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.
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