Data structure

scikit-image

scikit-learn

pandas (software)

Jupyter Notebook

Code Complexity

Memory management

Python

List (abstract data type)

File Processing

pandas DataFrame

Data Science Libraries

Search

Joins

Images

Statistics

Indexes

Control Structures

Data Visualization

Class (computer programming)

Algorithmic Efficiency

Ethics

p-hacking

Tree (data structure)

NumPy

String (computer science)

Computer file

Machine learning

Object (computer science)

Geospatial Data

Convolution

Algorithmic fairness

Algorithmic Privacy

CSE 163 Intermediate Data Programming University of Washington Summer 2022 This course offers an intermediate level of data programming, focusing on different data types, data science tools, code complexity, and memory management. It emphasizes the efficient use of concepts for data programming. The world has become data-driven. Domain scientists and industry increasingly rely on data analysis to drive innovation and discovery; this reliance on data is not only restricted to science or business, but also is crucial to those in government, public policy, and those wanting to be informed citizens. As the size of data continues to grow, everyone will need to use powerful tools to work with that data.

This course teaches intermediate data programming. It is a follow on to CSE142 (Computer programming I) or CSE160 (Data Programming).

The course complements CSE143, which focuses more deeply on fundamental programming concepts and the internals of data structures. In contrast, CSE163 emphasizes the efficient use of those concepts for data programming.

In this course, students will learn:

1. More advanced programming concepts than in CSE142 or CSE160 including how to write bigger programs with multiple classes and modules.
1. How to work with different types of data: tabular, text, images, geo-spatial.
1. Ecosystem of data science tools including Jupyter Notebook and various data science libraries including scikit image, scikit learn, and Pandas data frames.
1. Basic concepts related to code complexity, efficiency of different types of data structures, and memory management.  This is class is designed as the second introductory programming course that focuses on writing programs that work with data. The prerequisites for the class require students having taken CSE 142 or CSE 160 and the class has been designed to be accessible to students from either of those backgrounds. Students that have taken 143 are welcome to take this class as it will serve as a complement to the material learned in 143 with only minor overlap.

Because this course will have students coming from many different class backgrounds, the first couple of weeks will be pretty different for students depending on what classes they have taken. Here is what we expect students to see in the first weeks based on their background:

- 142: The first two weeks might go pretty fast, but will be doable since you already know all the concepts (loops, conditionals, methods) and you are just learning all the new “words” in Python to use those concepts. This might require a little bit of extra practice early in the quarter so you are familiar with translating all the ideas you have learned in 142 to this new language. The first week has been designed to be a recap of all things 142 so you don’t also have to be learning a ton of new material while learning a new language in the first week.
 - 160: The first week will just be a bit of a review for you, but the class will start covering material you haven’t seen before starting in the second week.
- 143: You are in a similar boat as the 142 students, where you know a lot of the concepts but don’t know the Python language. You’ll probably see a few things that you saw in 143 in this class, but I think the new context of processing data in a new language will still keep it new, exciting, and challenging. 

CSE 163 Intermediate Data Programming

Computer Programming

Data Science

Java (programming language)

Software testing

References

Recursion (computer science)

SLLists

Nested Classes

Debugging

Array (data structure)

Array Resizing

Inheritance (object-oriented programming)

Extends

Higher-order function (HOF)

Exception handling

Object Methods

Disjoint Sets

Set (abstract data type)

Binary Search Tree (BST)

Red Black Trees

Tree traversal

Multi-Dimensional Data

Prefix Operations and Tries

Reductions and Decomposition

Quick Sort

Algorithmic Bounds

Scope

Linked list

Shortest paths

Heap (data structure)

Kolmogorov complexity

Sentinel Nodes

DLLists

ALists

SLists

Implements

Casting

Subtype Polymorphism

Iterators

Asymptotics

Abstract Data Type (ADT)

Map (higher-order function)

B-tree

Hashing

Priority queue (PQ)

Graph traversal

Range Searching

Software Engineering

Sorting algorithm

Radix Sorts

Compression

P=NP

Static

Minimum Spanning Tree (MST)

