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Open Source Society University

📊 Path to a free self-taught education in Data Science!

Open Source Society University - Data Science

Contents

About

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

Python for Everybody

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, Part I

Algorithms, Part II

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.

Topic Progression Graph

Track your progress

  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.

Code of Conduct

OSSU's code of conduct.

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?

Subscribe to our newsletter.

You can also interact through GitHub issues.

Add Open Source Society University to your Linkedin and Facebook profile!

Team

About

📊 Path to a free self-taught education in Data Science!

Resources

Readme
License
Activity

Stars

1 star

Watchers

0 watching

Forks

0 forks
Report repository

Releases

Packages

Contributors

, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
BranchesTags
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Open Source Society logo
Open Source Society University

📊 Path to a free self-taught education in Data Science!


Open Source Society University - Data Science
Contents
About
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
Python for Everybody
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, Part I
Algorithms, Part II
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.
Topic Progression Graph
Track your progress
  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.
Code of Conduct
OSSU's code of conduct.
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?
Subscribe to our newsletter.
You can also interact through GitHub issues.
Add Open Source Society University to your Linkedin and Facebook profile!
Team
About
📊 Path to a free self-taught education in Data Science!
Resources
Readme
License
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Open Source Society logo
Open Source Society University

📊 Path to a free self-taught education in Data Science!


Open Source Society University - Data Science
Contents
About
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
Python for Everybody
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, Part I
Algorithms, Part II
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.
Topic Progression Graph
Track your progress
  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.
Code of Conduct
OSSU's code of conduct.
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?
Subscribe to our newsletter.
You can also interact through GitHub issues.
Add Open Source Society University to your Linkedin and Facebook profile!
Team
About
📊 Path to a free self-taught education in Data Science!
Resources
Readme
License
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Open Source Society logo
Open Source Society University

📊 Path to a free self-taught education in Data Science!


Open Source Society University - Data Science
Contents
About
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
Python for Everybody
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, Part I
Algorithms, Part II
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.
Topic Progression Graph
Track your progress
  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.
Code of Conduct
OSSU's code of conduct.
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?
Subscribe to our newsletter.
You can also interact through GitHub issues.
Add Open Source Society University to your Linkedin and Facebook profile!
Team
About
📊 Path to a free self-taught education in Data Science!
Resources
Readme
License
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
BranchesTags
Repository files navigation
Open Source Society logo
Open Source Society University

📊 Path to a free self-taught education in Data Science!


Open Source Society University - Data Science
Contents
About
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
Python for Everybody
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, Part I
Algorithms, Part II
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.
Topic Progression Graph
Track your progress
  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.
Code of Conduct
OSSU's code of conduct.
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?
Subscribe to our newsletter.
You can also interact through GitHub issues.
Add Open Source Society University to your Linkedin and Facebook profile!
Team
About
📊 Path to a free self-taught education in Data Science!
Resources
Readme
License
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
BranchesTags
Repository files navigation
Open Source Society logo
Open Source Society University

📊 Path to a free self-taught education in Data Science!


Open Source Society University - Data Science
Contents
About
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
Python for Everybody
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, Part I
Algorithms, Part II
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.
Topic Progression Graph
Track your progress
  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.
Code of Conduct
OSSU's code of conduct.
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?
Subscribe to our newsletter.
You can also interact through GitHub issues.
Add Open Source Society University to your Linkedin and Facebook profile!
Team
About
📊 Path to a free self-taught education in Data Science!
Resources
Readme
License
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Open Source Society logo
Open Source Society University

📊 Path to a free self-taught education in Data Science!


Open Source Society University - Data Science
Contents
About
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
Python for Everybody
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, Part I
Algorithms, Part II
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.
Topic Progression Graph
Track your progress
  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.
Code of Conduct
OSSU's code of conduct.
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?
Subscribe to our newsletter.
You can also interact through GitHub issues.
Add Open Source Society University to your Linkedin and Facebook profile!
Team
About
📊 Path to a free self-taught education in Data Science!
Resources
Readme
License
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

Skip to content
BranchesTags
Repository files navigation
Open Source Society logo
Open Source Society University

📊 Path to a free self-taught education in Data Science!


Open Source Society University - Data Science
Contents
About
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
Python for Everybody
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, Part I
Algorithms, Part II
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.
Topic Progression Graph
Track your progress
  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.
Code of Conduct
OSSU's code of conduct.
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?
Subscribe to our newsletter.
You can also interact through GitHub issues.
Add Open Source Society University to your Linkedin and Facebook profile!
Team
About
📊 Path to a free self-taught education in Data Science!
Resources
Readme
License
Activity
Stars
1 star
Watchers
0 watching
Forks
0 forks
Report repository
Releases
Packages
Contributors