Are you ready to jump into the exciting world of Data Science? Whether you’re a beginner or an experienced programmer, this Best Courses Guide (BCG) is here to help you find the top online courses to get you started.

Data Science is a rapidly growing field that combines statistics, programming, and domain expertise to extract insights from data. With the right skills and knowledge, you can unlock the power of big data and make a real impact in your career. So, grab a cup of coffee and let’s start exploring Data Science together!

One note about this guide: Although data science is typically considered a superset of data analytics, data analytics has been getting a lot of traction in its own right, so I’ve also included courses that explicitly focus on data analytics in this BCG. We’ve also made sure to include Python and R as they are the most popular programming languages used for data science.

Click on the shortcuts for more details:

• Top Picks
• What is Data Science?
• Why You Should Trust Us
• Courses Overview
• Best Courses Guides Methodology

Here are our top picks

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What is Data Science?

Data science is a field that utilizes scientific induction to extract general principles from specific observations. With the vast amount of data generated daily in the digital age, it would be impossible for humans to sift through them all to discover trends. This is where data scientists come in, offloading the difficult computational work to machines through machine learning or deep learning.

However, data scientists are needed to ensure that the data fed to the machines is clean and the right machine is chosen, and to communicate the findings to those who may not be as technically inclined. This challenging and rewarding field has been gaining popularity, being hailed as the sexiest job of the 21st century by HBR and paying a median salary of $159K per year in the United States.

Data analytics, a subset of data science, has also been gaining traction, and Python and R are the most popular programming languages used in the field. With data galore and new technologies emerging every day, data science is set to become an increasingly important and exciting field for years to come.

My Experience with Data Science

I (Elham) built this guide in collaboration with my colleague @manoel.

We both come from computer science backgrounds and are prolific online learners, having completed about 45 MOOCs between us. Additionally, Manoel has an online bachelor’s in computer science, while I am currently completing my foundation in computer science. In fact, data science is the primary reason I’m interested in CS!

Why You Should Trust Us

Class Central, a Tripadvisor for online education, has helped 60 million learners find their next course. We’ve been combing through online education for more than a decade to aggregate a catalog of 200,000 online courses and 200,000 reviews written by our users. And we’re online learners ourselves: combined, the Class Central team has completed over 400 online courses, including online degrees.

Courses Overview

• All courses combined have a total of 9M enrollments and YouTube views, with the most popular course having 3.8M views
• Seven of the courses are free or free-to-audit, while three courses are paid
• Seven courses are beginner level, with the rest at an intermediate level
• This guide has a diverse list of 6 providers, with the most-represented provider being Coursera
• Three of the courses use Python, two use R, and the rest do not involve coding
• Around 356K people are following Data Science Courses on Class Central.

Best Comprehensive and Rigorous Python Course on Data Science Fundamentals (MIT)

Introduction to Computational Thinking and Data Science, by the Massachusetts Institute of Technology on edX. This free-to-audit course is designed to teach you with a wide variety of concepts and methods to excel in computational thinking and data science, and does so very rigorously, as you’d expect from an MIT course.

This course is a continuation of Introduction to Computer Science and Programming Using Python. If you have prior Python programming experience and some knowledge of algorithms and complexity, you should be ready to take this course.

Be aware that since this course is a one-to-one reflection of what students at MIT learn and do on campus, you may find some of the course material and assignments challenging.

In this course, you’ll learn:

• Efficient algorithms: Greedy algorithms, breadth-first search, and depth-first search to solve optimization problems
• Stochastic thinking: Thinking in terms of probabilities to simulate solutions to problems
• Statistical techniques: Plotting probability density functions, confidence intervals, sampling, and standard error
• Machine learning: Supervised and unsupervised learning, linear regression, and clustering
• Limitations and pitfalls of statistics: Avoiding common statistical sins used to mislead people.

The course is based on the book Introduction to Computation and Programming Using Python, Second Edition.

Best Data Science Course for Preparing for a Career in Data Analytics (Google)

If you’re looking for a program that’ll prepare you for a data analytics career, Foundations: Data, Data, Everywhere might be what you need because it’s very hands-on and job-oriented.

Taught by Google’s own data analysts, this free-to-audit course provides you with the skills and mindset necessary to become a successful junior data analyst. You’ll understand what it means to be a data analyst and learn what tools and processes data analysts use in their day-to-day workflow.

You won’t need any prior experience to take this course. You’ll learn:

• Introduction to data and data analytics, the role of data analysts in informing business decisions
• Essential skills and analytical thinking aspects of data analysts
• Six stages of the data life cycle and the data analysis process
• Data storage options: spreadsheets and databases, and visualization tools like Tableau, Looker, and R
• Various job opportunities for data analysts, best practices, and company expectations
• Importance of fairness and avoiding bias when analyzing data.