CS 61B: Data Structures UC Berkeley Fall 2022 CS 61B focuses on software efficiency from design and runtime perspectives. It covers object-oriented programming with Java, teaching data structures and various programming concepts. The course promotes hands-on learning with optional assignments. The CS 61 series is an introduction to Computer Science, with particular emphasis on software and machines from a programmer’s point of view. CS 61A covered high-level approaches to problem-solving, providing you with a variety of ways to organize solutions to programming problems as compositions of functions, collections of objects, or sets of rules. In CS 61B, we move to a somewhat more detailed (and to some extent, more basic) level of programming by focusing particularly on the efficiency of writing programs (design) and running programs (runtime).  This class assumes you have taken CS 61A, CS 88, or E 7, or have equivalent background to a student who has taken one of these courses. The course is largely built upon the assumption that you have taken CS 61A. CS 88 and E7 students may find the beginning of the course to be a bit scarier, particularly when it comes to object oriented programming. We assume you are coming in with zero Java experience, but we will move through basic Java syntax very quickly.

We recommend all students to complete the [optional introductory assignment](https://fa22.datastructur.es/materials/hw/hw0/) by the beginning of the semester to get comfortable with Java and practice some of the programming skills expected by this class.

If you already have Java experience, great! We hope that you’ll help out your fellow students in discussion, lab, and on our class forum, particularly in the opening weeks when everyone is catching up on Java. **There is no required textbook for the class.**

There is an online textbook written by myself and a large team of former TAs. It can be found at https://joshhug.gitbooks.io/hug61b. If you find these notes insufficient, you might consider consulting [Paul Hilfinger’s (free) Java Reference](http://www-inst.eecs.berkeley.edu/~cs61b/fa14/book1/java.pdf) or [Head First Java, 2nd Edition by Sierra and Bates (O’Reilly, 2005)](https://www.google.com/search?q=head+first+java). These are not required for the course. The **optional** textbook for the weeks 5-14 of the course is Algorithms, 4th Edition by Wayne and Sedgewick.

The official description of the Java core language is available online in [The Java Language Specification](https://docs.oracle.com/javase/specs/jls/se17/html/index.html) (Java SE 17 Edition) by James Gosling, Bill Joy, Guy Steele, Gilad Bracha, Alex Buckley, Daniel Smith, and Gavin Bierman. It’s extremely thorough and precise, at the expense of being quite dense and technical. You will find the official [Java 17 documentation](ttps://docs.oracle.com/en/java/javase/17/docs/api/index.html) to be useful as well

CS 61B: Data Structures

Data Structures and Algorithms

Rust

C Programming

Function (computer programming)

Pointer (computer programming)

Computer memory

Modules

Compilation

Makefiles

File I/O

Buffering

POSIX I/O

System Calls

Directories

C++ Programming

Introduction

Const

Constructors

new/delete

Smart pointer

Client-side Networking

HTTP

Templates

Networks

Server-side Networking

Standard Template Library (STL)

vtables

IP (Internet Protocol)

DNS (Domain Name System)

CSE 333 Systems Programming University of Washington Summer 2022 This course provides an understanding of systems in computing, focusing on operating systems, networking, and C/C++ languages. Students learn about low-level data representation, memory management, system interactions, and efficient programming workflows. It delves into C++ idioms, network protocols, and concurrency. Prior knowledge of C programming and Linux tools is required. Systems Programming is the exploration of the composition and interface of systems in computing looking through the lens of operating systems, networking, and C/C++ languages. Through peeling levels of abstraction, students will view practices of low-level data representation (e.g. C, C++); explicit memory management; interacting with operating-system services; and cache-aware programming. Course Learning objectives are the goals that are set out for you to accomplish in this course:

- Understand system theory of how a system interacts with its environment and controlling its input and output (feedback loops, input and output, deterioration, computation)
- Unravel layers of abstraction found within systems
- Describe multiple systems throughout the computing space (e.g. HTTP, Operating Systems, Threading, and Makefiles)
- Interpret and build an efficient workflow for programming in larger sized code bases
- Debug substantial C/C++ programs using debugging tools (GDB and Valgrind) - [CSE351 (The Hardware/Software Interface)](http://www.cs.washington.edu/education/courses/cse351): CSE 351 provides students with rudimentary knowledge of C programming; the ability to write, run, and debug programs; familiarity with Linux and the use of Linux compilation, editing, and debugging tools; a solid mental model of the relationship between high-level code (C) and assembly-level compiled code.
- [CSE 143 (Intro Programming II)](http://www.cs.washington.edu/education/courses/cse143): CSE 143 is not a direct prerequisite (CSE 351 prerequisite), but some of its topics are beneficial to review for CSE 333. This includes classes, inheritance, good style practices, and simple data structures such as linked lists, trees, hash tables, and queues. Further knowledge of Java and these data structures is not required, but could be helpful! ### Textbooks

