Over several years and 100+ hours watching course videos, engaging with quizzes and assignments, and reading reviews on various aggregators and forums, I’ve narrowed down the best data science courses available to the list below.

This is a fairly long article with reviews of each course, so here’s the TL;DR:

7 Best Data Science Courses & Certifications for 2024:

1. Data Science Specialization — JHU @ Coursera
2. Applied Data Science with Python Specialization — UMich @ Coursera
3. Data Science MicroMasters — UC San Diego @ edX
4. Dataquest
5. Statistics and Data Science MicroMasters — MIT @ edX
6. CS109 Data Science — Harvard
7. Python for Data Science and Machine Learning Bootcamp — Udemy

Criteria

The selections here are geared more toward individuals getting started in data science, so I’ve filtered courses based on the following criteria:

• The course goes over the entire data science process
• The course uses popular open-source programming tools and libraries
• The instructors cover the basic, most popular machine learning algorithms
• The course has a good combination of theory and application
• The course needs to either be on-demand or available every month or so
• There are hands-on assignments and projects
• The instructors are engaging and personable
• The course has excellent ratings – generally greater than or equal to 4.5/5

There are many more data science courses than when I started this page four years ago, so there needs to be a substantial filter to determine which courses are the best. I hope you feel confident that the courses below are worth your time and effort because it will take several months to learn and practice to be a data science practitioner.

Complementary courses

Some of the courses listed below teach introductory Python, but if you'd like to learn programming before joining a data science course, check out my picks for the best Python courses.

In addition to the top general data science course picks, I have included a separate section for more specific data science interests, like Deep Learning, SQL, and other relevant topics. These courses have a more specialized approach and don’t cover the whole data science process, but they are still the top choices for that topic. These extra picks are good for supplementing before, after, and during the main courses.

If you're more interested in just learning machine learning, then check out my complementary article on the best machine learning courses for this year.

Book companions

When learning data science online, it’s vital to get an intuitive understanding of what you’re actually doing and sufficient practice using data science on unique problems.

In addition to the courses listed below, I would suggest reading two books:

1. Introduction to Statistical Learning — available for Free — is one of the most widely recommended books for beginners in data science. Explains the fundamentals of machine learning and how everything works behind the scenes
2. Applied Predictive Modeling — a breakdown of the entire modeling process on real-world datasets with incredibly useful tips each step of the way

These two textbooks are incredibly valuable and provide a much better foundation than just taking courses alone. The first book is incredibly effective at teaching the intuition behind much of the data science process, and if you can understand almost everything in there, then you’re more well off than most entry-level data scientists.

Furthermore, since both of these books utilize R in their exercises and examples, a great learning experience would be to work through them in R and then convert them to Python.

Data Science Specialization — JHU @ Coursera

This course series is one of the most enrolled and highly rated course collections on this list. JHU did an incredible job with the curriculum's balance of breadth and depth. One thing included in this series that's usually missing from many data science courses is a complete section on statistics, which is the backbone of data science.

Overall, the Data Science specialization is an ideal mix of theory and application using the R programming language. As far as prerequisites go, you should have some programming experience (it doesn't have to be R) and a good understanding of Algebra. While not necessary, previous knowledge of Linear Algebra and Calculus is helpful.

Price – Free or $49/month for certificate and graded materials
Provider – Johns Hopkins University

Curriculum:

1. The Data Scientist's Toolbox
2. R Programming
3. Getting and Cleaning Data
4. Exploratory Data Analysis
5. Reproducible Research
6. Statistical Inference
7. Regression Models
8. Practical Machine Learning
9. Developing Data Products
10. Data Science Capstone

If you're rusty with statistics and want to learn more about R first, check out the Statistics with R Specialization.

Applied Data Science with Python Specialization — UMich @ Coursera

The University of Michigan, which also launched an online data science Master’s degree, produces this fantastic specialization focused on the applied side of data science. This means you’ll get a solid introduction to commonly used data science Python libraries, like matplotlib, pandas, nltk, scikit-learn, and networkx, and learn how to use them on real data.

This series doesn’t include the statistics needed for data science or the derivations of various machine learning algorithms but does provide a comprehensive breakdown of how to use and evaluate those algorithms in Python. Because of this, I think this would be more appropriate for someone that already knows R and/or is learning the statistical concepts elsewhere.

If you’re rusty with statistics, consider the Statistics with Python Specialization first. You’ll learn many of the most important statistical skills needed for data science.

