Conditional (computer programming)

Histograms

Pivots

Models

P-Value

Confidence Intervals

Sample Means

Least Squares

Privacy

Bayes' theorem

Data type

Iteration 

Census

Function (computer programming)

Joins

Chance

Comparing Distributions

A/B Testing

Center and Spread

Correlation

Residuals

Classification

Conditional probability

Tables

Charts

Groups

Sampling

Decisions

Causality

Normal distribution

Linear regression

Regression Inference

Classifiers

Data 8: The Foundations of Data Science UC Berkeley Fall 2022 UC Berkeley's course blends inferential thinking, computational thinking, and real-world relevance, offering students hands-on analysis of real-world datasets. It covers critical concepts in computer programming, statistical inference, privacy, and study design. Foundations of Data Science combines three perspectives: inferential thinking, computational thinking, and real-world relevance. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon? The course teaches critical concepts and skills in computer programming and statistical inference, in conjunction with hands-on analysis of real-world datasets, including economic data, document collections, geographical data, and social networks. It also delves into social issues surrounding data analysis such as privacy and study design.  This course does not have any prerequisites beyond high-school algebra. The curriculum and format is designed specifically for students who have not previously taken statistics or computer science courses. Students with some prior experience in either statistics or computing are welcome to enroll and will find much of interest due to the innovative nature of the course. Students who have taken several statistics or computer science courses should instead take a more advanced course. Our primary text is an online book called [Computational and Inferential Thinking: The Foundations of Data Science](http://inferentialthinking.com/). This text was written for the course by the course instructors. A complete PDF of the textbook can be found in the Student Materials Google Drive.

The computing platform for the course is hosted at [data8.datahub.berkeley.edu](file:///Users/jweichert/Desktop/Data%208/data-8.github.io/fa22/data8.datahub.berkeley.edu). Students find it convenient to use their own computer for the course. If you do not have adequate access to a personal computer, we can help you borrow a machine; please contact data8@berkeley.edu.

Data 8: The Foundations of Data Science

Data Science

Boolean

Lambda

Regular Expressions

Intermediate Representations

Register Allocation

Garbage Collection

Type Checking

Pattern Matching

Unary Operations

Binary Operations

Function Pointers

OCaml

S-Expression

Handling Errors

First-Class Functions

Constant Folding

Compiler

Types

Correctness and Testing

Naming Expressions

Tail Call Optimization

Functional Programming (FP)

Custom Types

Pairs

Parsing

Optimization

CS 164: Programming Languages and Compilers UC Berkeley Fall 2022 Explores how compilers translate high-level languages into machine-understandable code, offering practical experience with developing compilers for various languages. Also covers reasoning about compiler correctness and understanding runtime errors.  Have you ever wondered why C programs seem to run faster than Python programs? Have you ever been confused by an error message and wondered why Java couldn’t understand your program? In CS 164, we’ll learn how compilers take in code in one language and produce code in another; in particular, we’ll learn how to translate high-level languages into code that your computer’s processor can understand. We’ll get hands-on practice developing compilers for a series of increasingly complex languages. Along the way, we’ll learn some general best practices for developing and testing complex software systems. After completing this course, students will be able to:

- Develop programs to translate between different programming languages
- More broadly, develop programs that operate on programs
- Explain the relationship between compilers and interpreters, and the differences between them
- Understand the tools they use to run programs—and the errors those tools produce
- Assign meaning to high-level language constructs
- Implement high-level language constructs
- Reason about what it means for a compiler to be correct
- Reason about undefined behavior
- Read and write assembly language
- Write a parser - [CS 61B. Data Structures](https://www2.eecs.berkeley.edu/Courses/CS61B/)
- [CS 61C. Machine Structures](https://www2.eecs.berkeley.edu/Courses/CS61C/)

### Course Culture

Students taking CS 164 come from a wide range of backgrounds. We hope to foster an inclusive and safe learning environment based on curiosity rather than competition. All members of the course community—the instructor, students, and GSIs—are expected to treat each other with courtesy and respect. Some of the responsibility for that lies with the staff, but a lot of it ultimately rests with you, the students. There is no textbook for the course; all readings will be provided digitally.

CS 164: Programming Languages and Compilers

Compilers

Programming Languages

Java (programming language)

Functional Decomposition

Control Structures

Data Abstraction

Documentation and Readability

Static methods

`print` statements

Scanners

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

String (computer science)

Cumulative algorithm

Fencepost problems

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

Computer Programming

Conditional (computer programming)

3 courses cover this concept

Data 8: The Foundations of Data Science

CS 164: Programming Languages and Compilers

CSE 142 Computer Programming I