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.

Contents

• About
• Motivation & Preparation
• Prerequisites
• Curricular Guideline
• How to use this guide
• Curriculum

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.

• MIT Challenge

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.

• Learning How to Learn

Are you ready to get started?

Prerequisites

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

Curricular Guideline

Curriculum Guidelines for Undergraduate Programs in Data Science

Which programming languages should I use?

Python, SQL 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.

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.

Curriculum

Introduction to Data Science

What is 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

Introduction to Computer Science

This course will introduce you to the world of computer science. Students who have been introduced to programming, either from the courses above or through study elsewhere, should take this course for a flavor of the material to come. If you finish the course wanting more, Computer Science is likely for you!

Topics covered:

computation imperative programming basic data structures and algorithms and more

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

Introduction to programming

Introduction to Computational Thinking and Data Science

Courses	Duration	Effort	Prerequisites	Discussion
Introduction to Computer Science and Programming using Python (alternative)	9 weeks	15 hours/week	high school algebra	chat

This course has been developed by MIT and is available from three different places. We recommend you to do it from the archived version on Edx.

6.0001 Introduction to Computer Science and Programming in Python is intended for students with little or no programming experience. It aims to provide students with an understanding of the role computation can play in solving problems and to help students, regardless of their major, feel justifiably confident of their ability to write small programs that allow them to accomplish useful goals. The class uses the Python 3.5 programming language.

MITx 6.00.1x + 2T2018

Introduction to Mathematical Thinking

Most people's views of mathematics are destroyed in school by focusing on memorization and regurgitation. But mathematicians see math as an elegant way to explain the world around us. This class covers how to think like a mathematician and solve problems.

Topics covered: Mathematical mindset Number Theory

Courses	Duration	Effort	Prerequisites
Introduction to Mathematical Thinking	10 weeks	4 hours/week	none
LaTeX	1 week	30 minutes/week	none

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(Coursera)

Algorithms I: ArrayLists, LinkedLists, Stacks and Queues(Edx)

Algorithms, Part II(Coursera)

Algorithms II: Binary Trees, Heaps, SkipLists and HashMaps(Edx)

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

Algorithms IV: Pattern Matching, Dijkstra’s, MST, and Dynamic Programming Algorithms

Code	Course	Duration	Effort
COMP 3311a	Algorithmic Thinking 1	4 Weeks	6 Hours/Week
COMP 3311b	Algorithmic Thinking 2	4 Weeks	6 Hours/Week
COMP 2311	CS 2 - Object Oriented Java	6 Weeks	4-6 Hours/Week
MATH 3312	Bayesian Statistics (with R)	5 Weeks	6 Hours/Week

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

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

Code	Course	Duration	Effort
COMP 2312	Databases	10 Weeks	8-12 Hours/Week
MATH 2314	Inferential Statistics (with R)	5 Weeks	6 Hours/Week
COMP 4311	Data Science	13 Week	10 Hours/Week

Core theory

Topics covered: divide and conquer sorting and searching randomized algorithms graph search shortest paths data structures greedy algorithms minimum spanning trees dynamic programming NP-completeness and more

Courses	Duration	Effort	Prerequisites	Discussion
Graph Search, Shortest Paths, and Data Structures	4 weeks	4-8 hours/week	Divide and Conquer, Sorting and Searching, and Randomized Algorithms	chat

Core applications

Topics covered:

Agile methodology REST software specifications refactoring relational databases transaction processing data modeling neural networks supervised learning unsupervised learning OpenGL ray tracing and more

Courses	Duration	Effort	Prerequisites	Discussion
Databases: Modeling and Theory	2 weeks	10 hours/week	core programming	chat
Databases: Relational Databases and SQL	2 weeks	10 hours/week	core programming	chat
Databases: Semistructured Data	2 weeks	10 hours/week	core programming	chat
Machine Learning	11 weeks	9 hours/week	Basic coding	chat

Code	Course	Duration	Effort
COMP 5311	Introduction to Machine Learning	10 Weeks	6 Hours/Week

Single Variable Calculus

Calculus 1A: Differentiation

Calculus 1B: Integration

Calculus 1C: Coordinate Systems & Infinite Series

Courses	Duration	Effort	Prerequisites
Multivariable Calculus	12 weeks	6 hours/week	Calculus 1C

Discrete Mathematics

Discrete mathematics is the mathematics of objects and ideas. It includes topics such as combinatorics, graph theory, and logic. The topics discussed here also form the basis of the field of computer science. For mathematics majors, a discrete math course is usually also a first introduction to formal proofs.

