Every data structure I've ever used was built into the language, and I didn't know how they worked under the hood at all. I've never had to manage memory unless a process I was running would give an "out of memory" error, and then I'd have to find a workaround. I've used a few multidimensional arrays in my life and thousands of associative arrays, but I've never created data structures from scratch. It's a long plan. It may take you months. If you are familiar with a lot of this already it will take you a lot less time. Create a new branch so you can check items like this, just put an x in the brackets: [x].
More about Github-flavored markdown. Some videos are available only by enrolling in a Coursera or EdX class. These are called MOOCs. Sometimes the classes are not in session so you have to wait a couple of months, so you have no access. Effective Whiteboarding during Programming Interviews. Demystifying Tech Recruiting. Cracking The Coding Interview Set How to Get a Job at the Big You can use a language you are comfortable in to do the coding part of the interview, but for large companies, these are solid choices:.
See language resources here.
Top 43 Programming Languages: When and How to Use Them
There are a few books involved, see the bottom. The book was published in , and is somewhat outdated, but it's a terrific resource for understanding a computer in brief. Not widely used, but decent examples of what assembly looks like. Here are my recommendations by language. I don't have resources for all languages. I welcome additions.
If you read through one of these, you should have all the data structures and algorithms knowledge you'll need to start doing coding problems. You can skip all the video lectures in this project , unless you'd like a review. Additional language-specific resources here. Looking for a comprehensive resource. I watched hours of videos and took copious notes, and months later there was much I didn't remember.
I spent 3 days going through my notes and making flashcards so I could review. Retaining Computer Science Knowledge. To solve the problem, I made a little flashcards site where I could add flashcards of 2 types: general and code. Each card has different formatting. Keep in mind I went overboard and have cards covering everything from assembly language and Python trivia to machine learning and statistics. It's way too much for what's required. Note on flashcards: The first time you recognize you know the answer, don't mark it as known.
You have to see the same card and answer it several times correctly before you really know it. Repetition will put that knowledge deeper in your brain. An alternative to using my flashcard site is Anki , which has been recommended to me numerous times. It uses a repetition system to help you remember. It's user-friendly, available on all platforms and has a cloud sync system. I study them when I have some spare time. There are a lot of distractions that can take up valuable time. Focus and concentration are hard. Some subjects take one day, and some will take multiple days.
Some are just learning with nothing to implement. Each day I take one subject from the list below, watch videos about that subject, and write an implementation in:. You don't need all these. You need only one language for the interview. Write code on a whiteboard or paper, not a computer. Test with some sample inputs. Then test it out on a computer. Learn C. How computers process a program:. Sedgewick - Mergesort 5 videos. Sedgewick - Quicksort 4 videos. Bubble Sort video.
Analyzing Bubble Sort video. Insertion Sort, Merge Sort video. Insertion Sort video. Merge Sort video. Quicksort video. Selection Sort video. Not required, but I recommended them:.
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As a summary, here is a visual representation of 15 sorting algorithms. If you need more detail on this subject, see "Sorting" section in Additional Detail on Some Subjects. Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were. Skiena Lectures - great intro:. Graphs review and more :. Now that you know all the computer science topics above, it's time to practice answering coding problems.
Coding question practice is not about memorizing answers to programming problems. There is a great intro for methodical, communicative problem solving in an interview. You'll get this from the programming interview books, too, but I found this outstanding: Algorithm design canvas.
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No whiteboard at home? That makes sense. I'm a weirdo and have a big whiteboard. Instead of a whiteboard, pick up a large drawing pad from an art store. You can sit on the couch and practice. This is my "sofa whiteboard". I added the pen in the photo for scale. If you use a pen, you'll wish you could erase. Gets messy quick. Once you've learned your brains out, put those brains to work.
