Algorithmic fairness

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

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 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

Free Speech

Social Connection and Time Well-Spent

Privacy

Government Censorship and Surveilence

Memes and Viral Content

Framing Society and Identity

Software Risks

The Optimization Mindset, Tech Policy, Big Tech

Education

Labor and automation

Algorithmic bias

CS 195 Social Implications of Computer Technology UC Berkeley Fall 2022 This course is an in-depth discussion on the societal impacts of computer technology. The course content is drawn from various fields including sociology, philosophy, economics, and public policy. Students are expected to explore topics like privacy, algorithmic bias, tech policy, and the implications of tech on education and jobs. CS 195 is a discussion-intensive course about the social implications of computer technology. The purpose of this course is to help students make informed and thoughtful choices about their careers, participation in society, and future development activities. Readings and lecture topics are drawn from a range of fields that together seek to describe our contemporary global society: sociology, philosophy, economics, public policy, etc.   

CS 195 Social Implications of Computer Technology

Computer Ethics

Optimization

Algorithmic decision-making

Accountability

Surveillance

Civil liberties

Artificial intelligence

Platforms

Social media

Misinformation

Blockchain

Cryptocurrency

Decentralization

CS 182: Ethics, Public Policy, and Technological Change Stanford University Winter 2023 This course examines the intersections of philosophy, public policy, social science, and engineering in the context of recent computing technology and platforms. Key areas of focus include algorithmic decision-making, data privacy, AI, the influence of private computing platforms, and issues of diversity in tech. Students need to have completed CS106A. Examination of recent developments in computing technology and platforms through the lenses of philosophy, public policy, social science, and engineering.  Course is organized around five main units: algorithmic decision-making and bias; data privacy and civil liberties; artificial intelligence and autonomous systems; the power of private computing platforms; and issues of diversity, equity, and inclusion in the technology sector.  Each unit considers the promise, perils, rights, and responsibilities at play in technological developments.  ### Menu of Topics

The course will focus on six frontiers that (a) you are likely to play a role in shaping over the next decade and (b) where engagement with material from philosophy, social science, and public policy is likely to be helpful.

1. Algorithmic Decision-Making 
2. Political Economy of Tech Sector
3. Data Collection, Privacy, and Civil Liberties 
4. Artificial Intelligence and Autonomous Systems
5. Power of Private Platforms
6. Blockchain, Decentralization, and Cryptocurrency
 CS106A ### Cross-Cutting Themes

Throughout the course, we will foreground two cross-cutting themes:

- First, issues of diversity, equity, and inclusion which focus our attention on who has power, voice, and influence in shaping the design and regulation of technology.
- Second, issues of political economy which focus our attention on the interplay of markets and governments in shaping what technology gets built, who benefits from that technology, and how any harmful impacts are/are not mitigated.

CS 182: Ethics, Public Policy, and Technological Change

Fairness (machine learning)

Algorithmic fairness

3 courses cover this concept

CSE 163 Intermediate Data Programming

CS 195 Social Implications of Computer Technology

CS 182: Ethics, Public Policy, and Technological Change