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.
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.
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 – 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|
|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|
|MIT OCW Multivariable Calculus||15 weeks||8 hours/week|
|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|
|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||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|
|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||9 weeks||10 hours/week|
|Data Wrangling with MongoDB||8 weeks||10 hours/week|
|Intro to Hadoop and MapReduce||4 weeks||6 hours/week|
|Deploying a Hadoop Cluster||3 weeks||6 hours/week|
|Stanford’s Database course||– weeks||8-12 hours/week|
|Deep Learning for Natural Language Processing||– weeks||– hours/week|
|Deep Learning||12 weeks||8-12 hours/week|
- 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.