Take coding challenges every day, as many as you can. Think of about 20 interview questions you'll get, along with the lines of the items below. Have answers for each. Have a story, not just data, about something you accomplished. The Unix Programming Environment. Head First Design Patterns. Algorithm Design Manual Skiena. Introduction to Algorithms. Programming Pearls. These topics will likely not come up in an interview, but I added them to help you become a well-rounded software engineer, and to be aware of certain technologies and algorithms, so you'll have a bigger toolbox.
A particularly interesting self-organizing data structure is the splay tree, which uses rotations to move any accessed key to the root. Of these, I chose to implement a splay tree. From what I've read, you won't implement a balanced search tree in your interview. But I wanted exposure to coding one up and let's face it, splay trees are the bee's knees. I did read a lot of red-black tree code. Self-balancing binary search tree. AVL trees. Splay trees. N-ary K-ary, M-ary trees. More Dynamic Programming videos.
Advanced Graph Processing videos. MIT Probability mathy, and go slowly, which is good for mathy things videos :. Simonson: Approximation Algorithms video. String Matching. List of individual Dynamic Programming problems each is short. MIT Discrete Mathematics by Shai Simonson 19 videos. Discrete Mathematics Part 1 by Sarada Herke 5 videos. CSE - Analysis of Algorithms 25 videos. MIT 6. Carnegie Mellon - Computer Architecture Lectures 39 videos.
Stanford: Programming Paradigms 27 videos. Introduction to Cryptography by Christof Paar. Mining Massive Datasets - Stanford University 94 videos. Graph Theory by Sarada Herke 67 videos. Skip to content. Dismiss Join GitHub today GitHub is home to over 40 million developers working together to host and review code, manage projects, and build software together.
Sign up. A complete computer science study plan to become a software engineer.
Branch: master New pull request. Find File. Download ZIP. Sign in Sign up. Launching GitHub Desktop Go back. Launching Xcode Launching Visual Studio Latest commit eb Sep 14, Coding Interview University I originally created this as a short to-do list of study topics for becoming a software engineer, but it grew to the large list you see today.
This is my story: Why I studied full-time for 8 months for a Google interview The items listed here will prepare you well for an interview at just about any software company, including the giants: Amazon, Facebook, Google or Microsoft. Best of luck to you! Table of Contents What is it? Why use it? How to use it How to use it Everything below is an outline, and you should tackle the items in order from top to bottom.
I'm using Github's special markdown flavor, including tasks lists to check progress. I'd appreciate your help to add free and always-available public sources, such as YouTube videos to accompany the online course videos.
I like using university lectures. Python for Data Structures, Algorithms, and Interviews!
Annual Review in Automatic Programming - 1st Edition
Intro to Data Structures and Algorithms using Python! Setting it to On ran all cores up to 5GHz under fairly heavy loads most of the time. To measure that, we also run Cinebench in single-threaded mode. The Core iK easily dominates the mainstream CPUs, as well as the workhorse chips that cost almost four times as much. Our next test is the Corona Renderer.
This is a photorealistic unbiased renderer. We ran the Core i9 with MCE off, on auto, and manually turned on. For the most part, that means a long-term boost speed of all cores of 4. Next up is V-ray, which is a physically-based renderer. More cores matter, and more cores at higher clocks seem to matter more.
Far ahead, though, is that Core iK. Blender is a popular and free 3D modelling application.
Where the Core i9 consistently pulls far ahead of the Ryzen 7 X is in lighter loads. In fact, even the older Core iK passes the Ryzen 7 on the single-threaded test. Closed captioning available on our YouTube channel. Alienware Aream review Alienware Aream review. Gordon and Adam sit down to talk about the Intel Core iK review - and of course Gordon brought some benchmarking charts that show performance versus the K and Ryzen X!
This is live so we'll be taking all your burning questions. Table of Contents Just what does 9th-gen even mean? To MCE or not?
Intel Intel has three new 9th-gen CPUs in the stable and now offers an 8-core option. Just what does 9th-gen even mean?