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Open Source Society University
Path to a free self-taught education in Computer Science!
Path to a free self-taught education in Computer Science!
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- You can use Ctrl + I to reindent the whole file.
- If you are on Windows or Linux, use Alt + Backspace to delete whole words.
- If you are unable to watch the videos on Edx for some reason, you can watch them on [this youtube channel](https://www.youtube.com/@systematicprogramdesign7962/playlists).
## FAQ
### This course is boring. Can I skip it?
**No.** This course may seem boring at first, but we suggest working though it. This is a great course, and it will probably change the way you think. Many students who found this course boring at first became a fan of the course by the time they completed it. Be very careful. The early parts (especially the rules about how evaluation works) plays a huge role in understanding how code works and gets executed for the rest of the course.
### Why is this course taught using BSL? Would it not make more sense to teach it in an industry standard language?
This is an intentional choice, and here's why:
1. Lisp is the lingua franca of computer scientists -- by which we mean, PhD algorithm researchers. There are some good and some merely historical reasons for this, but it's a fact of life, so if you want to read white papers you're going to want to read Lisp. BSL is a good introduction, and frankly once you get over the parenthesis hell and once you know any Lisp you know how to read them all.
2. This is the first computer science course in most people's curricula that isn't centered around teaching you how to use a language. Because the point of computer science isn't to teach you a language. Or to teach you to code. Or to teach you to be a fullstack software engineer. Computer Science is a very narrowly-applied applied math with wide-ranging practical usage. But if you strip away all the qualifying language, it's math. Which means it has certain overarching rules that are completely, totally independent of your implementation language.
This course is built in a throwaway student language, specifically so you don't focus on the language and instead focus on what you're doing with the language. We don't care about public static void main or PEP8 style. We want to see ways to structure a program in any language. So we focus not on the things that make Java unique, and not on the things that make Python unique, and instead focus on the things that make code better.
It might seem rough to learn a new language just to take this course, but BSL frees you from having to worry about style linting or runtime issues or code compartmentalization or compiling or coding environment. It's a gift. Take it. The design patterns are hard enough.
### Why there are different course versions, HTC, SPD? Why do you recommend the archived version?
There are two reasons people do these courses:
- The Knowledge
- The Certificate
OSSU assumes you're in it for the knowledge. You can have that for free. If you're doing it for the knowledge you don't need to submit your homework. You just need to do the homework.
If you want the recognition that you've done the thing, then you're in it for the certificate. You can't have the certificate for free. You have to pay for that.
There's no reason for you to submit homework sets unless you're doing it for the certificate -- If you're doing that, you can't actually get a certificate from the SPD course (because the course is expired) so you're in the wrong place.
If you want a certificate, then you need to take How To Code and you need to pay for it.
But you don't need to pay for anything in OSSU. We suggest you take SPD because the access to the information is better (because the course is expired) and it's more than sufficient for obtaining the knowledge.
TL;DR:
If you're in it for the knowledge, take SPD -- it's free but inactive
If you're in it for the certificate, take and pay for How To Code -- it's still an active course
### Can I do this course in another programming language?
This course has really integrated with the programming languages it uses. We suggest you to use the language specified by the course. While, the concepts you learn from this course are applicable anywhere, trying to do the course with another language is not really sensible, and would just lead to wastage of time.
### Can I use another IDE? I don't like Dr. Racket.
The programs in this course embed pictures and rich-text blocks in the code, which means, the files cannot be opened by other IDEs. While it is possible to prepare the starter files for use with other IDEs, you need Dr. Racket for that, and the time needed to do that can be better utilized in learning the concepts taught by the course.
### How do I test functions which are supposed to output random values?
You can use `check-random` to test those functions. You can [learn more about it here](https://docs.racket-lang.org/htdp-langs/beginner-abbr.html#(form._((lib._lang%2Fhtdp-beginner-abbr..rkt)._check-random))). It is needed for the Space Invaders project.
## Credits
The problem starter files and the space invaders instructions were taken from the course ["Systematic Program Design" on Edx](https://learning.edx.org/course/course-v1:UBCx+SPD1x+2T2015), licensed under [CC BY-NC-SA](https://creativecommons.org/licenses/by-nc-sa/4.0/) license.
