Contents

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

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.

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

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

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

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

Introduction to programming

Introduction to Computational Thinking and Data Science

Bonus From the 🎓Computer Science Curriculum 😋

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

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

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

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

Bonus From 🎓Computer Science Curriculum 😋

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

Bonus From 🔬BioInformatics Curriculum 😋

Code	Course	Duration	Effort
COMP 2312	Databases	10 Weeks	8-12 Hours/Week

Single Variable Calculus

Calculus 1A: Differentiation

Calculus 1B: Integration

Calculus 1C: Coordinate Systems & Infinite Series

Bonus From The 🧮Maths Curriculum 😋

Courses	Duration	Effort	Prerequisites
Multivariable Calculus	12 weeks	6 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

Bonus From 🔬BioInformatics Curriculum 😋

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

Statistics & Probability

Introduction to Probability

Statistical Reasoning| - weeks | - hours/week

Intro to Descriptive Statistics

Intro to Inferential Statistics

Introduction to Statistics: Probability| 5 weeks | - hours/week

Introduction to Statistics: Inference| 5 weeks | - hours/week

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

Bonus From The 🧮Maths Curriculum 😋

Probability & Statistics

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

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

Bonus From The 🧮Maths Curriculum 😋

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

Bonus From 🔬BioInformatics Curriculum 😋

Code	Course	Duration	Effort
MATH 1315	Introduction to Probability and Data (with R)	5 Weeks	6 Hours/Week
MATH 2314	Inferential 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

Machine Learning/Data Mining

Machine Learning

Intro to Machine Learning

Mining Massive Datasets

Process Mining

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

Bonus From 🔬BioInformatics Curriculum 😋

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

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
Game Design and Development with Unity 2020 (Specialization)	6 months	5 hours/week	programming, interactive design

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.

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.

Optional Bioinformatics Courses

1st Year

Code	Course	Duration	Effort
Py4E	Python for Everybody	10 weeks	10 hours/week
6.00.1x	Introduction to Computer Science and Programming using Python (alt)	9 weeks	15 hours/week
MATH 1311	College Algebra and Problem Solving	4 Weeks	6 Hours/Week
MATH 1312	Pre-calculus	4 Weeks	6 Hours/Week
18.01.1x	Calculus 1A: Differentiation	13 weeks	6-10 hours/week
18.01.2x	Calculus 1B: Integration	13 weeks	5-10 hours/week
MATH 1315	Introduction to Probability and Data (with R)	5 Weeks	6 Hours/Week

2nd Year

Code	Course	Duration	Effort
18.01.3x	Calculus 1C: Coordinate Systems & Infinite Series	6 weeks	5-10 hours/week
6.042J	Mathematics for Computer Science (Solutions)	13 weeks	5 hours/week
COMP 2312	Databases	10 Weeks	8-12 Hours/Week
18.06	Linear Algebra and Essence of Linear Algebra	14 weeks	12 hours/week
COMP 2313	Introduction to Linux	8 Weeks	5-7 Hours/Week
MATH 2314	Inferential Statistics (with R)	5 Weeks	6 Hours/Week

3rd Year

Code	Course	Duration	Effort
COMP 3311a	Algorithmic Thinking 1	4 Weeks	6 Hours/Week
COMP 3311b	Algorithmic Thinking 2	4 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
MATH 3313	Differential Equations	7 Weeks	8-10 Hours/Week

4th Year

Code	Course	Duration	Effort
COMP 4311	Data Science	13 Week	10 Hours/Week

Extra Year

Code	Course	Duration	Effort
COMP 5311	Introduction to Machine Learning	10 Weeks	6 Hours/Week
COMP 5312	Deep Learning	8 Weeks	6 Hours/Week
Extension	Genomic Data Science Specialization	32 Week	6 Hours/Week

We also have labels to help you have more control through the process. The meaning of each of these labels is:

• Main Curriculum: cards with that label represent courses that are listed in our curriculum.
• Extra Courses: cards with that label represent courses that was added by the student.
• Doing: cards with that label represent courses the student is current doing.
• Done: cards with that label represent courses finished by the student. Those cards should also have the link for at least one project/article built with the knowledge acquired in such course.
• Section: cards with that label represent the section that we have in our curriculum. Those cards with the Section label are only to help the organization of the Done column. You should put the Course's cards below its respective Section's card.
• Extra Sections: cards with that label represent sections that was added by the student.

The intention of this board is to provide 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. You can change the status of your board to be public or private.

Should I take all courses?

Yes! The intention is to conclude all the courses listed here! Also we highly encourage you to complete more by reading papers and attending research projects after your coursework is done.

Which programming languages should I use?

List of skills:

• C/C++
• Unix System
• Python/Perl
• R
• Algorithms

These skills mentioned above are the very essential tool set that bioinformatician and computational biologist depends on.

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.

Bonus From Maths Curriculum

Curriculum

The curriculum is separated into two parts:

• Core Mathematics
  • Introduction to Mathematical Thinking
  • Calculus
  • Introduction to Differential Equations
  • Linear Algebra
  • Probability & Statistics
  • Introduction to Analysis
  • Introduction to Abstract Algebra
• Advanced Topics
  • Differential Equations
  • Mathematical Logic
  • Probability and Statistics
  • Mathematical Analysis
  • Abstract Algebra

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	-

Probability and Statistics

Combinatorics, probability, statistics, game theory, applied stats

Mathematical Analysis

Real analysis, numerical analysis, complex analysis, optimization theory

Abstract Algebra

Abstract algebra, category theory, algebraic geometry and topology