There is a lot of free and useful information online, especially in the form of MOOCs (massive open online courses), but sometimes the sheer amount can feel overwhelming. This is a small biased sample of the knowledge that Coursera and edX offered between 2012 and 2015, knowledge that greatly expanded by the end of the decade. All of them are courses I have taken from beginning to end and earned a certificate of accomplishment, so my opinion is based on the whole course, not just the first few videos. To keep it simple, I have ordered all the courses from most “worth it” to least, but they are separated into two lists, as a lot of the them were specific to my major, computer science. There is no line within each list separating the good from the bad, so you can decided where to draw the line depending on how much more you weigh the following aspects:

  • The opportunity cost to the dozens of hours required for a course that can be spent working or studying. If that cost is high to you, you should be more picky.
  • The human capital benefit. If you are young, the knowledge can compound for longer and offer great returns on time, so you might want to take more courses.

First, the list of general courses. I highly recommend everyone take the first ten courses as their knowledge has stayed with me even five years after. Conversely, I anti-recommend taking any course in the lower half of either list. Although some of the lower-ranked courses are there because I can only vaguely recall them, that itself is an indicator that they haven’t stood the test of time.

Course Blurb
A Brief History of Humankind This course became the decade-best-seller Sapiens. Go read the book!
The Modern World: Global History since 1760 Long, but extremely insightful.
Introduction to Genetics and Evolution Natural selection is easily misunderstood, and this is a good fix to that.
Introduction to Psychology as a Science The brain is fascinating, and if you have one, you should know how it works.
Climate Literacy: Navigating Climate Change Conversations Take this and have an informed opinion climate change policy.
Social Psychology Contains fascinating studies like Asch’s conformity and Milgram’s obedience experiment.
A Look at Nuclear Science and Technology Nuclear is unfairly maligned, even though it is the future of green energy.
Game Theory The prisoner’s dilemma will show you why groups and societies can be stuck into sub-optimal outcomes.
Nutrition and Physical Activity for Health If you have a body you need to know how to take care of it.
Useful Genetics CRISPR/GMOs will only be more present in the future.
Making Better Group Decisions: Voting, Judgement Aggregation and Fair Division There are many voting systems better than the current one.
Understanding Einstein: The Special Theory of Relativity Reality is counterintuitive. Time is relative for all of us!
Galaxies and Cosmology We know more than you’d think about events from 13 billion years ago.
Dino 101: Dinosaur Paleobiology The age of the dinosaurs is reality manifestly more fantastical than fantasy.
Unpredictable? Randomness, Chance and Free Will Free will is an illusion.
Know Thyself The self is an illusion.
Sustainability of Food Systems: A Global Life Cycle Perspective We can make enough food, but how do we get it to everyone?
From the Big Bang to Dark Energy A contemporary account of cosmology, but better to take the one above.
Confronting The Big Questions: Highlights of Modern Astronomy Same as above.
Astrobiology and the Search for Extraterrestrial Life Since we haven’t found other life, not a good use of your time.
AstroTech: The Science and Technology behind Astronomical Discovery Telescopes might be immensely useful, but not that important to learn about.
The Language of Hollywood: Storytelling, Sound, and Color Somewhat useful for complete laymen to film-making.
Nutrition, Health, and Lifestyle: Issues and Insights Not as good as the above nutrition course.
Introduction to Philosophy Better to just listen to the podcast Philosophize This!.
The Ancient Greeks Not worth the time.
Archaeology’s Dirty Little Secrets Archaelogy somehow became less interesting than before taking it.
The Science of Gastronomy Unfortunately, it had some myths like different tongue areas having different taste buds.
How Things Work Fun, but not very useful physics experiments.
Fundamentals of Music Theory Useful only for aspiring composers.
Introduction to Digital Sound Design Applicable only for people who compose with software.
Introduction to Guitar A MOOC is no replacement for an in-person guitar instructor.

The recommmendations below are for computer science majors only. I highly recommend you take the first five courses.

Course Blurb
Machine Learning This is the legendary course that got millions of people exicted about machine learning and Coursera’s first course!
Algorithms: Design and Analysis, Part 1 Big-O notation is one of the fundamentals of computer science
Data Analysis and Statistical Inference Data analysis is a fast-growing field you should know more about.
Statistical Learning Machine learning from the viewpoing of statistics, a good companion to Ng’s course.
Social Network Analysis An excellent, fun introduction to social networks!
Probabilistic Graphical Models 1: Representation Rigorous, but very useful to the current deep learning paradigm.
Cryptography I This is the heart of modern privacy and it’s useful to know at least a bit about it.
Introduction to Data Science Take Data Analysis and Statistical Inference instead.
Data Analysis Same as above.
Calculus Two: Sequences and Series Calculus is one of the few things requiring practice and a good book.
Massively Multivariable Open Online Calculus Course Same as above.
Games without Chance: Combinatorial Game Theory Game Theory is fascinating, combinatorics less so.
Introduction to Linear Models and Matrix Algebra Ueful as a prerequisite for machine learning.
Scalable Machine Learning Only useful if you (want to) work with big data.
Introduction to Big Data with Apache Spark Same as above.
Introduction to C++ Too short and high-level, lots of practice is essential for any language.
Data Science and Machine Learning Essentials Take the machine learning course instead.
Computing for Data Analysis Computational details matter much less.
Introduction to Computational Thinking and Data Science Same as above.
Text Mining and Analytics Only useful if you’re doing NLP.
Networked Life Take Social Network Analysis instead.
Discrete Inference and Learning in Artificial Vision Very outdated.
Image and video processing: From Mars to Hollywood with a stop at the hospital Same as above.
An Introduction to Interactive Programming in Python The first course I ever took, but even at 2x speed it was tediously slow.