Topics covered: Counting Grouping Classifying Logic and Reasoning

Courses	Duration	Effort	Prerequisites
Mathematics for Computer Science	14 weeks	6-8 hours/week	Calculus 1C

Introduction to Differential Equations

Differential equations describe the science of change: the route by which natural and man-made systems move from one state to another. Epidemics, population growth, and weather patterns are all modeled using differential equations. It provides us a mathematical language to describe physical, chemical, and biological systems and their evolution.

Topics covered: First-order ODEs Second-order ODEs Higher-order ODEs Laplace Transforms

Courses	Duration	Effort	Prerequisites
Differential Equations	14 weeks	12 hours/week	Calculus 1C

Linear Algebra

Topics covered:

Vector and matrix calculations Linear transformations Vector spaces Eigenvalues and Eigenvectors

Essence of Linear Algebra

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

Code	Course	Duration	Effort
MATH 1311	College Algebra and Problem Solving	4 Weeks	6 Hours/Week

Code	Course	Duration	Effort
18.06	Linear Algebra and Essence of Linear Algebra	14 weeks	12 hours/week

Multivariable Calculus

Multivariable Calculus

Python

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

Py4e Py4e Textbook / EPUB / HTML / Buy hardcopy

Statistics & Probability

Probability is the mathematics of uncertainty. Statistics is the mathematical framework for quantifying uncertainty in real-world data. These two related but distinct fields of study help us describe variation and uncertainty in the world around us. These courses make heavy use of discrete mathematics, linear algebra, and calculus, and serve as a first opportunity to apply what you've learned in the other core courses.

Topics covered:

Random variables Expectation and Variance Probability Distributions

Introduction to Probability

Statistical Reasoning

Intro to Descriptive Statistics

Intro to Inferential Statistics

Introduction to Statistics: Probability

Introduction to Statistics: Inference

Statistical Learning with Python by Stanford University on EdX or Statistical Learning With R by Stanford University on EdX

Statistical Learning with Python by Stanford University on EdX (Textbook, Textbook resources) or Statistical Learning With R by Stanford University on EdX (Textbook, Textbook resources)

Code	Course	Duration	Effort
MATH 1315	Introduction to Probability and Data (with R)	5 Weeks	6 Hours/Week

Courses	Duration	Effort	Prerequisites
Probability	14 weeks	12-16 hours/week	Multivariable Calculus, Math for Computer Science, Linear Algebra
Statistics for Applications	14 weeks	12-16 hours/week	Probability

Topics covered: Random variables Expectation and Variance Probability Distributions

Courses	Duration	Effort	Prerequisites
Probability	14 weeks	12-16 hours/week	Multivariable Calculus, Math for Computer Science, Linear Algebra
Statistics for Applications	14 weeks	12-16 hours/week	Probability

Introduction to Analysis

Analysis is the mathematics of sequences and limits. Intro to Analysis is a course that builds on the concepts of Calculus and provides a rigorous and formalized study of the foundations of Calculus. This course will use formal proofs to establish mathematical results, starting by proving the existence of real numbers and building the foundation of single-variable Calculus from scratch.

Topics covered:

Proofs Real analysis

Courses	Duration	Effort	Prerequisites
Introduction to Analysis	14 weeks	8-10 hours/week	Multivariable Calculus
Supplemental Lecture Videos	16 weeks	8-10 hours/week	Multivariable Calculus

Code	Course	Duration	Effort
MATH 2314	Inferaential Statistics (with R)	5 Weeks	6 Hours/Week
MATH 3311	Linear Regression and Modeling (with R)	4 Weeks	6 Hours/Week
MATH 3312	Bayesian Statistics (with R)	5 Weeks	6 Hours/Week

Data Science Tools & Methods

Tools for Data Science

Data Science Methodology

Data Science: Wrangling

Code	Course	Duration	Effort
COMP 2312	Databases	10 Weeks	8-12 Hours/Week
COMP 4311	Data Science	13 Week	10 Hours/Week
COMP 5312	Deep Learning	8 Weeks	6 Hours/Week
Extension	Genomic Data Science Specialization	32 Week	6 Hours/Week

Machine Learning/Data Mining

Machine Learning

Intro to Machine Learning

Mining Massive Datasets

Process Mining

Supervised Machine Learning: Regression and Classification

Advanced Learning Algorithms

Unsupervised Learning, Recommenders, Reinforcement 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

Final project

OSS University is project-focused. The assignments and exams for each course are to prepare you to use your knowledge to solve real-world problems.

