Best Data Science Courses

Updated on June 16, 2022
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Are you looking for a career change? Are you thinking about becoming a professional data scientist? A great idea, of course, as data science is a booming field providing numerous job opportunities to people worldwide.

But, what is data science exactly? – It is a multidisciplinary field that focuses on preparing data for analysis, including finding actionable information in large, raw, or structured data sets, cleansing, aggregating, identifying patterns, and uncovering other insights. Sounds pretty exciting and challenging, right?

TitleMeta ScoreDifficultyDurationPlatform
What is Data Science?
by Rav Ahuja
Based on 54K ratings, and 587.6K students.
Difficulty: Beginner
9 hours
Platform: Coursera – Review
Rating: 4.9/5
2022 Python for Machine Learning & Data Science Masterclass
by Jose Portilla
Lifetime Access
Based on 7.5K ratings, and 62.1K students.
Difficulty: Beginner
44 hours
Platform: Udemy – Review
Rating: 4.5/5
Learn SQL | SQL Course & Training
by Malavica Sridhar
Lifetime Access
Based on 299 ratings.
Difficulty: Beginner
80 hours
Platform: Udacity – Review
Rating: 4.8/5
The Data Science Course 2022: Complete Data Science Bootcamp
by 365 Careers
Lifetime Access
Based on 107.2K ratings, and 492.8K students.
Difficulty: Beginner
30 hours
Platform: Udemy – Review
Rating: 4.5/5
Data Science Specialization
by Jeff Leek
Based on 38K ratings, and 460.5K students.
Difficulty: Beginner
308 hours
Platform: Coursera – Review
Rating: 4.9/5
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Data Science Overview

Now, if you are still keen on the idea of becoming a data scientist, continue reading this article to find out everything you need to know about the topic, from the tools to use, jobs to explore to where to find the best data science courses to master your skills.

Best Way to Learn Data Science

The demand for data scientists is at an all-time high. So if you’re considering a career in data science, now’s the best time to get started. Yet, where exactly to start? No worries, here’s how you can quickly build your data science skills.

On the one hand, you could always enroll at a college or university and earn a degree in data science. However, not only could the experience cost you lots of money, but it will also take you years to complete, and thus not be able to work in the field. The same goes for the plenty of academies and schools out there.

On the other hand, but lots more convenient, you could take some of the best data science courses provided by online learning platforms. Since the demand for the field is constantly growing, all the world’s best online course providers are focusing on offering classes on the topic and helping learners learn or improve their skills in several weeks or months. Plus, you can find some for free, which is fantastic.

Similarly, take advantage of all the online resources available. For instance, subscribe to newsletters, read blogs or listen to podcasts from world-known data science experts, contribute to open-source projects, create your own projects, watch YouTube videos, etc. The list is endless as there are plenty of learning opportunities on the internet.

Why Learn Data Science

In the 21st century, data is considered the new driving force behind every industry aiming to boost performance. Consequently, data science has become the most sought-after job in the market, making data scientists some of the highly paid professionals.

The truth is that companies need professionals who are proficient at handling, managing, understanding, and analyzing trends in data to provide them with an in-depth analysis of their performance and give recommendations on how to achieve objectives.

And, since there is a massive discrepancy in the demand and supply of such experts, companies are willing to pay astronomical figures for these positions. According to the U.S. Bureau of Labor Statistics, the average data scientist’s salary is a bit more than $115,000. Remember that you can earn even more depending on your experience, job title, industry, and company size.

Perks of Being a Data Scientist and Big Brands

Data science jobs are predicted to grow by over 360,000 new listings in the U.S. only. Hence, becoming a data scientist is undoubtedly a smart move. But, the high job growth rate isn’t the only thing worth mentioning. Indeed, there are many other perks of being a data scientist, including:

Good Work-Life Balance

Pursuing a career in data science is an excellent choice for those looking for an office job with regular working hours, Monday through Friday. However, remote work and flexible hours are also possible, which is great for keeping a good work-life balance.

