Class (computer programming)

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 structure

Data Visualization

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

Types

struct (C programming language)

Initialization

References

Streams

Containers

Iterators

Pointer (computer programming)

Template Classes

Const Correctness

Template Functions

Function (computer programming)

Lambda

Operators

Special Member Functions

Move Semantics

`std::optional`

Type Safety

Resource acquisition is initialization (RAII)

Smart pointer

Building C++ Projects

CS 106L Standard C++ Programming Stanford University Winter 2023 This is a companion course to CS106B/CS106X, diving into the modern C++ language. It covers some of the most exciting features of C++, including modern patterns that give it beauty and power. It is an intermediate-level course assuming familiarity with basic programming concepts. **CS 106L** is a companion class to CS106B/CS106X that explores the modern C++ language in depth. We'll cover some of the most exciting features of C++, including modern patterns that give it beauty and power.   

CS 106L Standard C++ Programming

Java (programming language)

Software testing

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

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

Computer memory

Modules

Compilation

Makefiles

File I/O

Buffering

POSIX I/O

System Calls

Directories

C++ Programming

Introduction

Const

Constructors

new/delete

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

Tkinter

Arithmetic Expressions

Variable (computer science)

Boolean

Tuple

Array slicing

Control flow

For loop

Dictionary (data structure)

Modular programming

File system

While loop

Assertion (software development)

API (Application Programming Interface)

Filter (higher-order function)

Event loop

Graphical User Interface (GUI)

CICS 110: Foundations of Programming  The University of Massachusetts Amherst Spring 2023 This course introduces computer programming and problem-solving. Students learn using a modern language, covering variables, data types, branching, functions, classes, and methods. Emphasis is on real-world problem translation, computational understanding, and debugging. An introduction to computer programming and problem solving using computers. This course teaches you how real-world problems can be solved computationally using programming constructs and data abstractions of a modern programming language. Concepts and techniques covered include variables, expressions, data types, objects, branching, iteration, functions, classes, and methods. We will also cover how to translate problems into a sequence of instructions, investigate the fundamental operation of a computational system and trace program execution and memory, and learn how to test and debug programs. No previous programming experience required. The objective of this course is to introduce the fundamentals of computing and programming using a general-purpose programming language from a modern perspective. This includes understanding the operation of a machine from a programming language perspective and what it means to execute a whole program as well as its individual parts, how to solve problems using constructs that a programming language provides such as variables, data types, objects, branching, iteration, functions, and classes, and how to write programs that receive data from various sources, process that data, and produce output in various forms.

## Learning Outcomes

At the completion of this course you will be able to:

- Read and write programs to solve non-trivial programs using the Python programming language.
- Describe fundamental units of computation and program structure.
- Translate real-world problems into computational solutions.
- Solve problems using a general-purpose programming language and the tools it provides such as variables, data types, objects, branching, iteration, functions/methods, and classes.
- Design and implement whole programs and functions to solve computational problems using top-down and bottom-up techniques.
- Describe application programming interfaces (API) and use APIs available from existing objects and libraries to solve problems.
- Translate data from and into various formats that are in computer memory, a graphical interface, a file, from a remote API on the web, or other data resources.
- Use console-based or graphical interfaces to learn about input/output to move data into and out of a program.
- Use modules and classes to organize data and functions.
- Explain the notion of a machine and how it relates to the execution of a general-purpose programming language.
- Explain how programs and their data are represented in a computer and build mental models and use diagrams of program and function execution and data stored in memory.
- Use basic debugging techniques such as “print debugging” and assert statements to determine the cause of logical programming errors and show the correctness of a program and its implementation.
- Describe programs using proper documentation techniques to communicate implementation details at various levels of granularity. R1 (or a score of 20 or higher on the math placement test Part A), or one of the following courses: MATH 101&102 or MATH 104 or MATH 127 or MATH 128 or MATH 131 or MATH 132. ## Textbook

The following textbook is required:

- Programming in Python 3, an interactive textbook from [zyBooks](https://learn.zybooks.com/) designed specifically for this course.

The information you need to subscribe to this book can be found on the course LMS (e.g., Moodle).

Here are some additional textbook recommendations freely available online. You may consider looking at these as supplemental material:

- Automate the Boring Stuff with Python, Al Sweigert, https://automatetheboringstuff.com
- A Byte of Python, https://www.gitbook.com/book/swaroopch/byte-of-python/details
- Dive into Python, Mark Pilgrim, http://getpython3.com/diveintopython3
- Learn python the hard way, Zed Shaw, http://learnpythonthehardway.org/book
- Python Practice Book, Anad Chitpothu, http://anandology.com/python-practice-book

CICS 110: Foundations of Programming 

Expressions

Dictionaries

Search algorithm

Decomposition

Python Control Flow

Graphics

List of Lists

Internet

Python Variables

Graphical Animation

Nested Structures

Function Variables

CS 106A: Programming Methodologies Stanford University Spring 2023 This is an introductory course offering step-by-step instruction on key programming concepts with Python, including control flow, decomposition, data science, and more. Students are guided through exercises to learn by doing. ### How Does This Class Work?

- Start the very beginning
- Careful to go step by step, little exercises every day
- Like swimming, you can't just read about it
- Learn by doing
- "reps" - Nick's favorite word
- Technology to practice ideas right away
- In just a couple weeks .. amazed at what you can do
- Partly because computers are powerful
- Partly because coding is easier than you think  No prior programming experience required. There is one required text for this class, entitled Karel the Robot Learns Python. It's a short interactive tutorial that introduces the major concepts in programming in the context of an extremely simple robot world. This text is available for free on the class website. In addition to this text, we will also have reference material on Python available via the class webpage.

CS 106A: Programming Methodologies