Best Course for Building a Strong Foundation in R for Data Science (Harvard)

What sets Data Science: R Basics apart from others is its unique pedagogy. Through a case study focusing on crime in the United States, you’ll analyze and use a dataset to answer questions like ‘What is the smallest state?’,  ‘What is the most dangerous state?’, and ‘What is the average murder rate in the entirety of the US?’ — without googling of course!

Although no programming experience is required, this free-to-audit course assumes you are comfortable with basic math and algebra.

You’ll learn:

• The fundamentals of R and RStudio
• What makes R a popular language for data analysis
• How to define and perform basic arithmetic and logical operations with objects
• The importance of pre-defined functions in R
• The different data types in R
• How to create vectors and use them to build lists or sequences
• The basics of vector arithmetics
• How to use indexing and subsetting methods
• The basics of plotting in R
• Four different kinds of plots to visualize patterns and trends in the data.

The Professional Certificates comes with a companion book written by Rafael Irizarry, the course instructor.

Best Python for Data Science Course for Beginners (freeCodeCamp)

If you want to learn data science with Python but have no programming experience, this course is for you.

This beginner-friendly, free course on freeCodeCamp’s YouTube channel will guide you from the ground up to help you acquire the fundamentals of both Python and data science. The course not only covers Python and data science from a conceptual standpoint, it also covers the tools and libraries data scientists use, like Anaconda, NumPy, Pandas, and Matplotlib, so you get plenty of practical experience as well.

What you’ll learn:

• Problem-solving with programming
• Installing and running Python with Anaconda
• Basics of Python programming
• Advanced Python topics
• Writing small programs with Python
• Python libraries for data science
• NumPy for fast and efficient code
• Pandas for representing and computing data
• Matplotlib for data visualization.

Best Introduction to Data Science and Its Applications Without Coding (DataCamp)

Similar to Google’s Foundations: Data, Data, Everywhere but much shorter, DataCamp’s Understanding Data Science teaches data science with no coding involved. If you’re not sure about what data science actually is and what its applications are, this course will enlighten you.

You do not need any prior experience to take this course.

In this course, you’ll explore:

• What is data science and its workflow steps: data collection & storage, data preparation, exploration & visualization, and experimentation & prediction
• The four common jobs of data science: data engineer, data analyst, data scientist, and machine learning scientist
• Data collection & storage: where data is extracted from, what it looks like, and how to store it efficiently
• Data preparation: the most important part of the data science workflow, including how to deal with missing values and outliers
• Common tools for data visualization
• Experimentation and prediction: statistical techniques like A/B testing, testing for statistical significance, and time series forecasting
• Differences between supervised and unsupervised machine learning.

Best No-Coding Data Science Course for Non-Technical Business Professionals (Johns Hopkins)

A Crash Course in Data Science by John Hopkins University is a short but intensive overview of data science — no coding involved. What makes it different from the previous listing is that it’s geared towards non-technical people who’ll manage and/or work with data scientists.

The goal of this free-to-audit course is to get you up to speed as fast as possible so that you can get to work reaping the benefits of practical data science. The course is taught from a high-level perspective, hence it will only cover the essentials without getting into the technical aspects.

There are no prerequisites required prior to taking this course.

In this course, you will:

• Understand the scientific process in data science
• Learn the role of statistics, machine learning, and software engineering in data science
• Explore the key terms and tools used by data scientists
• Study the structure of a data science project and its workflow stages
• Discover how data scientists communicate their insights and evaluate the success of their projects.

The free textbook, Executive Data Science, is based on the contents of the specialization and provides additional examples on data science project management.

Best No-Coding Data Science Course on Process Mining (Eindhoven Tech)

This free-to-audit course, Process Mining: Data science in Action is quite different from the other courses in this guide in terms of contents. For starters, this course won’t teach you coding. What it does teach you is the key theoretical tools and analytical skills needed to perform process mining — not “just” data mining, we are higher in the ladder of abstraction here, and we’re entering specialized territory.

This course asserts that processes should be considered first-class citizens, to the same extent as data, and therefore, that they should be put through the same scrutiny.

The course provides easy-to-use software, real-life data sets, and practical skills for you to directly apply the theory in a variety of application domains.

More on process mining: process mining is a technique used to analyze and track processes. Its goal is to help organizations turn event data into actionable insight. Example applications include: analyzing treatment processes in hospitals, understanding the browsing behavior of customers using booking sites, analyzing failures of a baggage handling system, and improving the user interface of an X-ray machine.