Work with Top Executives and Big Brands

The main job task of a data scientist is analyzing data to boost a company’s performance. In other words, these professionals get to communicate and work with the top executives, i.e., the C-level administration and the ‘who’s who in the company. Likewise, they also get the opportunity to work with giant companies like Google, Facebook, Amazon, LinkedIn, etc.

Steer Company Decisions

Not only should data scientists analyze data, but they should also come up with possible solutions and recommendations to improve the company’s performance, revenue, etc. That said, you get the chance to make a difference and be part of the decision-making process.

Competitive Salary and Benefits

We have already noted that the average salary of a data scientist is a bit higher than $115,000. Namely, even the starting salaries for these positions remain high. An entry-level data scientist, for example, gets a tidy median pay of almost $110,000 a year. And, as you would expect, salaries for manager positions go up to $250,000.

Continuous Learning Opportunities

Finally, working in the data science field allows professionals to upgrade their knowledge by constantly exploring different industries in-depth, learning new tools, building great networks, and developing business problem-solving skills, all of which are essential when starting a business.

Data Science Trends of 2022

Over the past years, especially during the pandemic, we’ve seen how companies adopt the latest technology to increase performance, productivity, and ROI (return of investment). In addition, they’ve put a lot of effort into adopting data-driven models to streamline processes and make intelligent decisions based on data analysis. So now, let’s look at the utmost data science trends of 2022 to understand how data is becoming an essential part of every business.

Moving Big Data to Cloud Platforms

Enterprises have faced challenges collecting, cleaning, structuring, formatting, and analyzing the vast volume of data gathered in one place. That’s when data science models come to the rescue together with artificial intelligence. Companies are increasingly moving big data to cloud platforms for storage, processing, and distribution of data.

Internet of Things (IoT) Integration

The use of big data in the Internet of Things is the next emerging data science trend of 2022. It refers to the network of physical objects embedded with sensors, software, and other technologies. This network allows different devices to connect and exchange information over the internet. Now, businesses are integrating IoT with data analytics to boost the accuracy of responses.

Use of Edge Computing in Business Systems

Edge computing also referred to as edge intelligence, is becoming a mainstream process in 2022. It is where data analysis and aggregation are done close to the network to increase flexibility, scalability, and reliability. Eventually, this will lead to a company’s better performance, reduced latency, and improved processing speed.

Python as an Integral Part of Data Science

Last but not least, many would agree that Python is still the top programming language which is why most data scientists consider it an integral part of the field. Hence, it doesn’t surprise that Python will keep ruling the world of data science in 2022. It allows collaboration while simplifying integrations for other programming languages and libraries.

Data Science Jobs

We already noted that data drives business decision-making today. And, data scientists are no longer restricted to IT industries only. The truth is, data scientists are needed across all sectors from retail, finance, entertainment, and transport, to government and everything else in between.

That’s the reason why pursuing a career in data science has been generating lots of interest lately. Here’s a list of possible job positions you can hold if you want to work in the data science world.

Data Analyst

Your first job position would probably be a data analyst when starting your data science career. You would clean and study raw data gathered through the systems, create reports, and cooperate with several teams, such as the marketing, sales, finance, customer support department, etc.

Average Annual Salary: $61,841

Data Scientist

We’ve already provided you with a general idea of what it’s like to be a data scientist. But, of course, you would not only have to analyze big data to address issues and find solutions. Indeed, you would also closely cooperate with the C-level administration to generate actionable insights and business strategies.

Average Annual Salary: $109,257

Data Engineer

Moving on to data engineers – experts in accessing and processing ample amounts of real-time data and interpreting unformatted and unverified data. Examples of daily tasks include maintaining high data volumes or making data accessible by creating data pipelines. In short, they design the infrastructure using programming languages.

Average Annual Salary: $114,353

Data Architect

Next, we have data architects who are visualizers of a company’s entire data management framework. They are in charge of understanding the company’s strategy and collecting the needed data to create new database systems or improve existing ones. Plus, they make the flows and processes for data management.

