Today, a friend asked me how to study computer science well - a popular question that many others wonder too. Thus, I decided to write an all-in-one note to explained detailed about learning computer science and data science as well. I’m not excellent in computer science at all, and this note is only sharing some experiences I have.

Before you start

Knowing where you are is important. You need to understand how much cs knowledge you have. This journey is long and exhausted but I can ensure it’ll be fun as well, if you really like those stuff.

Prepare some things:

  • A notebook and a pen to note and visualize important things while you learn such as how HTTP requests are sent and received, how CPU and RAM works, and so on. Visualization always helps you understand things faster and easier. Examples: Some things I drew when learning about CPU and RAM

note1

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  • A laptop/computer with Linux/OSX operation system. Yup, you can use Window if you like, but I prefer Linux to learn programming.

  • A lot of time

  • And last but not least, a superior unbreakable persistent mind

There are 3 parts of this note:

  • Learning Maths

  • Learning Computer Science

  • Learning Data Science

Maths is basic stuff that support everything else, so I put it into an independent part.

All 3 parts will only contains materials which I personally like and find them useful the most.

Bold courses are must-taken or you should be familiar with the outcomes of those courses (so you don’t have to re-take it).
Other courses are useful and good to learn.

Learning Path

For whom want to learn programming applied in non-cs area (like business, finance, health care, …) this is learning path I think able to help you learn effectively.

  1. Read 2 starter books
  2. Do maths courses Math_01 -> Math_02 -> Math_03 or make sure you understand all the conceps in each course.
  3. Do core courses in Computer Science: 1 -> 2 -> 3 -> 8 (in course 8 only need Lecture 1 to 8)
  4. Learn Python: Py_2 -> Py_5 (do all projects)
  5. Learn DS and Algorithm: Al_01 -> Al_02 -> Al_05
  6. Do real projects

To get some certifications, some courses below are recommend:

  1. Algorithms: https://www.coursera.org/specializations/algorithms Standford Course - Intermediate Level You should learn basic algorithms before taking this one Example: Al_01 is good start.
  2. C Programming: https://www.edx.org/professional-certificate/dartmouth-imtx-c-programming-with-linux Dartmouth Course - Intermediate Level At least you should read the book C_1 in C sections below before take this course. Self-pace and $308 fee
  3. C++ Programming: Series 3 courses from Microsoft
    Intro C++ https://www.edx.org/course/introduction-to-c-5
    Intermediate C++ https://www.edx.org/course/intermediate-c-3
    Advanced C++ https://www.edx.org/course/advanced-c-3
  4. Python: https://www.coursera.org/specializations/data-science-python Python for data science series from University of Michigan

Mathematics

The courses below should be taken in order.

No. Course Link What you expected to learn
Math_01 Single Variable Calculus https://ocw.mit.edu/courses/mathematics/18-01sc-single-variable-calculus-fall-2010/ Limit, Derivatives, Chain Rules, Approximation, Mean Value Theorem, Riemann Sums, The Fundamental Theorem of Calculus, Integrals and Application, L’Hospital’s Rule, Infinite Series, Taylor’s Series
Math_02 Multivariable Calculus https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/ Vectors and operators on vectors, Determinants and Matrices (Basics), Partial Derivatives, Second Derivative Test, Total Differentials and the Chain Rule, Gradient, Lagrange Multipliers, Double Integrals, Vector Fields, Curl, Green’s Theorem, Triple Integrals, Flux, Stokes’ Theorem
Math_03 Math 54 Linear Algebra and Differential Equations https://math.berkeley.edu/~apaulin/54_002(Spring2018).html Vectors Spaces and Linear transformations, subspaces, kernels, range, eigenvalues and eigenvectors, The Gram-Schmidt Process, Least-Squares Problems, Linear Systems of Differential Equations, Fourier Series
Math_04 Math 55 - Discrete Mathematics (useful for computer science. If you learn cs70 below, you don’t really need this) https://math.berkeley.edu/~williams/55.html Propositional logic and equivalences, Sets and set operations, Functions, sequences, cardinality, Solving congruences, Mathematical induction, recursive, Counting, Permutations and combinations, Graphs and graph models, Graph isomorphism

Computer Science

Before studying courses

You need to read a few books to understand the big pictures of computer science. And read those books in the order too.

