Sorting algorithm

Abstract Data Type (ADT)

Stack (abstract data type)

Queue (abstract data type)

Map (higher-order function)

Algorithmic Analysis

Case Analysis

Algorithm Analysis

Asymptotic Analysis

Recursive Algorithm Analysis

Binary Search Tree (BST)

Hashing

AVL Trees

Tree (data structure)

Graphs

Breadth-first search (BFS)

Depth-first search (DFS)

Topological sorting

Graph traversal

Minimum Spanning Tree (MST)

Disjoint Sets

Dynamic programming

Graph Modelling

P/NP

Computer memory

Shortest paths

Heap (data structure)

CSE 373 Data Structures and Algorithms University of Washington Summer 2022 This course focuses on common data structures and algorithms. It integrates theoretical understanding with practical exercises, preparing students for software-related roles and industry technical interviews. Programming projects, unit testing, and source control techniques are emphasized.  In this course, you will gain a strong theoretical and conceptual understanding of common data structures and algorithms, as well as how to apply them within larger programming projects.

Specific topics we will cover include:

- **Data structures and ADTs**: lists, stacks, queues, sets, dictionaries, linked lists, arrays, trees, balanced trees, AVL trees, hash tables, priority queues, binary heaps, and disjoint sets.
- **Graphs and graph algorithms**: graph search, shortest path, and minimum spanning trees.
- **Algorithm analysis**: asymptotic analysis, and P and NP complexity classes.
- **Sorting** and divide-and-conquer.

This course is designed to have a practical component to help you gain basic familiarity with techniques used within industry. In particular, you’ll be asked to:

- Work on **programming projects** and integrate your work in an existing codebase.
- Learn how to use an **industrial-strength IDE**.
- Learn techniques for **checking correctness**: writing unit tests with JUnit, designing and checking invariants, etc.
- Learn how to collaborate on a single code base using **source control** repositories.

Finally, this course emphasizes the importance of making and justifying design decisions. Taken together, all of the above skills are chosen to set you up for success in a software-related role. In fact, this course is typically regarded as useful preparation for industry and technical interviews. [CSE 143](https://courses.cs.washington.edu/courses/cse143/), or similar Java programming experience. 

CSE 373 Data Structures and Algorithms

Data Structures and Algorithms

Schemas

Data models

DB systems

Select-From-Where

Joins

Set operators

Subqueries

Grouping

Aggregations

Nested queries

Building Indices

Query Optimization

Systems Design

IO Model

Database index

CS 145 Data Management and Data Systems Stanford University Fall 2022 This course primarily focuses on the utilization of databases in applications, scaling for large datasets, and effective data systems design. It explores relational data models, SQL, and schema design, concluding with a hands-on SQL+Python project. The course requires CS 103 and CS 107 or equivalent as prerequisites. This course covers how to use databases in applications, first principles on how to scale for large data sets and how to design good data systems.

A few key topics:

— Introduction to relational data model, relational database engines, and SQL.

— How to scale systems for large data sets on servers and server clusters

— How to design good schemas based on dependencies, normal forms so we build and evolve good applications. This will include indexes, views and transactions.

The class will culminate in a hands-on programming project in SQL+Python — a key part of the course — where you will query, visualize and predict from terabytes of data on [BigQuery](https://cloud.google.com/bigquery/), a popular cloud database part of Google Cloud Platform.  CS 103 and CS 107 (or equivalent) 

CS 145 Data Management and Data Systems

Database Systems

Memory hierarchy

Matrix Multiplication

Shared Memory Parallelism

Roofline and Performance Modeling

Sources of Parallelism and Locality

Communication-avoiding matrix multiplication

Data Parallel Algorithms

CUDA (Compute Unified Device Architecture)

Graphics Processor (GPU)

Distributed Memory Machines and Programming

Advanced MPI

Collective Communication Algorithms

UPC++: Partitioned Global Address Space Languages

Domain Specific Languages (Halide)

Distributed Data Structures (BCL)

Parallel Matrix Multiply

Dense Linear Algebra

Sparse-Matrix-Vector-Multiplication

Iterative Solvers

Structured Grids

Machine learning

Supervised learning

Deep learning

Unsupervised learning

Graph Partitioning

Fast Fourier transform

Graph Algorithms

Cloud Computing

Big Data Processing

High Performance Computing (HPC)

