Study Plan: Software Engineering / Computer Science

Owner: Brandon Peebles _{^{Image from https://www.clipartkey.com/view/ohhmhm_computer-science-and-engineering-computer-science-icon-circle/}}

What is this?

This living document is my study plan for computer science and software engineering topics. Given my previous experience in analytics/operations plus education in industrial & systems engineering, it’s been tailored to specifically fill my own knowledge gaps and prepare me for a role in software engineering. It outlines what I’m covering (in roughly the order I plan to cover it) as well as serves as a way to track my progress. I will be focusing on core computer science topics/concepts, full-stack JavaScript web development, and Python.

Outline:

Other Topics (when I have time): Outline presented --> see detailed view in appendix below, adapted from the Coding Interview University repo created by John Washam (link)
- Recursion
- Dynamic Programming
- Combinatorics (n choose k) & Probability
- NP, NP-Complete and Approximation Algorithms
- Testing
- Scheduling
- String searching & manipulation
- Tries
- Floating Point Numbers
- Unicode
- Endianness
- Networking
- Compilers
- Emacs and vi(m)
- Unix command line tools (read book in Other Resources)
- Messaging, Serialization, and Qeueing Systems

To be prioritized:

Flask Web Development 2nd Edition (book) (TBD - may prioritize Node.JS instead)
Clean Code (book)
Cracking the Coding Interview 6th Edition (book)
Overview of Discrete Mathematics:
- Introductory Discrete Mathematics (book) - read for general knowledge
- [Intro to Discrete Math - 79 videos to watch for general knowledge] (https://www.youtube.com/watch?v=rdXw7Ps9vxc&list=PLHXZ9OQGMqxersk8fUxiUMSIx0DBqsKZS)

Other resources:

Books
- Learning Python: Powerful Object Oriented Programming 5th Edition
- The UNIX Programming Environment
- The C Programming Language 2nd Edition
- Cracking the Coding Interview 6th Edition
Websites
- LeetCode (link)
- OSSU: Computer Science (repo)
- Teach Yourself Computer Science

Appendix:

Detailed view of some of the content (to be) covered to supplement above outline

First section is again adapted directly from the Coding Interview University repo created by John Washam (link). I've included it below so I can check things off as I go.

Algorithmic complexity / Big-O / Asymptotic analysis

Data Structures

Arrays
- Implement an automatically resizing vector.
- Description:
  - Arrays (video)
  - UC Berkeley CS61B - Linear and Multi-Dim Arrays (video) (Start watching from 15m 32s)
  - Basic Arrays (video)
  - Multi-dim (video)
  - Dynamic Arrays (video)
  - Jagged Arrays (video)
  - Jagged Arrays (video)
  - Resizing arrays (video)
- Implement a vector/dynamic array class (mutable array with automatic resizing):
  - size() - number of items
  - capacity() - number of items it can hold
  - at(index) - returns item at given index, blows up if index out of bounds
  - push(item)
  - insert(index, item) - inserts item at index, shifts that index's value and trailing elements to the right
  - prepend(item) - can use insert above at index 0
  - pop() - remove from end, return value
  - delete(index) - delete item at index, shifting all trailing elements left
  - remove(item) - looks for value and removes index holding it (even if in multiple places)
  - find(item) - looks for value and returns first index with that value, -1 if not found
  - resize(new_capacity) // private function
    - when you reach capacity, resize to double the size
    - when popping an item, if size is 1/4 of capacity, resize to half
- Time
  - O(1) to add/remove at end (amortized for allocations for more space), index, or update
  - O(n) to insert/remove elsewhere
- Space
  - contiguous in memory, so proximity helps performance
  - space needed = (array capacity, which is >= n) * size of item, but even if 2n, still O(n)
Linked Lists
- General concept via textbook
- Linked List vs Arrays:
  - Core Linked Lists Vs Arrays (video)
  - In The Real World Linked Lists Vs Arrays (video)
- implement:
  - size() - returns number of data elements in list
  - empty() - bool returns true if empty
  - value_at(index) - returns the value of the nth item (starting at 0 for first)
  - push_front(value) - adds an item to the front of the list
  - pop_front() - remove front item and return its value
  - push_back(value) - adds an item at the end
  - pop_back() - removes end item and returns its value
  - front() - get value of front item
  - back() - get value of end item
  - insert(index, value) - insert value at index, so current item at that index is pointed to by new item at index
  - erase(index) - removes node at given index
  - value_n_from_end(n) - returns the value of the node at nth position from the end of the list
  - reverse() - reverses the list
  - remove_value(value) - removes the first item in the list with this value
- Doubly-linked List
  - Description (video)
  - No need to implement
Stack
- Stacks (video)
- Using Stacks Last-In First-Out (video)
- Will not implement. Implementing with array is trivial.
Queue
- Implement using linked-list, with tail pointer:
  - enqueue(value) - adds value at position at tail
  - dequeue() - returns value and removes least recently added element (front)
  - empty()
- Implement using fixed-sized array:
  - enqueue(value) - adds item at end of available storage
  - dequeue() - returns value and removes least recently added element
  - empty()
  - full()
- Cost:
  - a bad implementation using linked list where you enqueue at head and dequeue at tail would be O(n) because you'd need the next to last element, causing a full traversal each dequeue
  - enqueue: O(1) (amortized, linked list and array [probing])
  - dequeue: O(1) (linked list and array)
  - empty: O(1) (linked list and array)
Hash table
- Videos:
- Online Courses:
- implement with array using linear probing
  - hash(k, m) - m is size of hash table
  - add(key, value) - if key already exists, update value
  - exists(key)
  - get(key)
  - remove(key)