Price – Free or $49/month for certificate and graded materials
Provider – University of Michigan

Courses:

1. Introduction to Data Science in Python
2. Applied Plotting, Charting & Data Representation in Python
3. Applied Machine Learning in Python
4. Applied Text Mining in Python
5. Applied Social Network Analysis in Python

To take these courses, you’ll need to know some Python or programming in general, and there are actually a couple of great lectures in the first course dealing with some of the more advanced Python features you’ll need to process data effectively.

Data Science MicroMasters — UC San Diego @ edX

MicroMasters from edX are advanced, graduate-level courses that count towards a real Master’s at select institutions. In the case of this MicroMaster’s, completing the courses and receiving a certificate will count as 30% of the full Master of Science in Data Science degree from Rochester Institute of Technology (RIT).

Since these courses are geared toward prospective Master’s students, the prerequisites are higher than many of the other courses on this list. Since the first course in this series doesn’t spend any time teaching basic Python concepts, you should already be comfortable with programming. Spending some time going through a platform like Codecademy would probably get you up to speed for the first course.

Overall, I found this MicroMaster’s to be a perfect mix of theory and application. The lectures are comprehensive in scope and balanced superbly with real-world applications.

Price – Free or $1,260 for certificate and graded materials
Provider – UC San Diego

Courses:

1. Python for Data Science
2. Probability and Statistics in Data Science using Python
3. Machine Learning Fundamentals
4. Big Data Analytics using Spark

The one downside of this MicroMaster’s, and many courses on edX, is that they aren’t offered as frequently as other platforms. If your schedule aligns with the first course's start date, consider jumping in.

Dataquest

Dataquest is a fantastic resource on its own, but even if you take other courses on this list, Dataquest serves as a superb complement to your online learning.

Dataquest foregoes video lessons and instead teaches through an interactive textbook of sorts. Every topic in the data science track is accompanied by several in-browser, interactive coding steps that guide you through applying the exact topic you’re learning.

Dataquest stands out from the other interactive platforms because the curriculum is very well organized, you get to learn by working on full-fledged data science projects, and there's a super active and helpful Slack community where you can ask questions.

The platform has one primary data science learning curriculum for Python:

Data Scientist In Python Path
This track currently contains 31 courses, covering everything from Python's basics to Statistics to math for Machine Learning, Deep Learning, and more. The curriculum is constantly being improved and updated for a better learning experience.

Price – 1/3 of content is Free, \$29/month for Basic, \$49/month for Premium

Here's a condensed version of the curriculum:

1. Python - Basic to Advanced
2. Python data science libraries - Pandas, NumPy, Matplotlib, and more
3. Visualization and Storytelling
4. Effective data cleaning and exploratory data analysis
5. Command-line and Git for data science
6. SQL - Basic to Advanced
7. APIs and Web Scraping
8. Probability and Statistics - Basic to Intermediate
9. Math for Machine Learning - Linear Algebra and Calculus
10. Machine Learning with Python - Regression, K-Means, Decision Trees, Deep Learning, and more
11. Natural Language Processing
12. Spark and Map-Reduce

Additionally, there are also entire data science projects scattered throughout the curriculum. Each project's goal is to apply everything you've learned up to that point and familiarize you with what it's like to work on an end-to-end data science strategy.

Lastly, if you're more interested in learning data science with R, check out Dataquest's new Data Analyst in R path. The Dataquest subscription gives you access to all paths on their platform so you can learn R or Python (or both!).

Statistics and Data Science MicroMasters — MIT @ edX

The inclusion of probability and statistics courses makes this series from MIT a well-rounded curriculum for understanding data intuitively. This MicroMaster's from MIT dedicates more time towards statistical content than the UC San Diego MicroMaster's mentioned earlier in the list.

Due to its advanced nature, you should have experience with single and multivariate calculus and Python programming. There isn't any introduction to Python or R like in some of the other courses in this list, so before starting the ML portion, they recommend taking Introduction to Computer Science and Programming Using Python to get familiar with Python. If you'd rather utilize an on-demand interactive platform to learn Python, check out Codecademy's Python track.

Price – Free or $1,350 for certificate and graded materials
Provider – University of Michigan

Courses:

1. Probability - The Science of Uncertainty and Data
2. Data Analysis in Social Science—Assessing Your Knowledge
3. Fundamentals of Statistics
4. Machine Learning with Python: From Linear Models to Deep Learning
5. Capstone Exam in Statistics and Data Science

The ML course has several interesting projects you'll work on, and at the end of the whole series, you'll focus on one exam to wrap everything up.

