data are important for the interviewer to understand your analytical skills.
In order to effectively communicate, you must develop a pragmatic and structured line of thinking in approaching the problem. This way, no matter what problem you face, you have a structured method to show the interviewer exactly how you approached the solution.
Questions can often involve analyzing lines of codes. Be careful to look at the syntax and explain what each block of code is achieving. From there, identify a “big picture” for what the code is achieving and identify what could be added to or removed from this code to reach a solution. By following this reliable process, you ensure that the interviewer can easily follow your thought process. And you ensure that nothing is missed on your end.
3. Closing the Edge Cases
Edge cases are one of the most important parts of understanding an analytical problems. With any problem you encounter in an interview, try to think of any edge cases where the code could break and communicate that to the interviewer. In addition, identify edge cases where certain situations in the business problem might not be captured in your solution. Then build a solution to capture those edge cases. Suggest ways that they could be accounted for so that a given situation will not break the code. This is much easier to do because you identified the potential problem areas first.
4. Understand Trade-Offs
In technical interviews, there is almost always more than one way to analyze the data and solve a problem which raises the inevitability of trade-offs. You might have to forgo one method to pursue a different one. In your interview, the most important thing to do in this situation is to identify these trade-offs then explain each option and why you may or may not chose one over the other. This should be a conversation with the interviewer so that your solution is optimized for the business goal.
Your solution is probably not going to be 100% perfect but if you communicate why you chose one option over the other and why you think your solution is ideal, the interviewer will be satisfied with your problem-solving skills and analytical abilities. These are the things that interviewers are often most interested in evaluating.
5. Explain What the Solution Will Give You In A Way That Is Easy For Anyone To Understand
Once you are satisfied with your thought process and your solution to the problem, it is important to wrap it all up. A great way to do this is by providing a summary of your solution to the interviewer. Remember, the most important thing to get across is that you understand the problem, what you’re trying to achieve, and are willing to evaluate all the options to reach a solution.
So, wrap up each answer with a summary. Quickly explain your solution, the options you considered, the trade-offs, the edge cases, and why you believe your solution is the best. This ensures that the interviewer comes away with an understanding of your thought process.
You have probably noticed a common theme throughout these points. In these technical interviews it’s about about showing your understanding of the concepts and the ability to think critically and pragmatically rather than reaching the correct solution to a question. The right answer is really only a piece of the puzzle. Analytic interviewers approach the interview process with the intention of hiring someone who approaches each problem with an open mind and a willingness to consider every alternative.
These qualities represent a candidate who will not only think strategically but is also not afraid to work with others or ask for help. After all, companies want to hire someone they can work with and you need to prove that you have the communication skills and insight necessary to do just that.
If you want some practice with technical problems and want to see how others have approached the same problems, try some of the technical questions in Strata Scratch and review the approach and solutions from their users.
Today we’re going to focus on one of the most important parts of the recruitment process and the one that is probably the most feared: the technical interview. Your first interview (other than the screening call with the recruiter) is usually the technical interview, which is sometimes done over the phone with a screenshare. If you do well there, then you’re often invited to the second round, which may contain multiple technical screenings. There are many tips for success in the interview process, in fact, we have written interview preparation guides before. Check one out here. That said, you’re often nervous, sweating, and not thinking straight. It’s important to not only know what to do, but to also know what not to do (i.e., sort of like when you take a driving test at the DMV -- you can do everything right 99% of the time, but there are just some things that will automatically fail you, like hitting the curb).
Here, we’re going to give you our top six tips for what not to do during a technical interview. Let’s get started!
1. Do not immediately start to codeOne huge thing to note right off the bat is that analytical interviews are not just about finding the right answers. The interviewer is testing your ability to solve a problem which includes your ability to ask questions and use problem-solving skills to gain an understanding of the question or questions behind the question being asked.
To demonstrate this skill set, you can’t just start coding as soon as you receive the question or coding challenge in an interview. First, you should ask clarifying questions and state assumptions to demonstrate that you understand exactly how to solve this problem. The interviewer wants to see your process to make sure you understand the best way to approach the problem.
Even if you feel confident about the problem and don’t have any questions, you should communicate that to the interviewer. They want someone who is confident in their understanding of the problem before they begin coding. So, don’t just start coding right away – stop, ask questions, and demonstrate your understanding.
2. Don’t brush off the interviewer’s hints
Often, an interviewer gently nudges you in the right direction. Ultimately, they want you to succeed so they might drop hints or ask questions to lead you toward the solution. This is why it is so important to consider everything the interviewer says in the interview and not to brush off or gloss over their comments.
If they say something like, “What about the denominator in the ratio…” or, “Have you taken a look at this metric…”, then you need to think about that and apply it. In probably all cases, they wouldn’t have said it if it wasn’t relevant.
3. Don’t be too opinionated
One of the top skills that an interviewer is looking for is the ability to adapt. Don’t be someone with strong opinions who is unwilling to budge. You should demonstrate the ability to consider different options and adapt your approach when new information is present.
Be aware that job interview questions might be designed to test this. By presenting new information, the interviewer checks to see whether you can reflect on what you have done, recognize any errors, and adapt. Often, this skill is just as important as finding the right answer as it represents your ability to continuously work to get there.
Don’t be too opinionated. Go into the interview with a flexible and open mind.
3. Don’t accept the most obvious answer Analytics job interviews are supposed to weed out a certain proportion of interviewers. If they made it too easy, they wouldn’t be getting the best people for the job. This often means that the answer to the technical questions is not going to be the most straightforward, obvious one. After all, these questions are there to test your skills.
What interviewers are looking for is someone who carefully examines the questions and considers all the available alternatives and understands trade-offs. What does this mean for you? You should walk them through your decision-making process. Show them that you are considering each option and considering how each variable might affect your answer. This way, even if you do not reach the correct answer, they can see that your decision-making process is sound.
Do not accept the most obvious answer. Often, the answer is meant to seem obvious to trick you. Interviewers will not be satisfied if you do not give each option proper consideration.
4. Don’t talk about how you’re unqualified because you don’t have formal education in a technical field.
The fact is that not everyone working in analytics has a technical background. There are many ways to learn analytic skills that do not involve a formal education. Many people in analytics do not have a technical background but, through hard work and dedication, have achieved the skills necessary to do these jobs and do them well.
That said, if this applies to you, you should not draw attention to this fact - it’s not relevant. You are there and you have the skills you need to do the job so drawing attention to the fact that you are perhaps less qualified than those with a formal education is not going to help you get the job.
In an interview, you only need to bring something up if it is relevant to your ability to succeed in the role. If you are asked about your formal education, do not hide details but there is no need to bring it up if you aren’t required to.
5. Don’t talk about how you’re qualified because you do have a formal education in a technical field.
On the flip side, do not talk about how extremely qualified you are because you have a formal education in analytics. Likely, the interviewer already knows this based on your resume and mentioning it, again and again, can come off as bragging. At the end of the day, the interviewer doesn’t care about your formal education, they just want to see if you have the skills necessary to do well.
This interview exists to test your skills, after all, not to check up on your formal education. Focus on showing them what you can do, not talking about your degree.
It’s always a good idea to be prepared but it’s just as important to know what not to do in certain situations. We hope our tips have helped you in your preparation for your technical interview.
If you take one thing from this list, remember that these interviews are about the process. Even if you don’t think you can get the right answer, walk them through your decision-making process and show them that you are willing to accept all alternatives. A person who approaches the work this way is bound to get the right answer eventually, which is what they really want to see.
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