Dynamic Load Balancing

Hierarchical Methods for the N-Body Problem

Search algorithm

Computational Biology

Quantum Computing

CS 267: Applications of Parallel Computers UC Berkeley Spring 2020 The course addresses programming parallel computers to solve complex scientific and engineering problems. It covers an array of parallelization strategies for numerical simulation, data analysis, and machine learning, and provides experience with popular parallel programming tools. CS267 was originally designed to teach students how to program parallel computers to efficiently solve challenging problems in science and engineering, where very fast computers are required either to perform complex simulations or to analyze enormous datasets. CS267 is intended to be useful for students from many departments and with different backgrounds, although we will assume reasonable programming skills in a conventional (non-parallel) language, as well as enough mathematical skills to understand the problems and algorithmic solutions presented. CS267 satisfies part of the course requirements for the Designated Emphasis ("graduate minor") in [Computational Science and Engineering](http://www.google.com/url?q=http%3A%2F%2Fcitris-uc.org%2Finitiatives%2Fdecse%2F&sa=D&sntz=1&usg=AOvVaw1fRFGvIaIYhkwE3kp4J1nf).

While this general outline remains, a large change in the computing world started in the mid 2000's: not only are the [fastest computers](http://www.google.com/url?q=http%3A%2F%2Fwww.top500.org%2F&sa=D&sntz=1&usg=AOvVaw1SiByfUjOpTR6ctURAV2Ef) parallel, but nearly all computers are becoming parallel, because the physics of semiconductor manufacturing will no longer let conventional sequential processors get faster year after year, as they have for so long (roughly doubling in speed every 18 months for many years). So all programs that need to run faster will have to become parallel programs. (It is considered very unlikely that compilers will be able to automatically find enough parallelism in most sequential programs to solve this problem.) For background on this trend toward parallelism, click [here](http://www.google.com/url?q=http%3A%2F%2Fwww.eecs.berkeley.edu%2FPubs%2FTechRpts%2F2006%2FEECS-2006-183.html&sa=D&sntz=1&usg=AOvVaw2XoPTheTzKVlyjp1G_YcM5).

Students in CS267 will get an overview of the parallel architecture space, gain experience using some of the most popular parallel programming tools, and be exposed to a number of open research questions. The lectures will also cover a broad set of parallelization strategies for applications covering numerical simulation and data analysis to machine learning.   

CS 267: Applications of Parallel Computers

Parallelism and Distributed Systems

Abstract Data Structures

Recursion (computer science)

C++ Classes

Memory management

Implementation-level abstractions

Linked data structures

Python

Grids

Set (abstract data type)

Backtracking

Pointer (computer programming)

Binary Search Trees

String (computer science)

Fractals

Dynamic Memory

Implementing `StackInt`

Linked list

Huffman Coding

Graph Shortest Path Algorithms

Big-O notation

Procedural Recursion

Vector (C++)

CS 106B Programming Abstractions Stanford University Winter 2023 This course helps transition from coding to problem-solving using computers. The course explores techniques, tools, and models for problem-solving across disciplines using C++. Prior programming experience is assumed. A nonprofit needs to assign tasks to its volunteers so they get completed as fast as possible. A political scientist wants to determine how to allocate city council seats based on a popular vote. A climatologist wants to estimate how many trees are in a forest. A city planner wants to see whether a proposed development will be underwater as sea levels rise. A physicist wants to know why perturbations to a system cause cascading effects.

How can we use computing power to answer these questions?

This course is about transitioning from “I know how to write programs” to “I know how to solve problems with computers.” Over the course of ten weeks, we’ll explore an array of techniques, tools, and perspectives useful for modeling and solving problems. We’ll explore recursion and see how it can be used both to model the intricacies of nature and to optimally allocate resources. We’ll develop a rich vocabulary of structures that can both capture the position of a dancer in space and compress a data file. And we’ll see how to put these techniques to use in problems drawn from a range of disciplines. By the time you’ve completed this course, you’ll learn how to look at problems in fundamentally different ways and how to use those perspectives to create clean and elegant computational solutions.  CS106B assumes that you have a familiarity with programming at the level of CS106A or the AP Computer Science exam. Check out [our course placement page](https://web.stanford.edu/class/archive/cs/cs106b/cs106b.1234/course_placement) for more information about deciding whether CS106B is right for you. As always, feel free to get in touch with us if you have any questions.

