![GitHub - Springboard banner [email protected]](/bradleygrant/penny-data-science/blob/master/GitHub%20-%20Springboard%20banner%202%402x.png){kind=link}
You wanted an email, but this is easier. The Internet already did most of the work for me.
So, without further ado,
By the way, did you know you can (and should) edit this? Get your GitHub account set up and you can edit this page as you make decisions and take courses.
Simply put, what do you want to accomplish? We should spend a little bit of time trying to figure this out.
But don't feel like you have to get EVERYTHING figured out. All paths forward lead through the same waypoints. You'll need linear algebra, probability, statistics, and Python or R programming. As long as you stay in this domain, those are the next places you need to go. So if you want to make a little bit of progress, then just pick one or two and do 'em.
If you want to pick up degree work again next semester or in the summer, then we'll both feel a lot better if you've already done some pre-study so you don't feel overwhelmed.
Options include:
- Linear algebra
- Probability and/or statistics
- Real analysis
Well, you'll need to get into a grad school.
Here are a few grad school options that should appeal to you: (TODO)
- Colorado State University -- Master of Applied Statistics (here, and online, and awesome!) | Program Page | Github Page (TODO)
- Georgia Tech -- Online Master of Science Analysis (cheap, and online via edX! and awesome!) | Program Page | edX Page with more info | Subreddit
- Michigan State
- Northwestern
- probably some other ones
In order to get in to a grad school, you'll need to take (at a minimum) the following courses:
- Undergrad linear algebra (required by CSU and Georgia Tech)
- Undergrad statistics (required by CSU and Georgia Tech)
- Python programming (required by Georgia Tech who specifically recommends CS1301)
Then what you actually need is a data science bootcamp. (TODO)
But before that, you should take a data science bootcamp prep course. (TODO)
That's the entire point of the Open Source Society University! OSSU is a path to a free, self-taught education in Computer Science, Data Science, or a couple of other tracks. All you have to do is take the free resources and courses shown in this section down here. Sounds easy, right? Well.... yes, but you do have to take them.
You've come a long way, baby. Here's what you already have DONE and out of the way:
- Pre-calculus
- Calculus 1
- Calculus 2
- Calculus 3 / multivariate
- Differential Equations
- Discrete Math
- Computer Programming (Java)
- Computer Science 1: Control Flow and Objects
- Computer Science 2: Data Structures
- abunchofotherwisepointlessphysicsprerequisites erm I mean
- Physics 1
- Physics 2
Things you can take right now!
| Courses | Duration | Start Date |
|---|---|---|
| LAFF: Linear Algebra - Foundations to Frontiers | 15 weeks | August 1, 2019 (now) |
| MIT 600.1x: Introduction to Computer Science and Programming Using Python | 9 weeks | August 28, 2019 |
Things you can take in future months without any further study.
| Courses | Duration | Start Date |
|---|---|---|
| MIT 643.1x: Probability: The Science and Uncertainty of Data | 16 weeks | January 27, 2019 |
Things you can take in future sessions if you take some of these other classes now.
| Courses | Duration | Start Date |
|---|---|---|
| MIT 600.2x: Introduction to Computational Thinking and Data Science | 9 weeks | October 16, 2019 |
📊 Path to a free self-taught education in Data Science!
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.
Are you ready to get started?
- Linear Algebra
- Single Variable Calculus
- Multivariable Calculus
- Python
- Probability and Statistics
- Introduction to Data Science
- Machine Learning
- Project
- Convex Optimization
- Data Wrangling
- Big Data
- Database
- Deep Learning
- Natural Language Processing
- Capstone Project
| 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 |
| Done? | 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 |
| Done? | 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 | 9 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 |
| Courses | Duration | Effort |
|---|---|---|
| Probability: The Science and Uncertainty of Data | 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 |
| 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 |
| 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
| Courses | Duration | Effort |
|---|---|---|
| Convex Optimization | 9 weeks | 10 hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Data Wrangling with MongoDB | 8 weeks | 10 hours/week |
| 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 |
| Courses | Duration | Effort |
|---|---|---|
| Deep Learning for Natural Language Processing | - weeks | - hours/week |
| Courses | Duration | Effort |
|---|---|---|
| Deep Learning | 12 weeks | 8-12 hours/week |
- Participate in Kaggle competition
- List down other ideas
This guide was developed to be consumed in a linear approach. What does this mean? That you should complete one course at a time.
The courses are already in the order that you should complete them. Just start in the Linear Algebra section and after finishing the first course, start the next one.
If the course isn't open, do it anyway with the resources from the previous class.
Yes! The intention is to conclude all the courses listed here!
Python and R are heavily used in Data Science community and our courses teach you both, but...
The important thing for each course is to internalize the core concepts and to be able to use them with whatever tool (programming language) that you wish.
The only things that you need to know are how to use Git and GitHub. Here are some resources to learn about them:
Note: Just pick one of the courses below to learn the basics. You will learn a lot more once you get started!