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FILE: extras/courses.md
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# Computer Science - Great Courses
This is a list of high-quality courses that, for one reason or another, didn't make it into the curriculum.
The most common reasons are that the course isn't available often enough,
or that there was an alternative that fit better into the curriculum.
## Programming
Courses | Duration | Effort
:-- | :--: | :--:
[Introduction to Computational Thinking and Data Science](https://www.edx.org/learn/computer-science/massachusetts-institute-of-technology-introduction-to-computational-thinking-and-data-science#!)([alt](https://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-0002-introduction-to-computational-thinking-and-data-science-fall-2016/))| 10 weeks | 15 hours/week
[Introduction to Computer Science - CS50](https://www.edx.org/learn/computer-science/harvard-university-cs50-s-introduction-to-computer-science#!) ([alt](https://cs50.harvard.edu/x/)) | 12 weeks | 10-20 hours/week
[Introduction to Computer Science (Udacity)](https://www.udacity.com/course/intro-to-computer-science--cs101)| 7 weeks | 10-20 hours/week
[An Introduction to Interactive Programming in Python (Part 1)](https://www.coursera.org/learn/interactive-python-1)| 5 weeks | -
[An Introduction to Interactive Programming in Python (Part 2)](https://www.coursera.org/learn/interactive-python-2)| - | -
[Computing In Python, Part I: Fundamentals and Procedural Programming](https://www.edx.org/learn/python/the-georgia-institute-of-technology-computing-in-python-i-fundamentals-and-procedural-programming) | 5 weeks | 10 hours/week
[Computing In Python, Part II: Control Structures](https://www.edx.org/learn/python/the-georgia-institute-of-technology-computing-in-python-ii-control-structures) | 5 weeks | 10 hours/week
[Computing In Python, Part III: Data Structures](https://www.edx.org/learn/python/the-georgia-institute-of-technology-computing-in-python-iii-data-structures) | 5 weeks | 10 hours/week
[Computing In Python, Part IV: Objects & Algorithms](https://www.edx.org/learn/python/the-georgia-institute-of-technology-computing-in-python-iv-objects-algorithms) | 5 weeks | 10 hours/week
[Programming Basics](https://www.edx.org/learn/computer-programming/iitbombay-programming-basics)| 9 weeks | 8 hours/week
[Object-Oriented Programming with Java](https://java-programming.mooc.fi/)| 14 weeks | 10 hours/week
[Introduction to Programming with MATLAB](https://www.coursera.org/learn/matlab)| - | -
[Introduction to Functional Programming](https://www.edx.org/learn/computer-programming/delft-university-of-technology-introduction-to-functional-programming)| 7 weeks | 4-6 hours/week
[The Structure and Interpretation of Computer Programs (2022, Python)](http://cs61a.org/) | - | -
[The Structure and Interpretation of Computer Programs (2011, Scheme)](https://romanbird.github.io/sicp/) | - | -
[Introduction to Haskell](https://www.seas.upenn.edu/~cis194/fall16/) | 14 weeks | 4 hours/week
## Math
Courses | Duration | Effort
:-- | :--: | :--:
[Effective Thinking Through Mathematics](https://www.edx.org/learn/math/the-university-of-texas-at-austin-effective-thinking-through-mathematics-2) | 4 weeks | 2 hours/week
[Introduction to Mathematical Thinking](https://www.coursera.org/learn/mathematical-thinking) | 10 weeks | 10 hours/week
[High School Math](https://www.khanacademy.org/math/high-school-math) | - | -
[Precalculus](https://www.futurelearn.com/courses/precalculus) | 5 weeks | 6 hours/week
[Advanced Precalculus](https://www.futurelearn.com/courses/advanced-precalculus) | 4 weeks | 5 hours/week
[Calculus Applied!](https://www.edx.org/learn/calculus/harvard-university-calculus-applied) | 10 Weeks | 6hours/week
[Introduction to Probability and Data](https://www.coursera.org/learn/probability-intro)| - | -
[Linear Algebra (Strang)](https://ocw.mit.edu/courses/mathematics/18-06-linear-algebra-spring-2010/index.htm) | - | -