CS109 Data Science — Harvard

With a great mix of theory and application, this course from Harvard is one of the best for getting started as a beginner. It’s not on an interactive platform, like Coursera or edX, and doesn’t offer any certification, but it’s free and definitely worth your time.

Curriculum:

• Web Scraping, Regular Expressions, Data Reshaping, Data Cleanup, Pandas
• Exploratory Data Analysis
• Pandas, SQL, and the Grammar of Data
• Statistical Models
• Storytelling and Effective Communication
• Bias and Regression
• Classification, kNN, Cross-Validation, Dimensionality Reduction, PCA, MDS
• SVM, Evaluation, Decision Trees and Random Forests, Ensemble Methods, Best Practices
• Recommendations, MapReduce, Spark
• Bayes Theorem, Bayesian Methods, Text Data
• Clustering
• Effective Presentations
• Experimental Design
• Deep Networks
• Building Data Science

Python is used in this course, and many lectures go through the intricacies of the various data science libraries to work through real-world, exciting problems. This is one of the only data science courses that actually touches on every part of the data science process.

Python for Data Science and Machine Learning Bootcamp — Udemy

Also available using R.

A very reasonably priced course for the value. The instructor does an outstanding job explaining the Python, visualization, and statistical learning concepts needed for all data science projects. A huge benefit to this course over other Udemy courses is the assignments. Throughout the course, you’ll break away and work on Jupyter Notebook workbooks to solidify your understanding; then, the instructor follows up with a solutions video to thoroughly explain each part.

Curriculum:

• Python Crash Course
• Python for Data Analysis - Numpy, Pandas
• Python for Data Visualization - Matplotlib, Seaborn, Plotly, Cufflinks, Geographic plotting
• Data Capstone Project
• Machine learning - Regression, kNN, Trees and Forests, SVM, K-Means, PCA
• Recommender Systems
• Natural Language Processing
• Big Data and Spark
• Neural Nets and Deep Learning

This course focuses more on the applied side, and one thing missing is a section on statistics. If you plan on taking this course, it would be a good idea to pair it with a separate statistics and probability course as well.

An honorary mention goes out to another Udemy course: Data Science A-Z. I do like Data Science A-Z quite a bit due to its complete coverage. Still, since it uses other tools outside of the Python/R ecosystem, I don’t think it fits the criteria as well as Python for Data Science and Machine Learning Bootcamp.

Other top data science courses for specific skills

Deep Learning Specialization — Coursera
Created by Andrew Ng, maker of the famous Stanford Machine Learning course, this is one of the highest-rated data science courses on the internet. This course series is for those interested in understanding and working with neural networks in Python.

Complete SQL Mastery — CodeWithMosh
A fantastic beginner to advanced SQL course. Check out my picks for the best SQL courses for more options and reviews.

Computational Thinking using Python XSeries — edX
Although this series only runs once every several months, if you’re new to Computer Science and Python, this is a great series to jump into if you get the chance. I found the lecturers passionate about what they teach, making it a pleasant experience to take the courses.

Mathematics for Machine Learning — Coursera
This is one of the most highly rated courses dedicated to the specific mathematics used in ML. Take this course if you’re uncomfortable with the linear algebra and calculus required for machine learning, and you’ll save some time over other, more generic math courses.

Bayesian Statistics: From Concept to Data Analysis — Coursera
Bayesian, instead of Frequentist, statistics is an important subject to learn for data science. Many of us learned Frequentist statistics in college without even knowing it, and this course does a great job comparing and contrasting the two to make it easier to understand the Bayesian approach to data analysis.

Spark and Python for Big Data with PySpark — Udemy
From the same instructor as the Python for Data Science and Machine Learning Bootcamp in the list above, this course teaches you how to leverage Spark and Python to perform data analysis and machine learning on an AWS cluster. The instructor makes this course fun and engaging by giving you mock consulting projects to work on, then going through a complete walkthrough of the solution.

Learning Guide

How to actually learn data science

When joining any of these courses, you should make the same commitment to learning as you would to a college course. One goal for learning data science online is to maximize mental discomfort. It’s easy to get caught in the habit of signing in to watch a few videos and feel like you’re learning, but you’re not really learning much unless it hurts your brain.