More Knowledge

Binary search
- Binary Search (video)
- Binary Search (video)
- detail
- Implement:
  - binary search (on sorted array of integers)
  - binary search using recursion
Bitwise operations
- Bits cheat sheet - you should know many of the powers of 2 from (2^1 to 2^16 and 2^32)
- Get a really good understanding of manipulating bits with: &, |, ^, ~, >>, <<
- 2s and 1s complement
- count set bits
- swap values:
  - Swap
- absolute value:
  - Absolute Integer

Trees

Trees - Notes & Background
- Series: Core Trees (video)
- Series: Trees (video)
- basic tree construction
- traversal
- manipulation algorithms
- BFS(breadth-first search) and DFS(depth-first search) (video)
  - BFS notes:
    - level order (BFS, using queue)
    - time complexity: O(n)
    - space complexity: best: O(1), worst: O(n/2)=O(n)
  - DFS notes:
    - time complexity: O(n)
    - space complexity: best: O(log n) - avg. height of tree worst: O(n)
    - inorder (DFS: left, self, right)
    - postorder (DFS: left, right, self)
    - preorder (DFS: self, left, right)
Binary search trees: BSTs
- Binary Search Tree Review (video)
- Series (video)
  - starts with symbol table and goes through BST applications
- Introduction (video)
- MIT (video)
- C/C++:
- Implement (complete other methods later if time permits):
  - insert // insert value into tree
  - get_node_count // get count of values stored
  - print_values // prints the values in the tree, from min to max
  - delete_tree
  - is_in_tree // returns true if given value exists in the tree
  - get_height // returns the height in nodes (single node's height is 1)
  - get_min // returns the minimum value stored in the tree
  - get_max // returns the maximum value stored in the tree
  - is_binary_search_tree
  - delete_value
  - get_successor // returns next-highest value in tree after given value, -1 if none
Heap / Priority Queue / Binary Heap
- visualized as a tree, but is usually linear in storage (array, linked list)
- Heap
- Introduction (video)
- Naive Implementations (video)
- Binary Trees (video)
- Tree Height Remark (video)
- Basic Operations (video)
- Complete Binary Trees (video)
- Pseudocode (video)
- Heap Sort - jumps to start (video)
- Heap Sort (video)
- Building a heap (video)
- MIT: Heaps and Heap Sort (video)
- CS 61B Lecture 24: Priority Queues (video)
- Linear Time BuildHeap (max-heap)
- Implement a max-heap:
  - insert
  - sift_up - needed for insert
  - get_max - returns the max item, without removing it
  - get_size() - return number of elements stored
  - is_empty() - returns true if heap contains no elements
  - extract_max - returns the max item, removing it
  - sift_down - needed for extract_max
  - remove(i) - removes item at index x
  - heapify - create a heap from an array of elements, needed for heap_sort
  - heap_sort() - take an unsorted array and turn it into a sorted array in-place using a max heap
    - note: using a min heap instead would save operations, but double the space needed (cannot do in-place).

