This is a solid path for those of you who want to complete a Data Science course on your own time, for free, with courses from the best universities in the World.

In our curriculum, we give preference to MOOC (Massive Open Online Course) style courses because these courses were created with our style of learning in mind.

Becoming an OSS student

To officially register for this course you must create a profile in our web app.

ps: Currently, the web app is for tracking the progress of the Computer Science path, but we are working to extend this functionality for all of our courses. Thanks for the comprehension.

“How can I do this?”

Just create an account on GitHub and log in with this account in our web app.

The intention of this app is to offer for our students a way to track their progress, and also the ability to show their progress through a public page for friends, family, employers, etc.

In the “My Progress” tab, you are able to edit the status of the courses that you are taking, and also add the link of your final project for each one.

Motivation & Preparation

Here are two interesting links that can make all the difference in your journey.

The first one is a motivational video that shows a guy that went through the “MIT Challenge”, which consists of learning the entire 4-year MIT curriculum for Computer Science in 1 year.

The second link is a MOOC that will teach you learning techniques used by experts in art, music, literature, math, science, sports, and many other disciplines. These are fundamental abilities to succeed in our journey.

Are you ready to get started?


Linear Algebra

Courses Duration Effort
Linear Algebra – Foundations to Frontiers 15 weeks 8 hours/week
Applications of Linear Algebra Part 1 5 weeks 4 hours/week
Applications of Linear Algebra Part 2 4 weeks 5 hours/week

Single Variable Calculus

Courses Duration Effort
Calculus 1A: Differentiation 13 weeks 6-10 hours/week
Calculus 1B: Integration 13 weeks 5-10 hours/week
Calculus 1C: Coordinate Systems & Infinite Series 13 weeks 6-10 hours/week

Multivariable Calculus

Courses Duration Effort
MIT OCW Multivariable Calculus 15 weeks 8 hours/week


Courses Duration Effort
Introduction to Computer Science and Programming Using Python 9 weeks 15 hours/week
Introduction to Computational Thinking and Data Science 10 weeks 15 hours/week
Introduction to Python for Data Science 6 weeks 2-4 hours/week
Programming with Python for Data Science 6 weeks 3-4 hours/week

Probability and Statistics

Courses Duration Effort
Introduction to Probability 16 weeks 12 hours/week
Statistical Reasoning – weeks – hours/week
Introduction to Statistics: Descriptive Statistics 5 weeks – hours/week
Introduction to Statistics: Probability 5 weeks – hours/week
Introduction to Statistics: Inference 5 weeks – hours/week

Introduction to Data Science

Courses Duration Effort
Introduction to Data Science 8 weeks 10-12 hours/week
Data Science – CS109 from Harvard 12 weeks 5-6 hours/week
The Analytics Edge 12 weeks 10-15 hours/week

Machine Learning

Courses Duration Effort
Learning From Data (Introductory Machine Learning) [caltech] 10 weeks 10-20 hours/week
Statistical Learning – weeks 3 hours/week
Stanford’s Machine Learning Course – weeks 8-12 hours/week


Complete Kaggle’s Getting Started and Playground Competitions

Convex Optimization

Courses Duration Effort
Convex Optimization 9 weeks 10 hours/week

Data Wrangling

Courses Duration Effort
Data Wrangling with MongoDB 8 weeks 10 hours/week

Big Data

Courses Duration Effort
Intro to Hadoop and MapReduce 4 weeks 6 hours/week
Deploying a Hadoop Cluster 3 weeks 6 hours/week


Courses Duration Effort
Stanford’s Database course – weeks 8-12 hours/week

Natural Language Processing

Courses Duration Effort
Deep Learning for Natural Language Processing – weeks – hours/week

Deep Learning

Courses Duration Effort
Deep Learning 12 weeks 8-12 hours/week

Capstone Project

  • Participate in Kaggle competition
  • List down other ideas


After finishing the courses above, start your specializations on the topics that you have more interest. You can view a list of available specializations here.

keep learning