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ðŸ“Š Path to a free self-taught education in Data Science!

# ossu/data-science

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### Open Source Society University

ðŸ“Š Path to a free self-taught education in Data Science!

## Contents

This is a path for those of you who want to complete the Data Science undergraduate curriculum 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.

## Curricular Guideline

OSSU Data Science uses the report Curriculum Guidelines for Undergraduate Programs in Data Science as our guide for course recommendation.

## Curriculum

### Introduction to Data Science

What is Data Science

### Introduction to Computer Science

Students who already know basic programming in any language can skip this first course

Introduction to programming

Introduction to Computer Science and Programming Using Python

Introduction to Computational Thinking and Data Science

### Data Structures and Algorithms

The Algorithms courses are taught in Java. If students need to learn Java, they should take this course first

Java Programming

Algorithms I: ArrayLists, LinkedLists, Stacks and Queues

Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps

Algorithms III: AVL and 2-4 Trees, Divide and Conquer Algorithms

Algorithms IV: Pattern Matching, Dijkstraâ€™s, MST, and Dynamic Programming Algorithms

### Databases

Database Management Essentials

Data Warehouse Concepts, Design, and Data Integration

Relational Database Support for Data Warehouses

Business Intelligence Concepts, Tools, and Applications

Design and Build a Data Warehouse for Business Intelligence Implementation

MongoDB for Developers Learning Path

### Single Variable Calculus

Calculus 1A: Differentiation

Calculus 1B: Integration

Calculus 1C: Coordinate Systems & Infinite Series

### Linear Algebra

Essence of Linear Algebra

Linear Algebra

### Multivariable Calculus

Multivariable Calculus

### Statistics & Probability

Introduction to Probability

Intro to Descriptive Statistics

Intro to Inferential Statistics

### Data Science Tools & Methods

Tools for Data Science

Data Science Methodology

Data Science: Wrangling

### Machine Learning/Data Mining

Machine Learning

Intro to Machine Learning

Mining Massive Datasets

Process Mining

## How to use this guide

### Duration

It is possible to finish within about 2 years if you plan carefully and devote roughly 20 hours/week to your studies. Learners can use this spreadsheet to estimate their end date. Make a copy and input your start date and expected hours per week in the `Timeline` sheet. As you work through courses you can enter your actual course completion dates in the Curriculum Data sheet and get updated completion estimates.

### Order of the classes

Some courses can be taken in parallel, while others must be taken sequentially. All of the courses within a topic should be taken in the order listed in the curriculum. The graph below demonstrates how topics should be ordered.

1. Create an account in Trello.
2. Copy this board to your personal account. See how to copy a board here.

Now you just need to pass the cards to the `Doing` column or `Done` column as you progress in your study.

### Which programming languages should I use?

Python and R are heavily used in Data Science community and our courses teach you both. Remember, 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.

### Content Policy

You must share only files that you are allowed. Do NOT disrespect the code of conduct that you sign in the beginning of your courses.

## Prerequisites

The Data Science curriculum assumes the student has taken high school math and statistics.

## How to contribute

You can open an issue and give us your suggestions as to how we can improve this guide, or what we can do to improve the learning experience.

You can also fork this project and send a pull request to fix any mistakes that you have found.

If you want to suggest a new resource, send a pull request adding such resource to the extras section. The extras section is a place where all of us will be able to submit interesting additional articles, books, courses and specializations.

## Community

We have a Discord server! This should be your first stop to talk with other OSSU students. Why don't you introduce yourself right now?

You can also interact through GitHub issues.

## Team

ðŸ“Š Path to a free self-taught education in Data Science!

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