After you've gotten through all of Core CS and the parts of Advanced CS relevant to you, you should think about a problem that you can solve using the knowledge you've acquired. Not only does real project work look great on a resume, but the project will also validate and consolidate your knowledge. You can create something entirely new, or you can find an existing project that needs help via websites like CodeTriage or First Timers Only.

Students who would like more guidance in creating a project may choose to use a series of project oriented courses. Here is a sample of options (many more are available, at this point you should be capable of identifying a series that is interesting and relevant to you):

Project

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

Database

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

Specializations

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.

Courses	Duration	Effort	Prerequisites
Data Mining (Specialization)	30 weeks	2-5 hours/week	machine learning
Big Data (Specialization)	30 weeks	3-5 hours/week	none
Internet of Things (Specialization)	30 weeks	1-5 hours/week	strong programming
Cloud Computing (Specialization)	30 weeks	2-6 hours/week	C++ programming
Data Science (Specialization)	43 weeks	1-6 hours/week	none
Functional Programming in Scala (Specialization)	29 weeks	4-5 hours/week	One year programming experience

Courses	School	Duration	Effort	Frequency	Prerequisites
Machine Learning	Stanford	11 weeks	5-7 hours/week	twice a month	Linear Algebra - Foundations to Frontiers
Database Management Essentials	CU	7 weeks	4-6 hours/week	twice a month	basic programming & CS knowledge
Cryptography I	Stanford	7 weeks	5 hours/week	once a month	Linear Algebra - Foundations to Frontiers & Introduction to Probability and Data

• Udacity
  • Machine Learning Nanodegree by Google
  • Data Scientist Nanodegree
• edX
  • Data Science and Engineering with Apache Spark
• Coursera
• Data Mining Specialization
• Machine Learning Specialization
• Data Science Specialization
• FutureLearn

Udacity

Machine Learning Nanodegree by Google

Course	Duration	Effort
Machine Learning Engineer Nanodegree	- weeks	10 hours/week

Data Scientist Nanodegree

Course	Duration	Effort
Data Analyst Nanodegree	- weeks	10 hours/week

edX

Data Science and Engineering with Apache Spark

Course	Duration	Effort
Data Science and Engineering with Apache Spark XSeries	- weeks	10 hours/week

Coursera

Data Mining Specialization

Course	Duration	Effort
Data Mining	- weeks	8-12 hours/week

Machine Learning Specialization

Course	Duration	Effort
Machine Learning	- weeks	8-12 hours/week

Data Science Specialization

Course	Duration	Effort
Statistics with R	- weeks	- hours/week
Data Science at Scale	17 weeks	6-8 hours/week
Data Science	- weeks	4-9 hours/week

FutureLearn

Big Data

Course	Duration	Effort
Big Data Analytics	8 weeks	- hours/week

Applications

Name	Author(s)
Architecture of a Database System	Joseph M. Hellerstein, Michael Stonebraker, James Hamilton
Readings in Database Systems (5th Edition)	Peter Bailis, Joseph M. Hellerstein, Michael Stonebraker, editors
Database Management Systems (3rd Edition)	Raghu Ramakrishnan, Johannes Gehrke
Transaction Processing: Concepts and Techniques	Jim Gray, Andreas Reuter
Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World (3rd Edition)	William Kent
The Architecture of Open Source Applications	Michael DiBernardo (editor)
An Introduction to Statistical Learning	Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
Deep Learning	Ian Goodfellow, Yoshua Bengio and Aaron Courville
Bayesian Reasoning and Machine Learning	David Barber
Language Implementation Patterns	Terence Parr
Compilers: Principles, Techniques, and Tools (2nd Edition)	Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman
Compiler Construction	Niklaus Wirth
The Mythical Man-Month	Fred Brooks, Jr.
Physically Based Rendering: From Theory To Implementation	Matt Pharr, Wenzel Jakob, and Greg Humphreys

Core programming

Topics covered: functional programming design for testing program requirements common design patterns unit testing object-oriented design static typing dynamic typing ML-family languages (via Standard ML) Lisp-family languages (via Racket) Ruby and more