[Introduction to Computational Thinking](https://computationalthinking.mit.edu/Spring21/#introduction_to_computational_thinking) | - | -
[Multivariable Calculus](https://ocw.mit.edu/courses/mathematics/18-02sc-multivariable-calculus-fall-2010/index.htm) | 13 weeks | 12 hours/week
[Introduction to Probability - The Science of Uncertainty](https://www.edx.org/learn/probability/massachusetts-institute-of-technology-probability-the-science-of-uncertainty-and-data) | 18 weeks | 12 hours/week
[Matrix Methods In Data Analysis, Signal Processing, And Machine Learning](https://ocw.mit.edu/courses/18-065-matrix-methods-in-data-analysis-signal-processing-and-machine-learning-spring-2018/) | - | -
## Systems
Courses | Duration | Effort
:-- | :--: | :--:
[Cloud Computing / Distributed Programming](https://www.coursera.org/learn/cloud-computing) | 5 weeks | 5-10 hours/week
[Introduction to Parallel Programming](https://www.udacity.com/course/--cs344) ([alt](https://www.youtube.com/playlist?list=PLGvfHSgImk4aweyWlhBXNF6XISY3um82_)) ([HW](https://colab.research.google.com/github/depctg/udacity-cs344-colab))| 12 weeks | 8-10 hours/week
[Intro to Computer Systems](http://www.cs.cmu.edu/afs/cs.cmu.edu/academic/class/15213-f15/www/) ([Labs](http://csapp.cs.cmu.edu/3e/labs.html)) | 15 weeks | 12 hours/week
[Great Ideas in Computer Architecture (Machine Structures)](https://web.archive.org/web/20190919235114/https://inst.eecs.berkeley.edu/~cs61c/fa14/) ([Lectures](https://archive.org/details/ucberkeley_webcast_itunesu_915550404)) | 15 weeks | 12 hours/week
[Computer Architecture](https://www.coursera.org/learn/comparch) | - | 5-8 hours/week
[Operating System Engineering](http://ocw.mit.edu/courses/electrical-engineering-and-computer-science/6-828-operating-system-engineering-fall-2012/) | - | -
[Introduction to Operating Systems](https://www.udacity.com/course/operating-systems--ud923)| 8 weeks | 5-8 hours/week
[Advanced Operating Systems](https://www.udacity.com/course/ai-deep-learning--ud189)| 5 weeks | 5-8 hours/week
[Networking for Web Developers](https://www.udacity.com/course/computer-networking--ud436) | 12 weeks | 5-8 hours/week
Distributed Systems - [Playlist](https://www.youtube.com/playlist?list=PLrw6a1wE39_tb2fErI4-WkMbsvGQk9_UB), [Course site](http://nil.csail.mit.edu/6.824/2020/schedule.html), [Self-studying 6.824](https://lieuzhenghong.com/mit_6.824_self_study/) [6.824 Discord group for further help](https://discord.gg/KbhkEqpBqC) | - | -
## Theory
Courses | Duration | Effort
:-- | :--: | :--:
[Algorithms, Part I](https://www.coursera.org/learn/algorithms-part1) | 6 weeks | 6-12 hours/week
[Algorithms, Part II](https://www.coursera.org/learn/algorithms-part2) | 6 weeks | 6-12 hours/week
[Analysis of Algorithms (Sedgewick)](https://www.coursera.org/learn/analysis-of-algorithms) | 6 weeks | 6-8 hours/week
[Analysis of Algorithms (Skiena)](http://www3.cs.stonybrook.edu/~skiena/373/) | 15 weeks | 6-8 hours/week
[Programming Challenges (Skiena)](http://www3.cs.stonybrook.edu/~skiena/392/) | 14 weeks | 6-8 hours/week
[Data Structures and Algorithms (Specialization)](https://www.coursera.org/specializations/data-structures-algorithms) | 25 weeks | 3-10 hours/week
[Algorithmic Thinking (Part 1)](https://www.coursera.org/learn/algorithmic-thinking-1/) | - | -
[Algorithmic Thinking (Part 2)](https://www.coursera.org/learn/algorithmic-thinking-2/) | - | -
[Statistical Mechanics: Algorithms and Computations](https://www.coursera.org/learn/statistical-mechanics/) | - | -
[Approximation Algorithms Part I](https://www.coursera.org/learn/approximation-algorithms-part-1/) | - | -
[Approximation Algorithms Part II](https://www.coursera.org/learn/approximation-algorithms-part-2/) | - | -
[Design And Analysis Of Algorithms](https://ocw.mit.edu/courses/6-046j-design-and-analysis-of-algorithms-spring-2015/) | - | -
## Applications
Courses | Duration | Effort
:-- | :--: | :--:
[Using Databases with Python](https://www.coursera.org/learn/python-databases) | 5 weeks | 2-3 hours/week
