(Part 2) Best products from r/computerscience

Freshman highschool level?

First, play games. I don't mean video games, I mean pencil and paper games that you will find in places like Games Magazine. Not so much for the word puzzles, but for the logic puzzles - but when trying to solve them think about the ways you solve them. Also wood logic puzzles and Sudoku puzzles are another type to do - all while thinking about how you go about solving them while solving them.

If you are considering a 4 year university degree at most US colleges, a Computer Science degree is really a specialization of mathematics. Important here is calculus, linear algebra (the calculus of systems of equations), probability & statistics, and discrete mathematics (the calculus of countable integer numbers).

If you are considering "computer science" degree at a 2 year vocational/technical college, this is going to focus more on specific programming skills and not the science behind computation. Here learning to program might be helpful to get ahead of the coursework, but it also might backfire as you have to "relearn" what you thought you knew.

A Software Engineer program is a 4 year degree where you learn an engineering approach to designing software. You have to get a solid understanding of Computer Science during this time, so that is still important - but you will also be learning design patterns, software architecture, and methods of large scale programming. This means you don't need to know a specific programming language, but will need one to implement solutions - however, much of it can be learned without doing any programming and instead using pseudocode.

Computer Engineering is a sub-field of Electrical Engineering, and it often is done in tandem with a Computer Science degree due to the overlap (at least, at the university I attended). Here you focus on the hardware and engineering level of computer components.

***

I think it is important to remember that computer science is a science, but not in the traditional experimental type. Instead, it is akin to a mathematical science where you deal with abstract concepts that you conduct thought experiments on and do proofs.

One big area of focus is what happens with how you solve a problem as the problem size gets big. For example, suppose you have a computer directory where you keep all your user created files in it. How do you go about finding a particular file you created? Now ask how you go about finding that file among all the files on your computer and not that directory. That's one step up in the problem size, but let us go further - how do you find it on all the computer devices in your home (cellphones, laptops, desktops, cars, "Internet of Things" ready appliances, and so on)? Step up the problem again, how do you find that file on all the computers in an institution. Keep stepping up until you get to all the computers in the world - how do you go about finding one specific file where you only know the name/size/date of creation "fingerprint" among all the files that exist?

This finding a file issue does matter. You finding a file in a single directory that you created might not be too difficult, especially if you organized them in some way to make searching easier. For example, maybe you have a folder for each letter of the alphabet and all files are sorted by the first letter in them - thus if you are looking for "Computer_Science_Textbook.pdf" you can go straight to the C section, then start searching alphabetically. This solution, however, does not work when you consider a site like YouTube and you search for a video called "Rules for Rulers" - how does that site search through all its files to find you the right one?

Another example would be sorting objects. Suppose you have a bag of 0 or 1 items, they come pre-sorted. Now what do you do to sort a bag of 2 items? 10 items? 100 items? 1000 items? 1000000 items? 1000000000 items? What if you have a 10^100 items to sort? Here is a side-by-side visual comparison of different algorithms on sorting.

So computer science isn't just about coming up with efficient ways to solve problems, but ways to solve problems as they get incomprehensibly big.

****

Of course, just like Biology, there are a lot of sub-fields within computer science. Since you will often be dealing with computers, knowing how they work helps. What is a computer and a Turing machine? How do a bunch of 1s and 0s (on/off switches) make a modern operating system? How does an operating system work - storing instructions for computers, being able to have "plug and play" components like drive storage or keyboards, how does it manage memory, and so on? When typing a program and then "compiling" it, what is happening? When executing a program, what does it do? As you get to understand things like the stack and heap, you start to understand more the limit and capabilities of a computer and how to use them in solving problems.

When exploring the field, start thinking about what really interest you. Maybe you are interested in how businesses store vasts amount of data and then use it to make recommendations for customers on products, maybe you are interested in computer vision and how to make a robot "see", maybe you are interested in how files system works on a computer system, or maybe you are interested in how a user interfaces with a computer - there are tons of different sub-fields in computer science.

Most importantly, start doing hard problems. Push yourself on mathematics and problem-solving games, all while thinking about how you think about solving them. Explore how computers work, how the internet works, what happens when problems get very big (and where these problems get bit), what types of things are getting computerized and why, and so on. Doing this will help you identify what areas of computer science might interest you.

Good luck and have fun learning!

The Stanford Algorithm book is complete overkill in my opinion do NOT read that book. That's insane. Read it when you've been doing programming for a while and have a grasp of how it even applies.