There are no strictly required texts for this course. Most people will find it useful to have both a C and a C++ reference; suggestions are given below. The C++ Primer is strongly recommended as C++ is a big, complex language and it is hard to understand how it all fits together from just Google and Stack Overflow snippets and folklore.

#### Strongly Recommended:

C++ Primer (5th Edition), Lippman, Lajoie & Moo. ISBN 0-321-71411-3.

#### Suggested:

- [*C Programming Language (2nd Edition)*](http://www.amazon.com/Programming-Language-2nd-Brian-Kernighan/dp/0131103628/), Kernighan & Ritchie. ISBN 0-13-110362-8.
    - The definitive book on C from the people who created it. You will want to use an online reference for information about the latest libraries, but every serious programmer should study this book.
- [*Computer Systems: A Programmer's Perspective (3rd Edition)*](http://csapp.cs.cmu.edu/3e/home.html), Bryant & O'Hallaron, ISBN 0-13-409266-X.
    - Useful review of CSE 351 and background for many additional topics covered more extensively in CSE333.
- [*Effective C++: 55 Specific Ways to Improve Your Programs and Designs (3rd Edition)*](http://www.amazon.com/Effective-Specific-Improve-Programs-Designs/dp/tags-on-product/0321334876), Scott Meyers. ISBN 0321334879.
- [*Effective Modern C++: 42 Specific Ways to Improve Your Use of C++11 and C++14*](http://www.amazon.com/Effective-Modern-Specific-Ways-Improve/dp/1491903996), Scott Meyers. ISBN 1491903996.


### Proposed Topic List

- C Programming and idioms (Memory Management, cstdio, error handling, and structs)
- System Management Tools (Makefiles, Header Guards, GDB, and Valgrind)
- System Interfacing with Operating Systems (POSIX)
- C++ Programming and idioms (namespaces, templates, STL, smart pointers)
- C++ Classes and Inheritance (Classes, operator overloading, OOP)
- Network Programming and Protocols (POSIX, HTTP)
- Concurrency (pthreads)

CSE 333 Systems Programming

Operating Systems

Hash table

Polymorphism (computer science)

Debugger

Object-Oriented Programming (OOP)

Web scraping

Binary tree

Web API

Merge sort

Random testing

Binary expression tree

CSCI 0112 - Computing Foundations: Program Organization Brown University Fall 2022 CSCI 0112 progresses from CSCI 0111, focusing on structuring programs for solving isolated subproblems. It delves into various algorithms, implementations from abstract descriptions, data organization methods, and program efficiency. Ethical considerations in software development are underscored. Topics include data structures, OOP, web APIs, machine learning, and more. CSCI 0112 builds on the data-focused programming introduced in CSCI 0111. Students will learn how to structure their programs so that subproblems can be solved in isolation. We will discuss several algorithms–abstract descriptions of computations–and students will get practice implementing working programs from these abstract descriptions. We will continue our study of data organization, covering several new ways of storing and accessing information. We will also continue to discuss program efficiency; students will learn how to reason about the performance of their programs. Finally, like CSCI 0111, CSCI 0112 will foreground the ethical considerations programmers must think about when developing software.   

CSCI 0112 - Computing Foundations: Program Organization

Regular Expressions from Axioms

Induction

Propositional Encodings

SAT Certificates

Contracts

Dynamic Logic

Convergence

DPLL

Blasting & Quantifiers

Compositional Reasoning

Predicate transformer semantics

Sound Transformations

Temporal logic

Ghosts

Loops

Sequent Calculus

Satisfiability Modulo Theories (SMT)