[Database Systems](https://15721.courses.cs.cmu.edu/spring2016/) | 15 weeks | 12 hours/week
[Database Management Essentials](https://www.coursera.org/learn/database-management) | 7 weeks | 4-6 hours/week
[Intro to Artificial Intelligence](https://www.udacity.com/course/intro-to-artificial-intelligence--cs271)| 16 weeks | 6-10 hours/week
[Intro to Machine Learning](https://www.udacity.com/course/intro-to-machine-learning--ud120)| 10 weeks | 6-10 hours/week
[Big Data Science with the BD2K-LINCS Data Coordination and Integration Center](https://www.coursera.org/course/bd2klincs)| 7 weeks | 4-5 hours/week
## Tools
Courses | Duration | Effort
:-- | :--: | :--:
[How to Use Git and GitHub](https://www.udacity.com/blog/2015/06/a-beginners-git-github-tutorial.html) | 3 weeks | 2-3 hours/week
[Kubernetes Certified Application Developer](https://www.udemy.com/course/certified-kubernetes-application-developer/) | 5 weeks | 2 hours/week
# Online Learning - Great Courses
Courses | Duration | Effort
:-- | :--: | :--:
[Learning How to Learn](https://www.coursera.org/learn/learning-how-to-learn) | 4 weeks | 2 hours/week
[Mindshift](https://www.coursera.org/learn/mindshift) | 4 weeks | 2 hours/week
[Powersearching with Google](https://www.edx.org/learn/google-power-searching/google-power-searching-with-google) | 3 weeks | 4-6 hours/week
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FILE: extras/other_curricula.md
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# Other curricula
OSSU is one of a few efforts to inform learners how they can pursue computer science independently. Below are a few other such efforts.
- [Google - Guide for Technical Development](https://www.google.com/about/careers/students/guide-to-technical-development.html)
- [Teach Yourself Computer Science](https://teachyourselfcs.com/)
- [Obtaining a Thorough CS Background Online](http://spin.atomicobject.com/2015/05/15/obtaining-thorough-cs-background-online/)
- [P1xt](https://github.com/P1xt/p1xt-guides)
- [Functional CS](https://functionalcs.github.io/curriculum/)
What differentiates OSSU? OSSU is dedicated to how an individual can learn the undergraduate CS curriculum. This is why we use the [CS2013](../CURRICULAR_GUIDELINES.md) as our curricular guidelines. These guidelines are written by the two foremost professional organizations in computing.
This approach has the benefit of clarifying to others the extent of a student's study and understanding. Students have gone from OSSU study into both tech industry employment and into Computer Science graduate school. We look forward to helping you do the same.
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FILE: extras/puzzles-practice-plods.md
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# Computer Science - Puzzles, Practice and Plods
**Some OSSUnians value sites that allow them to practice coding problems disconnected from a course.**
> Solving "textbook" problems is easy because we've been prepped for them by the course. Solving a "real world" example is much more difficult because there is no hand-holding.
[link](https://github.com/ossu/computer-science/issues/783#issuecomment-694187743)
> So give us a problem where the parameters are laid out in x,y fashion, and we'll be able to code for it. But confront us with another problem, and it might stump us since we're still not practiced with thinking algorithmically or what have you.
[link](https://github.com/ossu/computer-science/issues/783#issuecomment-694176898)
**Others feel that its important not to get distracted by anxieties and puzzles.**
> Consider this a love letter to all the beginners out there.
> I think that when you are still in the beginner stages, it FEELS like you do not have enough competence to contributing to a project.
It FEELS like taking classes, doing programming assignments etc. are not good enough in your eyes to be considered worthy of a "project".
They feel too small and insignificant. You're not quite satisfied.