After taking this course, you’ll be able to run process mining projects and have a good understanding of the Business Process Intelligence field. You’ll also benefit from practical data science knowledge that can be applied directly to analyze and improve processes in a variety of domains. So this course has well defined scope within data science that may be suitable for learners that already have a background in the field and would like to explore an additional facet.

The course assumes a basic understanding of logic, sets, and statistics at the undergraduate level prior to taking this course.

You’ll learn:

• Overview of approaches and technologies that use event data to support decision making and business process (re)design
• Discovery process mining: algorithms for taking an event log and producing a process model without using any a-priori information
• Conformance process mining: comparing an existing process model with an event log of the same process to determine if reality conforms to the model
• Enhancement process mining: extending or improving an existing process model using information about the actual process recorded in some event log
• How to get the right event data, process mining software, and how to get from data to results.

Best Overview of Core Mathematical Ideas for Data Science (Duke)

Data science courses contain math — there’s no avoiding that!

The aim of this free-to-audit course is to teach fledgling data scientists the core mathematical concepts data science is built upon, introducing unfamiliar ideas and math symbols one at a time. By the end of this course, you’ll be ready to tackle almost any in-depth data science course out there.

To take this course, you’ll only need basic math skills. No algebra or pre-calculus needed.

You’ll learn:

• Basic set theory, including unions, intersections, cardinality, and Venn diagrams
• Real-world applications of set theory in medical testing
• Properties of the real number line and interval notation
• Sigma notation to represent sums
• Functions and graphs, including the Cartesian plane, equations of lines, and inverse functions
• Calculus concepts such as instantaneous rate of change, tangent lines, optimization, continuous growth, Euler’s number, exponents, logarithms, and the natural log function
• Probability theory, vocabulary, and notation
• Permutations and combinations, including the Binomial and Bayes theorems.

Best Data Science and Machine Learning Course for Confident Python Users (Udemy)

If you are already familiar with Python programming and want to start straight away with practical data science (especially machine learning), this course is for you. This Udemy course is the most comprehensive all-in-one package in this guide— at least, in terms of breadth.

What I like the most about this course is that it goes through the history, theory and intuition behind each machine learning algorithm before you start applying it, unlike some courses out there. This unfortunately (or fortunately for nerds like us) means the course will expose you to math and statistics, but nothing too overwhelming. Knowing some highschool mathematics and statistics should be enough to be comfortable taking this course.

You’ll learn:

• Introduction to NumPy and Pandas libraries
• Data visualization with Matplotlib and Seaborn
• Supervised machine learning models like linear regression, lasso regression, ridge regression, Elastic Net, Support Vector Machines, Decision Trees, and Random Forests
• Unsupervised machine learning models like K-Nearest Neighbors, K-Means Clustering, DBSCAN, and Principal Component Analysis
• Model deployment to the web as an API
• Model considerations like retraining and revisiting.

Also Great Introductory Course to R for Data Science (Pluralsight)

This course from Pluralsight aims to teach data science — the act of transforming data into actionable insights — using the R programming language. By the end of this course, you’ll have the skills necessary to use R to perform data analysis and deploy your own web-based interactive machine learning models.

There are no prerequisites for this course, although having some basic knowledge of programming and statistics would be helpful.

You’ll learn:

• Introduction to data science with the R programming language
• How to create actionable insight from data with R
• How and why to clean up data before analyzing it
• How to interpret descriptive statistics like mean and median
• How to visualize data to make it easy for an audience to understand trends and patterns
• How to build statistical models to form simple predictions with data
• How machine learning algorithms work to make predictions with big data
• How to deploy an interactive machine learning application on the web.

Best Courses Guides Methodology

I built this guide following the now tried-and-tested methodology used in previous Best Courses Guides (you can find them all here). It involves a three-step process:

1. Research: I started by leveraging Class Central’s database with 200K online courses and 200K+ reviews. Then, I made a preliminary selection of 6,500+ Data Science courses by rating, reviews, and bookmarks.
2. Evaluate: I read through reviews on Class Central, Reddit, and course providers to understand what other learners thought about each course and combined it with my own experience as a learner.
3. Select: Well-made courses were picked if they presented valuable, engaging content and fit a set of criteria: comprehensive curriculum, affordability, release date, ratings and enrollments.

After going through this process — combining Class Central data, our experience as lifelong learners, and a lot of editing — we arrived at our final guide. So far, we’ve spent more than 21 hours building this article, and we intend to continue updating it in the future.

Fabio revised the research and the latest version of this article.