Average Annual Salary: $121,606

Machine Learning Engineer

At last, here’s a unique mix of a software engineer and a data scientist who works with big data on a daily basis. Both roles may work closely together but also have independent tasks. Yet, data scientists should be machine learning experts with great programming skills as machine learning engineers develop software, models, and artificial intelligence systems.

Average Annual Salary: $118,948

Data Science Tools

The constantly increasing volume of enterprise data and its complexity and importance in decision-making are driving businesses to invest in new technologies and tools to help them gain valuable insights from data assets. Precisely speaking, a survey conducted about half a year ago showed that 91.7% of business executives from 94 big companies are boosting investments in data and AI. But what’s all the fuss about making such investments? – Basically, using data science tools and technologies is vital to learning and working in the field, and they also make everyday work more straightforward and simple.

Therefore, let’s have a look at some of the best tools and technologies that may be able to help you in the analytics process.

Programming Languages


Python is certainly on the top of our list of data science technologies to learn as it is the most widely used programming language for both machine learning and data science. It is an interpreted, object-oriented programming language with dynamic semantics with built-in data structures, typing, and binding capabilities. It can be used for data analysis, data visualization, natural language processing, artificial intelligence, etc.


The R programming language is design, especially for statistical computing, graphics applications, data manipulation, analysis, and visualization. Logically, data scientists use R to collect, cleanse, analyze, and display data. R is an open-source environment, an interpreted language like Python, and a very reputable choice of programming language among data experts.


Here’s an exceptional programming language and analytics environment for numerical computing and modeling and data visualization. Engineers initially use it to analyze data and develop algorithms and systems for wireless communications, signal processing, and other applications. And although it isn’t that popular in data science, MatLab is useful for machine and deep learning, predictive modeling, big data analytics, etc.


This integrated software suite is perfect for statistical analytics, advanced analytics, business intelligence (BI), and data management. It allows data scientists to integrate, cleanse, prepare, and manipulate data that can be analyzed using various statistical and data science methods. In addition, SAS is great for completing numerous tasks, including basic BI, data visualization, risk management, data mining, predictive analytics, and machine learning.


Although many data scientists tend to incline to Python and R for writing programs for managing and processing data, Java is still a highly scalable programing language. It makes it easier to scale up or down and provides excellent load balancing features. What’s more, many key frameworks crucial in the field of Big Data, for example, Apache Spark, run on the JVM (Java Virtual Machine).


Julia is yet another open-source programing language used for numerical computing, machine learning, and other types of data science applications. It was created with a specific idea in mind – to be that one language addressing all possible needs. That said, Julia is a mix of high-level dynamic language and performance comparable to statically typed languages like Java and C. And, an in-built option defines data types in programs, not the users.


Once again, data scientists are expected to learn one of either Python or R. However, learning JavaScript too will undoubtedly prove valuable if you want to specialize in data visualization or need to use a product built using JavaScript or some of the related technologies. Also, the machine learning capabilities of JavaScript are advancing quite rapidly. Hence, mastering this programing language would be an excellent addition to your CV.

Software Technologies

Apache Spark

Apache Spark is an open-source data processing and analytics engine suitable for rapidly handling vast amounts of data. Thanks to its speed, this technology is great for continuous intelligence applications powered by near-real-time data processing. Furthermore, it is suited for extract, transform, and load uses and other SQL batch tasks.


Jupyter Notebook is another open-source web application enabling interaction among data scientists, engineers, mathematicians, etc. The tool is used to create, edit, and share code, text, images, and other information. Jupyter can add code, computations, comments, visuals, and media representations to a document that can be shared with others.


D3.js, also known as D3 (Data-Driven Documents), is a JavaScript library for creating data visualizations in a web browser. It uses HTML, Scalable Vector Graphics, and CSS instead of having its graphical vocabulary. The tool is helpful for designing numerous kinds of data visualizations and supports interaction, animation, annotation, and quantitative analysis.


Pandas is another popular Python library usually used for data analysis and manipulation. It’s composed of two primary structures: the Series (one-dimensional array) and the DataFrame (two-dimensional structure). Moreover, Pandas features in-built data visualization capabilities and exploratory data analysis functions and supports other file formats and languages.