(Part of the reason might be that early stuff is too easy for you, and you haven't taken a course that kicked your ass and wiped the floor with you yet.)
Since this is online learning and there is nobody/nothing holding you back, the natural tendency is to spread wide and try to do many things all at once, get distracted.
I'VE BEEN THERE, trust me.
And naturally you are in a rush to become competent as fast as possible.
You keep thinking, "when will I be ready to do something BIG?" And you don't want to wait.
Similar to how children always seem like they are in a hurry to grow up and start imitating adults.
The dissatisfaction and the impatience make for an uncomfortable combination.
Let's call this: beginner's mind.
> The fact is, you have to be patient.
I would argue that early on in your studies, arguably, is NOT when it is most critical for you to contribute to a project...
> I would recommend the slow, steady and patient approach of OSSU over the distracted, all over the place style of learning lots of others use, with various uncoordinated resources on the internet.
Looking for a project? How about THE NEXT COURSE?
> So I think that the problem is not really a problem, but a need to adjust expectations.
I would recommend a shift in thinking, away from beginner's mind.
Think long term, so that those small, insignificant exercises and programming assignments in each course should feel like they are crucial parts of your biggest, long-term project: YOU
[link](https://github.com/ossu/computer-science/issues/783#issuecomment-690023645)
So OSSUnian, read the above and then consider how much time you wish to put into these resources:
- [Exercism](https://exercism.io/)
- [LeetCode](https://leetcode.com/)
- [HackerRank](https://www.hackerrank.com/)
- [CodeWars](https://www.codewars.com/)
- [100+ Python Coding Problems with Solutions](https://github.com/ProgrammingHero1/100-plus-python-coding-problems-with-solutions)
- [Rosalind](http://rosalind.info/problems/locations/)
- [Advent of Code](https://adventofcode.com/)
- [GitHub projects for beginners](https://github.com/MunGell/awesome-for-beginners) - Projects that are specifically geared towards beginners to GitHub. Possibly intermediate to advanced coding issues to be solved.
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FILE: extras/readings.md
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# Computer Science - Great Readings
This document consists of great books or articles on computer science.
Some are here because there is a course covering the same material;
some are just great books that you should read at some point in your career.
Once you have made it through most of the curriculum, knowing whether a book is worth your time will become easier.
Or, if you are struggling in one of the courses, perhaps reading a book on the subject will help.
## Programming
Name | Author(s)
:-- | :--:
[Introduction to Computation and Programming Using Python 3e](https://mitpress.mit.edu/books/introduction-computation-and-programming-using-python-third-edition) | John V. Guttag
[Think Python 2e](http://greenteapress.com/wp/think-python-2e/) | Allen B. Downey
[How to Design Programs 2e](https://www.htdp.org/) | Matthias Felleisen, Robert Bruce Findler, Matthew Flatt, Shriram Krishnamurthi
[Structure and Interpretation of Computer Programs 2e](https://mitpress.mit.edu/sites/default/files/sicp/full-text/book/book.html) | Hal Abelson, Jerry Sussman, Julie Sussman
[The Structure and Interpretation of Computer Programs (2021, JavaScript)](https://sourceacademy.org/sicpjs/index) | Harold Abelson, Gerald Jay Sussman, Julie Sussman, Martin Henz, Tobias Wrigstad and Samuel Fang
[Concepts, Techniques, and Models of Computer Programming](https://www.amazon.com/gp/product/0262220695) | Peter Van Roy, Seif Haridi