  1. Code: The Hidden Language of Computer Hardware and Software
    A great book that provides you the first sense of computer science, what is binary, how machine understand something like binary and more.
    I like how authors respresent physics mechanism of computer here, so if you are not so good at physics, this book is even better for you. This book is only 400 pages long, easy to read, therefore I highly recommend it as your first read when start learning cs.

  2. Structure and Interpretation of Computer Programs
    A legendary book, SICP is kind of book that is recommended from everywhere in cs world. It covers a lot of programming aspects from functional programming, abstraction, instruction processor to compilers. This might be quite hard for complete beginner and because this book is used Scheme programming language , it’s pretty hard to read as well.

Jump into some courses

Disclaimer: There are many things have not covered in this path yet (after complete CS61 series). So if you want to discover other courses about security, networks, machine learning, looking for it on Internet or search in this awesome guide https://hkn.eecs.berkeley.edu/courseguides

1. CS50’s Introduction to Computer Science - Harvard

Link: https://www.edx.org/course/cs50s-introduction-computer-science-harvardx-cs50x

Arguably the best intro course for cs I’ve ever learnt.

What you can learn from it:

  • basic understanding of computer science and programming
  • basic algorithms and solving problem thinking
  • some concepts like abstraction, data structures, encapsulation, web development
  • learn Python, C, Sql, Javascript

2. CS 61A Structure and Interpretation of Computer Programs - Berkeley

Link: https://inst.eecs.berkeley.edu/~cs61a/sp15/

A truly great course which makes me extremely eager to learn everything from Berkeley. However, to really enjoyed it as well as get useful knowledge, you need to put a quite large amount of effort, should definitely commit to do all the homeworks, assignments, and take all the exams. Might take you few months to actually get things done.

What you can learn from it:

  • Python and its implementation
  • Higher-Order Functions, Recursion, Environment diagrams,
  • Data Abstraction
  • Scheme, Exceptions, Iterators, Streams, SQL

3. CS 61B Data Structures - Berkeley

Videos: http://www.infocobuild.com/education/audio-video-courses/computer-science/cs61b-spring2015-berkeley.html
Courses: http://datastructur.es/sp15/

This one is quite hard for me because I don’t like learning Java much. But I think it’s useful if you want to learn data structure.

What you can learn from it:

  • Basic Java
  • Some data structures like Lists, Abstract Classes, Trees, BST, Graphs
  • Some algorithms like Sorting and Algorithmic Bounds

4. CS 70 Discrete Mathematics and Probability Theory - Berkeley

Videos: http://www.infocobuild.com/education/audio-video-courses/computer-science/CS70-Spring2015-Berkeley/lecture-01.html
Courses: https://inst.eecs.berkeley.edu/~cs70/sp15/

I’ve not done this course, discrete maths is cool and probability is interesting so I’ll finish this course soon.

What you can learn from it:

  • Induction and Recursion
  • Graphs, Eulerian Tour
  • Modular Arithmetic
  • Polynomials, Secret Sharing, Erasure Codes
  • Probability: Counting, Sample Spaces, Events, Independence, Conditional Probability
  • Inference

5. CS 61C Great Ideas in Computer Architecture (Machine Structures) - Berkeley

Videos: http://www.infocobuild.com/education/audio-video-courses/computer-science/cs61c-spring2015-berkeley.html
Courses: http://inst.eecs.berkeley.edu/~cs61c/sp15/

My favorite one, because you will learn a lot about how machine works under the hood. Not dig too deep but quite good.

What you can learn from it:

  • C programming
  • Assembly Language, MIPS Intro

6. CS 162 Operating Systems and Systems Programming - Berkeley

Videos: http://www.infocobuild.com/education/audio-video-courses/computer-science/cs162-spring2015-berkeley.html
Courses: https://inst.eecs.berkeley.edu/~cs162/sp15/

A very tough course that I’ve finished yet. More details about operating system than cs61c.

What you can learn from it:

  • Processes, Fork, I/O, Files
  • Thread, Scheduling, Address Translation, Caching
  • Performance, StorageDevices, Queueing theory
  • Distributed Systems, TCP/IP

7. CS164 Programming Languages and Compilers - Berkeley

Videos: http://www.infocobuild.com/education/audio-video-courses/computer-science/cs164-spring2012-berkeley.html
Courses: https://sites.google.com/a/bodik.org/cs164/

I have not learned this course, but knowing about programming languages and compilers surely useful for being software engineer.

