Control Structures

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

Data structure

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)

Functional Decomposition

Data Abstraction

Documentation and Readability

Static methods

Types

`print` statements

Scanners

Conditional (computer programming)

While loop

Array (data structure)

Object-Oriented Programming (OOP)

ArrayList

Java

Expressions

Scope

Parameters

`Math` class

Information flow

Pseudorandomness

Line-based file input

reference semantics

Constructors

Polymorphism (computer science)

Escape sequences

`for` loops

Class constants

Return values

Chaining

Sentinel loops

Token-based file input

Array Traversals

Inheritance (object-oriented programming)

Fields

`println`

Variable (computer_science)

Nested `for` loops

Cumulative algorithm

Fencepost problems

Boolean

File output

Array Shifting

Instance Methods

Critters

CSE 142 Computer Programming I University of Washington Summer 2022 This introductory course teaches programming using Java. Students learn computational problem-solving techniques, functional decomposition, control structures, data abstraction, and code documentation. No prior programming experience is required. This course provides an introduction to programming using the Java programming language. We will explore common computational problem-solving techniques useful to computer scientists, but also to anyone who has large data sets, repetitive processes or other needs for computation. No prior programming experience is assumed, although students should know the basics of using a computer (e.g., using a web browser and a text editor) and should be comfortable with math through Algebra 1. Students with significant prior programming experience should consider skipping CSE 142 and taking [CSE 143](https://cs.washington.edu/143). CSE 142 is organized around five key learning objectives. At the end of the course, students will be able to do the following:

1. Functionality/Behavior: Write functionally correct Java programs that meet a provided specification and/or solve a specified problem
1. Functional Decomposition: Break down problems into subproblems that are modular and reusable, and define methods to represent those subproblems
1. Control Structures: Select and apply control structures (e.g. methods, loops, conditionals) to manage the flow of control and information in programs
1. Data Abstraction: Select and apply basic data abstractions (e.g. variables, parameters, arrays, classes) to manage and manipulate data in programs
1. Documentation and Readability: Define programs that are well-written, readable, maintainable, and conform to established standards  Recommended textbook: [*Building Java Programs* by Reges and Stepp (5th edition)](https://courses.cs.washington.edu/courses/cse142/22su/textbook.html)

CSE 142 Computer Programming I

Control flow

Control Structures

2 courses cover this concept

CSE 163 Intermediate Data Programming

CSE 142 Computer Programming I