[Design Patterns: Elements of Reusable Object-Oriented Software](https://www.amazon.com/Design-Patterns-Elements-Reusable-Object-Oriented/dp/0201633612) | Erich Gamma, Richard Helm, Ralph Johnson, John Vlissides
[Refactoring](https://www.refactoring.com/) | Martin Fowler
[Clean Code](https://www.amazon.com/Clean-Code-Handbook-Software-Craftsmanship/dp/0132350882) | Robert Martin
[Code Complete 2e](https://www.amazon.com/Code-Complete-Practical-Handbook-Construction/dp/0735619670) | Steve McConnell
[The Pragmatic Programmer 2e](https://pragprog.com/titles/tpp20/the-pragmatic-programmer-20th-anniversary-edition/) | Andrew Hunt, David Thomas
[Programming Languages: Application and Interpretation](http://cs.brown.edu/~sk/Publications/Books/ProgLangs/) | Shriram Krishnamurthi
[Programming and Programming Languages](https://papl.cs.brown.edu/2018/) | Shriram Krishnamurthi, Benjamin S. Lerner, Joe Gibbs Politz
[Competitive Programming](https://cpbook.net/) | Steven Halim, Felix Halim
[Introduction to computing in Java](https://introcs.cs.princeton.edu/java/home/) | Robert Sedgewick, Kevin Wayne
## Math
Name | Author(s)
:-- | :--:
[Calculus Made Easy](http://www.gutenberg.org/ebooks/33283) | Silvanus P. Thompson
[Ximera: Interactive Calculus Textbooks](https://ximera.osu.edu/) | [Ximera team](https://ximera.osu.edu/about/team)
[Discrete Mathematics with Applications (4th Edition)](http://www.amazon.com/Discrete-Mathematics-Applications-Susanna-Epp/dp/0495391328/) | Susanna S. Epp
[Discrete Mathematics: An Open Introduction](https://discrete.openmathbooks.org/dmoi4/frontmatter.html) | Oscar Levin
[Applied Discrete Structures](http://faculty.uml.edu/klevasseur/ads2/) | Alan Doerr, Ken Levasseur
[Grinstead and Snell’s Introduction to Probability](https://math.dartmouth.edu/~prob/prob/prob.pdf) | Charles M. Grinstead, J. Laurie Snell
[Linear Algebra for Everyone](https://math.mit.edu/~gs/everyone/) | Gilbert Strang
[The Art of Linear Algebra](https://github.com/kenjihiranabe/The-Art-of-Linear-Algebra) | Kenji Hiranabe, Gilbert Strang
## Systems
Name | Author(s)
:-- | :--:
[But How Do It Know?](http://www.buthowdoitknow.com/) | J. Clark Scott
[Computer Systems: A Programmer's Perspective (3rd Edition)](http://csapp.cs.cmu.edu/) | Randal E. Bryant, David R. O'Hallaron
[Modern Operating Systems (4th Edition)](http://www.amazon.com/Modern-Operating-Systems-Andrew-Tanenbaum/dp/013359162X/) | Andrew S. Tanenbaum, Herbert Bos
[Computer Organization and Design: The Hardware/Software Interface (6th Edition)](https://www.amazon.com/Computer-Organization-Design-MIPS-Architecture/dp/0128201096/) | David Patternson, John Hennessy
[Computer Networks (5th Edition)](http://www.amazon.com/Computer-Networks-5th-Andrew-Tanenbaum/dp/0132126958/) | Andrew S. Tanenbaum, David J. Wetherall
[Computer Networking: A Top-Down Approach (8th Edition)](https://gaia.cs.umass.edu/kurose_ross/index.html) | James F Kurose, Keith W Ross
[Computer Networks: A Systems Approach](https://book.systemsapproach.org/) | Larry Peterson and Bruce Davie
[Distributed Systems: Principles and Paradigms](https://www.amazon.com/Distributed-Systems-Principles-Andrew-Tanenbaum/dp/153028175X) | Andrew Tanenbaum
[Operating Systems Design and Implementation](https://www.amazon.com/Operating-Systems-Design-Implementation-3rd/dp/0131429388) | Andrew S. Tanenbaum, Albert S. Woodhull
[Crafting Interpreters](https://www.craftinginterpreters.com/contents.html) | Robert Nystrom
[Principles of Compiler Design](https://www.amazon.com/Principles-Compiler-Addison-Wesley-information-processing/dp/0201000229) | Alfred Aho, Jeffrey Ullman
[Distributed Systems Reading Group](http://dsrg.pdos.csail.mit.edu/papers/) | Various
[System Design: Design large-scale systems](https://github.com/donnemartin/system-design-primer) | Various