Semantics

Resolution

Equality and Arrays

15-414 Bug Catching: Automated Program Verification Carnegie Mellon University Spring 2022 This course is about software verification, with the goal of writing bug-free code. Students will learn to formalize program correctness, write verified code, and use automated tools for verification. It explores the principles behind verification tools, logical specifications, and deductive reasoning. Previous knowledge in program correctness reasoning is beneficial. High-profile bugs continue to plague the software industry, leading to major problems in the reliability, safety, and security of systems. This course teaches students how to write bug-free code through the process of software verification, which aims to prove the correctness of a program with respect to a mathematical specification. Along the way, students will learn how to:

- Specify correct program behavior
- Prove the correctness of their code
- Use formal semantics to reason about the soundness of proof rules
- Write practical and efficient verified code
- Use decision procedures and model checkers to reduce verification effort

Students will learn the principles and algorithms behind automated verification tools, and understand their practical limitations while gaining experience writing verified, machine-checked code that solves real problems. **Identify and formalize program correctness.** In order to reason about the existence of bugs in code, and ultimately justify their absence, we need to state what it means for the code to be correct. Students will learn how to formalize correctness using logical specifications that give precise meaning to the program's intended behavior.

**Understand how to write correct software from the ground up.** Writing good specifications of program correctness is essential to showing that there aren't any bugs in the code, but it is also useful for writing good code from the start. Students will develop experience reasoning about correctness as they implement functionality, learning how to write code that establishes and preserves properties and invariants needed to meet a target specification.

**Apply rigorous reasoning to program correctness.** Program verification is the process of mathematically proving that all possible behaviors of a given program adhere to its specification. Students will learn how to use deductive reasoning and state space exploration to realize this goal, and gain experience verifying their own code against logical correctness specifications.

**Learn to use automated tools to aid in verification.** Formally verifying real code is challenging, but given the scale of modern software, can also be tedious and labor-intensive. A number of tools exist for automating key parts of the verification process, notably deductive verification tools and model checkers. Students will learn how to use these tools when developing correct software, and gain experience applying them to non-trivial implementations.

**Understand how verification tools work.** Verification tools are not perfect, and will never be able to scale universally to all software development needs. Understanding the principles and techniques that these tools use is necessary for evaluating when it is appropriate to use a particular tool, and for addressing a tool's limitations when they arise in practice.  There is no textbook for this class, and detailed lecture notes will be posted to the website within a few days after each class. Keep in mind that lecture notes may not contain everything we cover in class. Comments and corrections are welcome, please give them to the course staff on Piazza.

15-414 Bug Catching: Automated Program Verification

Interface (object-oriented programming)

Graphical User Interface (GUI)

Software design pattern

Big-O notation

Search algorithm

Stack (abstract data type)

Queue (abstract data type)

CSCI 0150: Introduction to Object-Oriented Programming and Computer Science Brown University Fall 2022 CS0150 introduces Computer Science via object-oriented design and programming using Java and JavaFX for creating interactive programs with GUIs. Concepts like data structures, algorithms, and computational efficiency are explored. Practical exercises include engaging programming assignments like Doodle Jump and Tetris. The course is designed for all, requiring no prior knowledge. This course introduces students to Computer Science through object-oriented design and programming, using Java and the JavaFX graphics library. You will use these tools for building interactive programs with graphical user interfaces. CS0150 reinforces concepts with practical exercises in weekly lab sessions and with challenging and engaging programming assignments, such as Doodle Jump and Tetris!

There are no prerequisites for CS0150 and the course expects no prior programming experience. This course introduces principles of computer science, emphasizing object-oriented design and programming in Java, an effective modern technique for producing modular, reusable, and internet-aware programs. It also introduces interactive 2D computer graphics, user interface design, some fundamental data structures and algorithms, and computational efficiency. A sequence of successively more complex graphics programs, including Tetris and culminating in a significant final project, helps provide a serious introduction to the field. Each assignment includes an interactive user interface. Therefore, most of the games you program can be played! However, this course is not focused on games nor game design. It uses games as a natural and fun vehicle for learning object-oriented programming and graphical user interfaces.  The course takes an interactive approach to programming assignments and an equally interactive approach to lectures. Andy’s lectures are supplemented by skits performed by the UTAs (Undergraduate Teaching Assistants) to teach course concepts and to make the class fun and enjoyable!  This course is intended for both potential concentrators and those who may take only a single course. No prerequisites and no prior knowledge of programming and mathematics, beyond basic algebra, are required.  

CSCI 0150: Introduction to Object-Oriented Programming and Computer Science