A note: although this class uses C++, this class is primarily designed to teach abstraction, recursion, and algorithmic analysis. If you already know those topics and just want to learn C++, you may want to opt to take CS106L instead of CS106B. The required reading for this course is Eric Roberts' *Programming Abstractions in C++*. You can purchase a copy at the bookstore using this link:

> https://www.bkstr.com/stanfordstore/search/keyword/programming%2520abstractions

You can also use this online version, which is a 2012 draft version of the book.

> https://web.stanford.edu/dept/cs_edu/resources/textbook/

We assume that the majority of you have no prior programming experience in C++, and this textbook is a great resource to use at the start of the quarter as you’re transitioning into the language.

A helpful note from the School of Engineering:

> *“All students should retain receipts for books and other course-related expenses, as these may be qualified educational expenses for tax purposes. If you are an undergraduate receiving financial aid, you may be eligible for additional financial aid for required books and course materials if these expenses exceed the aid amount in your award letter. For more information, review your award letter or visit the [Student Budget website](https://financialaid.stanford.edu/undergrad/budget/index.html).”*

CS 106B Programming Abstractions

Computer Programming

Java (programming language)

Class (computer programming)

Software testing

References

List (abstract data type)

SLLists

Nested Classes

Debugging

Array (data structure)

Array Resizing

Inheritance (object-oriented programming)

Extends

Higher-order function (HOF)

Exception handling

Object Methods

Red Black Trees

Tree traversal

Multi-Dimensional Data

Prefix Operations and Tries

Reductions and Decomposition

Quick Sort

Algorithmic Bounds

Data structure

Scope

Kolmogorov complexity

Sentinel Nodes

DLLists

ALists

SLists

Implements

Casting

Subtype Polymorphism

Iterators

Asymptotics

B-tree

Priority queue (PQ)

Range Searching

Software Engineering

Radix Sorts

Compression

P=NP

Static

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

File I/O

Hash table

Object (computer science)

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

Interface

Encapsulation

`ArrayIntList`

pre/post conditions

Reference Semantics

List Nodes

`LinkedIntList`

Recursive Tracing

Grammars

Binary Search

Recursive Backtracking

Comparable

Generics

Priority Queues

Huffman

Resizing

Abstract Classes

Computational complexity

CSE 143 Computer Programming II University of Washington Summer 2022 This course is a continuation of CSE 142, focusing on manipulating data, implementing data structures, and learning about algorithms in Java. It delves into abstract data types, recursion, inheritance, and more.  This course is a continuation of CSE 142. While CSE 142 focused on topics of program control (loops, conditionals, methods, parameter passing, etc.), CSE 143 focuses on topics ofb manipulating data. Topics include: abstract data types (ADTs), lists, stacks, queues, linked lists, binary trees, recursion, interfaces, inheritance, and encapsulation. Students will also learn about the notion of complexity and how to evaluate performance trade-offs of classic algorithms (such as sorting and searching) and classic data structures (such as lists, sets, and maps). The course will include a mixture of implementing data structures and using components from the Java Collections Framework.  ## Textbook

Our optional textbook for CSE 143 this quarter will be the following:

Reges/Stepp. Building Java Programs: A Back to Basics Approach, 5th edition. ISBN 978-0135471944

- [Building Java Programs: A Back to Basics Approach (authors' web site)](http://www.buildingjavaprograms.com/)

This book was written specifically for this course and makes a valuable supplement to the lecture presentations.

## Code Files

The textbook includes many sample programs. You can access a full set of [code files](http://www.buildingjavaprograms.com/code_files/) that is organized chapter by chapter. This will allow you to download any of the programs and run them on your own computer.

## Video Notes

New copies of the textbook come with an access code to watch "Video Notes." These videos supplement the lecture and book material by walking through more examples in detail. You can access the video notes at the Pearson [VideoNotes](http://www.pearsonhighered.com/regesstepp/) web site.

CSE 143 Computer Programming II

Interface (object-oriented programming)

Graphical User Interface (GUI)

Loops

Software design pattern

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

Sorting algorithm

8 courses cover this concept