What you can learn from it:

  • Unit Calculator
  • Scoping and desugaring
  • Coroutines and Lazy Iterators
  • Regular expressions
  • CYK and Earley Parsers

8. CS169 Software Engineering - Berkeley

Videos: http://www.infocobuild.com/education/audio-video-courses/computer-science/cs169-spring2015-berkeley.html
Courses: https://sites.google.com/site/ucbcs169fa15/lectures

I have not learned this course, just read the syllabus and think this’s practical and useful.

What you can learn from it:

  • Unit Testing
  • Git
  • UML
  • Software processes
  • MVC, Web development

Strengthen your knowledge

I will consider 8 courses above are core courses that you should take all of those seriously. After that, you now have good understanding about computer science and ready to improve yourself even more.

The first thing I want to dig into is programming language, I’m gonna cover few most popular ones like C, C++, Python.

To practice coding, you should join some popular competitive coding sites like https://codeforces.com, https://www.topcoder.com/ and practice there, quite helpful.

No. Language Books/Course Link What you expected to learn Hard Level
C_1 C Dennis M. Ritchie - The C programming language https://www.amazon.com/Programming-Language-2nd-Brian-Kernighan/dp/0131103628 Most popular C book out there, nothing much to talk, it’s great, I learned basic concepts about C here. It’s short and don’t take much time to read ★★☆☆☆
C_2 C Practical Programming in C https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-087-practical-programming-in-c-january-iap-2010/assignments/ A course about C from MIT, not much teaching material but good practice problems with solutions ★★★☆☆
C++_1 C++ Learn C++ https://www.learncpp.com/ Great and detailed tutorial about C++ ★★☆☆☆
C++_2 C++ Accelerated C++ https://www.amazon.com/Accelerated-C-Practical-Programming-Example/dp/020170353X Practices and examples ★★★☆☆
C++_3 C++ The C++ Programming Language https://www.amazon.com/C-Programming-Language-4th/dp/0321563840/ Detailed book from author of C++ itself, well-explained ★★★☆☆
Py_1 Python Official Docs https://docs.python.org/3/tutorial/index.html This’s where I’ve started learning Python, detailed, up-to-date ★★★☆☆
Py_2 Python Learn python the hard way https://learnpythonthehardway.org/python3/ Simple intro to python ★☆☆☆☆
Py_3 Python Real Python https://realpython.com/ Detailed examples for you to practice ★★☆☆☆
Py_4 Python Full stack Python https://www.fullstackpython.com/ Help you learn what need to build Python app ★★★★☆
Py_5 Python Automate the Boring Stuff with Python https://automatetheboringstuff.com/ Learn build Python cool stuff ★★☆☆☆

Secondly, we need to learn more about data structure and algorithm

No. Books/Course Link What you expected to learn
Al_01 Introduction to Algorithms https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-006-introduction-to-algorithms-fall-2011/ Basic about algorithms from MIT
Al_02 Design and Analysis of Algorithms https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-046j-design-and-analysis-of-algorithms-spring-2015/index.htm Part 2 from the MIT series, more advanced algorithms: divide and conquer, dynamic programming, complexity and so on
Al_03 Advanced Algorithms https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-854j-advanced-algorithms-fall-2008/ Part 3 from MIT series with advanced algorithms
Al_04 Data Structures and Algorithms - Richard Buckland https://www.youtube.com/playlist?list=PLE621E25B3BF8B9D1 Basic data structures
Al_05 CS 186: Introduction to Database Systems http://www.infocobuild.com/education/audio-video-courses/computer-science/cs186-spring2015-berkeley.html Database management system, SQL and so on

Data Science

Probability and Statistics

1. Probability and Statistics - Stanford Online Lagunita

Link: https://lagunita.stanford.edu/courses/course-v1:OLI+ProbStat+Open_Jan2017/about

2. Probabilistic Systems Analysis and Applied Probability

Link: https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-041sc-probabilistic-systems-analysis-and-applied-probability-fall-2013/
This course and the standford one can be taken seperately (you can choose one of them, it’s ok too). But for me, I take both of those and study at the same time. The MIT course is more engineering and the Standford course is more statistics.