Here's my list, it's a "wanna be a decent junior" list:

• Computer Science Distilled
  
• Java/ C# / PHP/ JS (pick one)
  
• Do some Programming Challenges
  
• SQL
  
• Maybe build a small web app. Don't worry about structure so much, just build something simple.
  
• Applying UML: and Patterns: An Introduction to Object Oriented Anaysis and Design Iterative Development
  
• Head First Design Patterns
  
• Clean Architecture
  
• Refactoring: Improving the Design of Existing Code
  
• If you're interested in Web
  
• Soft Skills: Power of Habit , A Mind for Numbers , Productivity Project
  
  ​
  
  Reasoning: So, the first book is to give you a sense of all that's out there. It's short and sweet and primes you for what's ahead. It helps you understand most of the basic industry buzz words and whatnot. It answers a lot of unknown unknowns for a newbie.
  
  Next is just a list languages off the top of my head. But you can pick anything, seriously it's not a big deal. I did put Java first because that's the most popular and you'll like find a mountain of resources.
  
  Then after some focused practice, I suggest grabbing some SQL. You don't need to be an expert but you gotta know about DBs to some degree.
  
  Then I put an analysis book that's OOP focused. The nifty thing about that book, is it breaks into design patterns nicely with some very simple design patters to introduce you to design patterns and GRASP.
  
  Then I put in a legit Design Patterns book that explains and explores design patterns and principles associated with many of them.
  
  Now that you know how code is structured, you're ready for a conversation about Architecture. Clean architecture is a simple primer on the topic. Nothing too crazy, just preps you for the idea of architecture and dealing with it.
  
  Finally, refactoring is great for working devs. Often your early work will be focused on working with legacy code. Then knowing how to deal with those problems can be helpful.
  
  FINAL NOTE: Read the soft skills books first.
  
  The reason for reading the soft skills books first is it helps develop a mental framework for learning all the stuff.
  
  Good luck! I get this isn't strictly computer science and it's likely focused more toward Software Development. But I hope it helps. If it doesn't. My apologies.

If you aren't doing well in your current CS courses, I'd highly recommend you focus on your university's courses and do well in them before deciding to bite off more than you can chew. Do some research and look for very basic coding books, not ones like this: https://www.amazon.com/Programming-Language-Brian-W-Kernighan/dp/0131103628/ref=sr_1_1?ie=UTF8&qid=1478891766&sr=8-1&keywords=introduction+to+C+programming

Look for a bit more like this: https://www.amazon.com/Head-First-Java-Kathy-Sierra/dp/0596009208/ref=sr_1_1?ie=UTF8&qid=1478891799&sr=8-1&keywords=Head+first+java


This depends on what language you are currently learning right now. Don't worry about entering contests and participating in projects or open source coding until maybe second or third year in especially if you haven't had any prior experience. Don't rush yourself into this, you need to make sure you absolutely understand the basics before going into things like hackathons or being very concerned about internships. Take your time learning, and don't enroll onto too many online courses if you think that you can't handle it. Yes, online courses can be helpful, and will only be really helpful if you treat them like real classes. I would advise against code academy or khan academy to learn languages since I've gone through them and they never helped me really grasp CS material better than a book and actual coding. Key here is to code as you go through a book, or else you'll never learn how to actually code. Do tons of simple programs and if you don't understand code bits, don't get frustrated. Copy paste the code, and use a debugger (a bit more advanced but very very helpful) to go through step by step what the code is doing.


Main Points:

1. Don't rush, learn slowly, fully understand each concept before moving on
   
   
   
2. This won't be very intuitive for most people, it's like learning an entirely new thing, but you will eventually hit a wall and learning gets much much easier in the future.
   
   
   
3. Don't do more than one or two online courses, and don't be too concerned about doing any projects or competitions you likely won't be able to understand most project code or any, same goes for competitions until you at least fully know how to code in an industry standard high level language such as C/C++ or Java.
   
   
   
4. There's a lot to do, but don't overwhelm yourself, pause every now and then and focus on a single task
   
   
   Best of luck to you, remember to enjoy the process, and keep in mind that while you might not like coding, CS isn't coding. It's the principles that underlie what we can do with code. A lot of it comes from really basic logic, you will be surprised in the future how easy some things can be to understand with basic step by step thinking.

Python in a Nutshell: A Desktop Quick Reference https://www.amazon.com/dp/144939292X/ref=cm_sw_r_cp_api_R7rVAb4GP2ADN

I personally think these are great for both learning and referencing. It is more expensive than I thought it would be but I’m sure you can find a good deal somewhere. Honestly you are likely to find success with any highly rated Python book on Amazon. Try to look through a few pages of some different ones and see what you prefer. Also make sure you specify which version of Python you are using (2.x or 3.x). Best of luck!