Here’s one developed by Google and famous for implementing deep learning neural networks. As the name suggests, TensorFlow takes inputs in the form of tensors and then uses a graph structure to flow data through computational operations determined by developers. And it provides an eager execution programming environment for running operations without graphs.


Last but not least, Tableau is one of the most popular tools used by data scientists and business intelligence professionals. It is a data visualization tool enabling users to create insightful and impactful visuals interactively and creatively. Its use goes beyond creating traditional graphs and charts.

Data Science Career Opportunities

Careers in Data Science have been generating quite the buzz lately. Data science has become a revolutionary technology and has been named the sexiest job of the 21st century. Namely, there are millions of worldwide job openings in data science with big names like Oracle, Microsoft, Apple, Walmart, etc., regularly having job postings for data scientists.

So, one thing is for sure! Provided you have the right qualifications (earning a university degree isn’t a must), you can jumpstart a bright career in data science as the demand is continuously increasing. It is actually the fastest-growing job on LinkedIn – predicted to offer 11.5 million job openings by 2026.
And those who are already in data science roles are enjoying higher salaries.

Furthermore, the field of data science offers an abundance of positions and opportunities. Not only can you land a job as a data scientist, engineer, analyst, architect, or machine learning engineer (all of which we’ve explained above), but you can also explore other job options like statistician, mathematician, business analyst, marketing analyst, clinical data manager, and much more.

Put shortly, data science has found its application in almost every industry, including healthcare, gaming, marketing, image recognition, recommendation systems, logistics, government, law, etc. That said, you can master your data science skills and improve your career in whichever industry you are working in.

Data Science Prerequisite

If you want to begin a data scientist career path, you have to prepare for a challenging workload as the qualification and skill requirements for data science roles are one of the steepest.

This means that you’ll need hard skills like analysis, machine learning, mathematical modeling, statistics, and familiarity with trending programming languages, databases, and other technologies and tools. In addition, you also have to have excellent soft skills like critical thinking, persuasive communication, listening, problem-solving, etc.

But, once you have the right qualifications and skills, you can rest assured that numerous job opportunities are waiting for you as data scientists are always in demand

Best Data Science Online Learning Platforms

Here are the best online platforms for learning Data Science. You can find out everything about these online learning platforms by reading our in-depth review.

Rating: 4.5/5
Datacamp is the go-to online learning platform for Data Science students and enthusiasts.
Rating: 4.9/5
Coursera offers University-level courses, accredited certifications, and has one of the biggest learning communities.
Rating: 4.6/5
edX offers University-level courses for free and has a great variety of topics.
Rating: 4.8/5
Udacity can help you aim higher in your professional career.
Rating: 4.5/5
Udemy is an online learning platform with the largest number of courses.
Rating: 3.8/5
Pluralsight helps to build better tech workforce with its highly proficiency courses.
Rating: 4.4/5
Codecademy will make you a code master.

Where to Learn Data Science

Now, it’s time to focus on where to learn the fastest-growing field in tech at the moment, i.e., where to learn data science.

We’ve already mentioned at the beginning that some possible ways to learn and master your data science skills include enrolling at a college, university, or an academy. However, getting a degree will take you years and cost tens, if not hundreds, of thousands of dollars.

Fortunately, there are many examples of successful people who’ve learned data science and grown their careers by taking the best data science courses available online. Indeed, online courses provide you with the chance to learn all the necessary skills faster and much more affordable. In addition, some courses actually come free of charge. Amazing, right?

Online learning platforms like DataCamp, Coursera, edX, Udacity, Udemy, Khan Academy, Pluralsight, and Codecademy make the perfect place to start exploring the best data science courses. They all specialize in offering high-quality tech courses and programs taught by world-renowned instructors and experts in the field.

These courses provide you with an in-depth knowledge of the most advanced and up-to-date technologies, tools, and skills needed to start and grow your career in data science. That said, don’t wait! Turn on your computer and let your online learning experience begin!