Sorting

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

Graphs can be used to represent many problems in computer science, so this section is long, like trees and sorting were.

Even More Knowledge

Dynamic Programming
- You probably won't see any dynamic programming problems in your interview, but it's worth being able to recognize a problem as being a candidate for dynamic programming.
- This subject can be pretty difficult, as each DP soluble problem must be defined as a recursion relation, and coming up with it can be tricky.
- I suggest looking at many examples of DP problems until you have a solid understanding of the pattern involved.
- Videos:
  - the Skiena videos can be hard to follow since he sometimes uses the whiteboard, which is too small to see
  - Skiena: CSE373 2012 - Lecture 19 - Introduction to Dynamic Programming (video)
  - Skiena: CSE373 2012 - Lecture 20 - Edit Distance (video)
  - Skiena: CSE373 2012 - Lecture 21 - Dynamic Programming Examples (video)
  - Skiena: CSE373 2012 - Lecture 22 - Applications of Dynamic Programming (video)
  - Simonson: Dynamic Programming 0 (starts at 59:18) (video)
  - Simonson: Dynamic Programming I - Lecture 11 (video)
  - Simonson: Dynamic programming II - Lecture 12 (video)
  - List of individual DP problems (each is short): Dynamic Programming (video)
- Yale Lecture notes:
  - Dynamic Programming
- Coursera:
Object-Oriented Programming
- Optional: UML 2.0 Series (video)
- SOLID OOP Principles: SOLID Principles (video)
Design patterns
- Quick UML review (video)
- Learn these patterns:
  - strategy
  - singleton
  - adapter
  - prototype
  - decorator
  - visitor
  - factory, abstract factory
  - facade
  - observer
  - proxy
  - delegate
  - command
  - state
  - memento
  - iterator
  - composite
  - flyweight
- Chapter 6 (Part 1) - Patterns (video)
- Chapter 6 (Part 2) - Abstraction-Occurrence, General Hierarchy, Player-Role, Singleton, Observer, Delegation (video)
- Chapter 6 (Part 3) - Adapter, Facade, Immutable, Read-Only Interface, Proxy (video)
- Series of videos (27 videos)
- Head First Design Patterns
  - I know the canonical book is "Design Patterns: Elements of Reusable Object-Oriented Software", but Head First is great for beginners to OO.
- Handy reference: 101 Design Patterns & Tips for Developers
- Design patterns for humans
Combinatorics (n choose k) & Probability
- Math Skills: How to find Factorial, Permutation and Combination (Choose) (video)
- Make School: Probability (video)
- Make School: More Probability and Markov Chains (video)
- Khan Academy:
  - Course layout:
    - Basic Theoretical Probability
  - Just the videos - 41 (each are simple and each are short):
    - Probability Explained (video)
NP, NP-Complete and Approximation Algorithms
- Know about the most famous classes of NP-complete problems, such as traveling salesman and the knapsack problem, and be able to recognize them when an interviewer asks you them in disguise.
- Know what NP-complete means.
- Computational Complexity (video)
- Simonson:
- Skiena:
- Complexity: P, NP, NP-completeness, Reductions (video)
- Complexity: Approximation Algorithms (video)
- Complexity: Fixed-Parameter Algorithms (video)
- Peter Norvig discusses near-optimal solutions to traveling salesman problem:
  - Jupyter Notebook
- Pages 1048 - 1140 in CLRS if you have it.
Caches
- LRU cache:
- CPU cache:
  - MIT 6.004 L15: The Memory Hierarchy (video)
  - MIT 6.004 L16: Cache Issues (video)
Processes and Threads
- Computer Science 162 - Operating Systems (25 videos):
  - for processes and threads see videos 1-11
  - Operating Systems and System Programming (video)
- What Is The Difference Between A Process And A Thread?
- Covers:
  - Processes, Threads, Concurrency issues
    - difference between processes and threads
    - processes
    - threads
    - locks
    - mutexes
    - semaphores
    - monitors
    - how they work
    - deadlock
    - livelock
  - CPU activity, interrupts, context switching
  - Modern concurrency constructs with multicore processors
  - Paging, segmentation and virtual memory (video)
  - Interrupts (video)
  - Process resource needs (memory: code, static storage, stack, heap, and also file descriptors, i/o)
  - Thread resource needs (shares above (minus stack) with other threads in the same process but each has its own pc, stack counter, registers, and stack)
  - Forking is really copy on write (read-only) until the new process writes to memory, then it does a full copy.
  - Context switching
    - How context switching is initiated by the operating system and underlying hardware
- threads in C++ (series - 10 videos)
- concurrency in Python (videos):
Testing
- To cover:
  - how unit testing works
  - what are mock objects
  - what is integration testing
  - what is dependency injection
- Agile Software Testing with James Bach (video)
- Open Lecture by James Bach on Software Testing (video)
- Steve Freeman - Test-Driven Development (that’s not what we meant) (video)
  - slides
- Dependency injection:
  - video
  - Tao Of Testing
- How to write tests
Scheduling
- in an OS, how it works
- can be gleaned from Operating System videos
String searching & manipulations
If you need more detail on this subject, see "String Matching" section in Additional Detail on Some Subjects
Tries
- Note there are different kinds of tries. Some have prefixes, some don't, and some use string instead of bits to track the path.
- I read through code, but will not implement.
- Sedgewick - Tries (3 videos)
- Notes on Data Structures and Programming Techniques
- Short course videos:
- The Trie: A Neglected Data Structure
- TopCoder - Using Tries
- Stanford Lecture (real world use case) (video)
- MIT, Advanced Data Structures, Strings (can get pretty obscure about halfway through) (video)
Floating Point Numbers
- simple 8-bit: Representation of Floating Point Numbers - 1 (video - there is an error in calculations - see video description)
- 32 bit: IEEE754 32-bit floating point binary (video)
Unicode
- The Absolute Minimum Every Software Developer Absolutely, Positively Must Know About Unicode and Character Sets
- What Every Programmer Absolutely, Positively Needs To Know About Encodings And Character Sets To Work With Text
Endianness
- Big And Little Endian
- Big Endian Vs Little Endian (video)
- Big And Little Endian Inside/Out (video)
  - Very technical talk for kernel devs. Don't worry if most is over your head.
  - The first half is enough.
Networking
- if you have networking experience or want to be a reliability engineer or operations engineer, expect questions
- otherwise, this is just good to know
- Khan Academy
- UDP and TCP: Comparison of Transport Protocols (video)
- TCP/IP and the OSI Model Explained! (video)
- Packet Transmission across the Internet. Networking & TCP/IP tutorial. (video)
- HTTP (video)
- SSL and HTTPS (video)
- SSL/TLS (video)
- HTTP 2.0 (video)
- Video Series (21 videos) (video)
- Subnetting Demystified - Part 5 CIDR Notation (video)
- Sockets:
  - Java - Sockets - Introduction (video)
  - Socket Programming (video)