Mechanical Keyboards: High quality keyboards with tactile feedback. They have a real impact on your typing speed, and also make programming less boring. They are very commonly used by programmers. Mechanical keyboards have different "switches" that make different tactile sounds. "Blue Switches" are considered the most popular. Here are some examples: https://www.amazon.com/s/ref=nb_sb_noss?url=search-alias%3Daps&field-keywords=mechanical+keyboard.

Gaming Mouse: Same usage as keyboards, but not as useful. Its mostly just nice to have.

Textbooks: In computer science, there are one or two textbooks per subfield that are considered to be "Bibles" of the subfield because of their importance. If your bf just started cs, he won't have any interests in any subfield. However, the subfield of algorithms is more or less all encompassing. More crucially, knowledge of algorithms is the most important (and arguably only) thing you need to know to get a job. If you want to work at a top CS company like Facebook or Google, you need to know your algorithms very well in order to pass the interviews. There are two books I can recommend for this:
https://www.amazon.com/Introduction-Algorithms-3rd-MIT-Press/dp/0262033844/ref=sr_1_1?ie=UTF8&qid=1503472210&sr=8-1&keywords=algorithms
and
https://www.amazon.com/Algorithm-Design-Manual-Steven-Skiena/dp/1848000693/ref=sr_1_5?ie=UTF8&qid=1503472210&sr=8-5&keywords=algorithms
These are the most prolific algorithms books (imo). Another important book is "Cracking the Coding Interview": https://www.amazon.com/Cracking-Coding-Interview-Programming-Questions/dp/0984782850/ref=sr_1_1?s=books&ie=UTF8&qid=1503471832&sr=1-1&keywords=how+to+crack+the+coding+interview

This is the "Bible" textbook for passing tech interviews. Every computer science student I know at school has at least skimmed through this book once.

Personally, if I were starting in CS, I would appreciate either the mechanical keyboard or the "Cracking the Coding Interview" textbook the most. Good luck!

Sorry to state the obvious, but it's the postulates of quantum mechanics. Cutting some corners, you can put it like this.

The input to your algorithm is a complex vector, |psi>. The algorithm itself is defined by a unitary matrix, U, that is multiplied with that vector giving the result |psi'> = U |psi>. And finally you measure your output from |psi'> by applying a projection to it.

If you want to simulate 5 qubits, you need to simulate a 2^5 dimensional complex vector and a 2^5 x 2^5 unitary matrix as the computation. Since there's an infinite number of possible states for any vector, you obviously can't simulate it perfectly, so you need to decide on some appropriate approximation.

Here's a decent lecture note on the basic postulates of QM.

If you have more interest in the subject, I'd recommend Quantum Computation and Quantum Information

Some of the first pages are available here for free

For some great comp sci style thinking puzzles without the computer, this is crazy fun. From bring up at party casual to mind meltingly difficult. Algorithmic Puzzles https://www.amazon.com/dp/0199740445/ref=cm_sw_r_cp_awd_AeMzwbE3NJARB

Example: if you can only fit two pancakes on your griddle, what's the fastest way to make three pancakes?

Let me refer this excellent book for you ....

It will take you on a wonderful journey through the science part of Computer Science ....

I enjoyed The Nature of Computation during my theory course.

If you want to better understand how computers work and take a bottom-up approach into programming, check out Patt's Introduction to Computing Systems.

If you are looking for specifically the basic hardware stuff, this is the book that I used. It is a textbook but it is written well. https://www.amazon.com/Introduction-Computing-Systems-Gates-Beyond/dp/0072467509

Read The Algorithm Design Manual by Steven Skiena, or if you prefer, watch and learn from his video series on youtube.

Tim Roughgarden’s course on coursera is also very good.

For practice, solve problems on leetcode.

​

Source: https://teachyourselfcs.com

Algorithmic Puzzles. Reading about programming is dull. Doing is more fun, but you have already to know something. This book introduces algorithmic way of thinking without actual programming

https://www.amazon.com/Algorithmic-Puzzles-Anany-Levitin/dp/0199740445

What's your education level? I can't speak for the one that you linked but the 'standard' textbook in the field is Nielsen & Chuang's Quantum Computation and Quantum Information.

I purchased the following two books:

• The Algorithm Design Manual (Springer) (Amazon)
  
• Understanding Cryptography (Springer) (Amazon)