## Theory
Name | Author(s)
:-- | :--:
[Introduction to Computing: Explorations in Language, Logic, and Machines](http://www.computingbook.org/) | David Evans
[Introduction to the Theory of Computation (3rd Edition)](https://www.amazon.com/Introduction-Theory-Computation-Michael-Sipser/dp/113318779X) | Michael Sipser
[Introduction to Algorithms (3rd Edition)](http://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press/dp/0262033844/) | Thomas H. Cormen, Charles E. Leiserson, Ronald L. Rivest, Clifford Stein
[The Algorithm Design Manual (3rd Edition)](https://www.algorist.com/) | Steven Skiena
[Algorithms (4th Edition)](https://algs4.cs.princeton.edu/home/) | Robert Sedgewick
[Algorithms Illuminated](https://algorithmsilluminated.org/) | Tim Roughgarden
[Category Theory: A Gentle Introduction](http://www.logicmatters.net/resources/pdfs/GentleIntro.pdf) | Peter Smith
[Category Theory for Programmers: The Preface](https://bartoszmilewski.com/2014/10/28/category-theory-for-programmers-the-preface/) | Bartosz Milewski
[An Introduction to Information Retrieval](https://nlp.stanford.edu/IR-book/pdf/irbookonlinereading.pdf) | Christopher D. Manning, Prabhakar Raghavan, Hinrich Schütze
[Computer Science: An interdisciplinary approach](https://introcs.cs.princeton.edu/java/cs/) | Robert Sedgewick, Kevin Wayne
[DSA CS3: Data Structures & Algorithms](https://opendsa-server.cs.vt.edu/OpenDSA/Books/CS3/html/) ([Coursenotes](https://opendsa-server.cs.vt.edu/OpenDSA/Books/CS3notes/html/) - [Slides](https://opendsa-server.cs.vt.edu/OpenDSA/Books/CS3slides/html/)) | The OpenDSA Project (supported by National Science Foundation)
## Applications
Name | Author(s)
:-- | :--:
[Architecture of a Database System](http://db.cs.berkeley.edu/papers/fntdb07-architecture.pdf) | Joseph M. Hellerstein, Michael Stonebraker, James Hamilton
[Readings in Database Systems (5th Edition)](http://www.redbook.io/) | Peter Bailis, Joseph M. Hellerstein, Michael Stonebraker, editors
[Database Management Systems (3rd Edition)](https://www.amazon.com/gp/product/0072465638) | Raghu Ramakrishnan, Johannes Gehrke
[Transaction Processing: Concepts and Techniques](https://www.amazon.com/Transaction-Processing-Concepts-Techniques-Management/dp/1558601902) | Jim Gray, Andreas Reuter
[Data and Reality: A Timeless Perspective on Perceiving and Managing Information in Our Imprecise World (3rd Edition)](https://www.amazon.com/Data-Reality-Perspective-Perceiving-Information/dp/1935504215) | William Kent
[The Architecture of Open Source Applications](http://aosabook.org/en/) | Michael DiBernardo (editor)
[An Introduction to Statistical Learning](https://www-bcf.usc.edu/~gareth/ISL/) | Gareth James, Daniela Witten, Trevor Hastie and Robert Tibshirani
[Deep Learning](http://www.deeplearningbook.org/) | Ian Goodfellow, Yoshua Bengio and Aaron Courville
[Bayesian Reasoning and Machine Learning](http://web4.cs.ucl.ac.uk/staff/D.Barber/pmwiki/pmwiki.php?n=Brml.HomePage) | David Barber
[Language Implementation Patterns](https://www.amazon.com/gp/product/193435645X) | Terence Parr
[Compilers: Principles, Techniques, and Tools (2nd Edition)](http://www.amazon.com/Compilers-Principles-Techniques-Tools-2nd/dp/0321486811/) | Alfred V. Aho, Monica S. Lam, Ravi Sethi, Jeffrey D. Ullman
[Compiler Construction](https://inf.ethz.ch/personal/wirth/CompilerConstruction/index.html) | Niklaus Wirth
[The Mythical Man-Month](https://www.amazon.com/Mythical-Man-Month-Software-Engineering-Anniversary/dp/0201835959/) | Fred Brooks, Jr.
[Physically Based Rendering: From Theory To Implementation](http://www.pbr-book.org/) | Matt Pharr, Wenzel Jakob, and Greg Humphreys
## Tools
Name | Author(s)
:-- | :--:
[The Linux Command Line](https://linuxcommand.org/tlcl.php) | by William Shotts