Final Review

This section will have shorter videos that you can watch pretty quickly to review most of the important concepts.
It's nice if you want a refresher often.

Series of 2-3 minutes short subject videos (23 videos)
- Videos
Series of 2-5 minutes short subject videos - Michael Sambol (18 videos):
- Videos
Sedgewick Videos - Algorithms I
Sedgewick Videos - Algorithms II

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Study Plan: Software Engineering / Computer Science

What is this?

Outline:

To be prioritized:

Other resources:

Appendix:

Detailed view of some of the content (to be) covered to supplement above outline

First section is again adapted directly from the Coding Interview University repo created by John Washam (link). I've included it below so I can check things off as I go.

Algorithmic complexity / Big-O / Asymptotic analysis

Data Structures

Arrays

Linked Lists

Stack

Queue

Hash table

More Knowledge

Binary search

Bitwise operations

Trees

Trees - Notes & Background

Binary search trees: BSTs

Heap / Priority Queue / Binary Heap

Sorting

Graphs

Even More Knowledge

Dynamic Programming

Object-Oriented Programming

Design patterns

Combinatorics (n choose k) & Probability

NP, NP-Complete and Approximation Algorithms

Caches

Processes and Threads

Testing

Scheduling

String searching & manipulations

Tries

Floating Point Numbers

Unicode

Endianness

Networking

Final Review

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