Courses	Duration	Effort	Prerequisites	Discussion
Systematic Program Design	13 weeks	8-10 hours/week	none	chat: part 1 / part 2
Class-based Program Design	13 weeks	5-10 hours/week	Systematic Program Design, High School Math	chat
Programming Languages, Part A	5 weeks	4-8 hours/week	Systematic Program Design (Hear instructor)	chat
Programming Languages, Part B	3 weeks	4-8 hours/week	Programming Languages, Part A	chat
Programming Languages, Part C	3 weeks	4-8 hours/week	Programming Languages, Part B	chat
Object-Oriented Design	13 weeks	5-10 hours/week	Class Based Program Design	chat
Software Architecture	4 weeks	2-5 hours/week	Object Oriented Design	chat

Core math

Discrete math (Math for CS) is a prerequisite and closely related to the study of algorithms and data structures. Calculus both prepares students for discrete math and helps students develop mathematical maturity.

Topics covered: discrete mathematics mathematical proofs basic statistics O-notation discrete probability and more

Courses	Duration	Effort	Notes	Prerequisites	Discussion
Calculus 1A: Differentiation (alternative)	13 weeks	6-10 hours/week	The alternate covers this and the following 2 courses	high school math	chat
Calculus 1B: Integration	13 weeks	5-10 hours/week	-	Calculus 1A	chat
Calculus 1C: Coordinate Systems & Infinite Series	6 weeks	5-10 hours/week	-	Calculus 1B	chat
Mathematics for Computer Science (alternative)	13 weeks	5 hours/week	2015/2019 solutions 2010 solutions 2005 solutions.	Calculus 1C	chat

Core applications

Topics covered: Agile methodology REST software specifications refactoring relational databases transaction processing data modeling neural networks supervised learning unsupervised learning OpenGL ray tracing and more

Courses	Duration	Effort	Prerequisites	Discussion
Databases: Modeling and Theory	2 weeks	10 hours/week	core programming	chat
Databases: Relational Databases and SQL	2 weeks	10 hours/week	core programming	chat
Databases: Semistructured Data	2 weeks	10 hours/week	core programming	chat
Machine Learning	11 weeks	9 hours/week	Basic coding	chat
Computer Graphics (alternative)	6 weeks	12 hours/week	C++ or Java, linear algebra	chat
Software Engineering: Introduction	6 weeks	8-10 hours/week	Core Programming, and a sizable project	chat

Advanced Topics

Upon finishing all the core mathematics courses, students can choose to take elective courses in advanced topics of their choice. It is not necessary to take every course within a subcategory, but it is recommended to take courses relevant to the intended field of study.

To complete your study of Advanced Topics, meet both the Breadth and Depth requirements.

• Breadth Requirement: For each of the 6 Advanced Topics below, select one course to take as an elective.
• Depth Requirement: Select one Advanced Topic below and take 3 additional courses from that topic.

Mathematical Logic

Courses	Duration	Effort	Prerequisites
Introduction to Formal Logic	15 weeks	9 hours/week	-

Advanced programming

Topics covered: debugging theory and practice goal-oriented programming parallel computing object-oriented analysis and design UML large-scale software architecture and design and more

Courses	Duration	Effort	Prerequisites
Parallel Programming	4 weeks	6-8 hours/week	Scala programming
Compilers	9 weeks	6-8 hours/week	none
Introduction to Haskell	14 weeks	-	-
Learn Prolog Now! (alternative)*	12 weeks	-	-
Software Debugging	8 weeks	6 hours/week	Python, object-oriented programming
Software Testing	4 weeks	6 hours/week	Python, programming experience

(*) book by Blackburn, Bos, Striegnitz (compiled from source, redistributed under CC license)

Advanced Information Security

Courses	Duration	Effort	Prerequisites
Web Security Fundamentals	5 weeks	4-6 hours/week	understanding basic web technologies
Security Governance & Compliance	3 weeks	3 hours/week	-
Digital Forensics Concepts	3 weeks	2-3 hours/week	Core Security
Secure Software Development: Requirements, Design, and Reuse	7 weeks	1-2 hours/week	Core Programming and Core Security
Secure Software Development: Implementation	7 weeks	1-2 hours/week	Secure Software Development: Requirements, Design, and Reuse
Secure Software Development: Verification and More Specialized Topics	7 weeks	1-2 hours/week	Secure Software Development: Implementation