Vik Paruchuri (from Dataquest) produced this helpful video on how to approach learning data science effectively:

Essentially, it comes down to doing what you’re learning, i.e., when you take a course and learn a skill, apply it to a real project immediately. Working through real-world projects, you are genuinely interested in helps solidify your understanding and proves you know what you’re doing.

One of the most uncomfortable things about learning data science online is that you never really know when you’ve learned enough. Unlike in a formal school environment, when learning online, you don’t have many good barometers for success, like passing or failing tests or entire courses. Projects help remediate this by first showing you what you don’t know and then serving as a record of knowledge when it’s done.

Overall, the project should be the main focus and courses and books should supplement that.

When I first started learning data science and machine learning, I began (as a lot do) by trying to predict stocks. I found courses, books, and papers that taught the things I wanted to know, and then I applied them to my project as I was learning. I learned so much in such a short period of time that it seems like an improbable feat if laid out as a curriculum.

It turned out to be extremely powerful working on something I was passionate about. It was easy to work hard and learn nonstop because predicting the market was something I really wanted to accomplish.

Essential knowledge and skills

All data scientists must possess a base skill set and level of knowledge, regardless of what industry they’re in. For hard skills, you need not only to be proficient with the mathematics of data science but also the skills and intuition to understand data.

The Mathematics you should be comfortable with:

• Algebra
• Statistics (Frequentist and Bayesian)
• Probability
• Linear Algebra
• Basic calculus
• Optimization

Furthermore, these are the basic programming skills you should be comfortable with:

• Python or R,
• SQL
• Extracting data from various sources, like SQL databases, JSON, CSV, XML, and text files
• Cleaning and transforming unstructured, messy data
• Effective Data visualization
• Machine learning – Regression, Clustering, kNN, SVM, Trees and Forests, Ensembles, Naive Bayes

Lastly, it’s not all about the hard skills; many critical soft skills aren’t taught in courses. These are:

• Curiosity and creativity
• Communication skills – speaking and presenting in front of groups and explaining complex topics to non-technical team members.
• Problem-solving – coming up with analytical solutions for business problems.

Python vs. R

After going through the list, you might have noticed that each course is dedicated to one language: Python or R. So which one should you learn?

Short answer: just learn Python, or learn both.

Python is an incredibly versatile language with huge support in data science, machine learning, and statistics. You can also do things like build web apps, automate tasks, scrape the web, create GUIs, build a blockchain, and create games.

Because Python can do so many things, I think it should be your chosen language. Ultimately, it doesn’t matter that much which language you choose for data science since you’ll find many jobs looking for either. So why not pick the language that can do almost anything?

However, learning R is also very useful in the long run since many statistics/ML textbooks use R for examples and exercises. In fact, both books I mentioned at the beginning use R, and unless someone translates everything to Python and posts it to Github, you won’t get the full benefit of the book. Once you learn Python, you’ll be able to learn R pretty easily.

Check out this StackExchange answer for a great breakdown of how the two languages differ in machine learning.

Are certificates worth it?

One big difference between Udemy and other platforms—like edX, Coursera, and Metis—is that the latter platforms offer certificates upon completion and are usually taught by university instructors.

Some certificates, like those from edX and Metis, even carry continuing education credits. Other than that, many real benefits, like accessing graded homework and tests, are only accessible if you upgrade. If you need to stay motivated to complete the entire course, committing to a certificate also puts money on the line, making you less likely to quit. There’s definitely personal value in certificates, but unfortunately, not many employers value them that much.

Coursera and edX vs. Udemy

Udemy does not currently have a way to offer certificates, so I generally find Udemy courses to be good for more applied learning material. In contrast, Coursera and edX are usually better for theory and foundational material.

Whenever I’m looking for a course about a specific tool, whether Spark, Hadoop, Postgres, or Flask web apps, I search Udemy first since the courses favor an actionable, applied approach. Conversely, when I need an intuitive understanding of a subject, like NLP, Deep Learning, or Bayesian Statistics, I’ll search edX and Coursera first.

Wrapping Up

Data science is a vast, interesting, and rewarding field to study and be a part of. You’ll need many skills, a wide range of knowledge, and a passion for data to become an effective data scientist that companies want to hire, and it’ll take longer than the hyped-up YouTube videos claim.

If you’re more interested in the machine learning and AI side of data science, check out my articles on machine learning courses and AI courses as supplements to this article.

If you have any questions or suggestions, feel free to leave them in the comments below.

Thanks for reading, and have fun learning!