Tables

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

Charts

Groups

Conditional (computer programming)

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

Sequences

Tree (data structure)

Generators

Inheritance (object-oriented programming)

Linked list

Scheme

Interpreters

Aggregation

Higher-order function (HOF)

Single View Metrology

Computer Science

Environments

Functional Abstraction

Tree Recursion

Data Abstraction

Iterators

Object (computer science)

Efficiency

Data Examples

Calculator

Control

Higher-Order Environments

Recursion (computer science)

Containers

Mutability

Gates & Circuits

Ethics and Bias in Computer Science

Scheme Lists

String (computer science)

CS 61A: Structure and Interpretation of Computer Programs UC Berkeley Fall 2022 A course that emphasizes software abstraction, equipping students with the skills to manage program complexity. Students will primarily use Python, with additional exposure to Scheme and SQL. The CS 61 series is an introduction to computer science, with particular emphasis on software and on machines from a programmer's point of view.

1. CS 61A concentrates on the idea of abstraction, allowing the programmer to think in terms appropriate to the problem rather than in low-level operations dictated by the computer hardware.
1. CS 61B deals with the more advanced engineering aspects of software, such as constructing and analyzing large programs.
1. CS 61C focuses on machines and how they execute programs.

In CS 61A, we are interested in teaching you about programming, not about how to use one particular programming language. We consider a series of techniques for managing program complexity, such as functional programming and object-oriented programming.

CS 61A primarily uses the Python 3 programming language. Python is a popular language in both industry and academia. It is also particularly well-suited to the task of exploring the topics taught in this course. It is an open-source language developed by a large volunteer community that prides itself on the diversity of its contributors.

Mastery of a particular programming language is a very useful side effect of CS 61A. However, our goal is not to dictate what language you use in your future endeavors. Instead, our hope is that once you have learned the concepts involved in programming, you will find that picking up a new programming language is but a few days' work. In fact, you will be asked to learn at least two other languages, Scheme and SQL, during the course.

A complete list of lecture topics, readings, and assignments appears in the [lecture schedule](https://cs61a.org/).  Math 1A is listed as a corequisite for CS 61A. That is, it may be taken concurrently. Math 10A or Math 16A are also fine. It is possible to take CS 61A without knowing or learning calculus. Knowledge of calculus concepts will never be required to complete any assignments. However, taking calculus is a great way to brush up on the arithmetic and algebra that appear regularly in CS 61A.

There is no formal programming-related prerequisite for CS 61A, but taking the course without any prior programming experience is typically very challenging. Students who take the course without prior programming experience typically must work substantially harder to master the material and tend to receive lower final grades in the course. Most CS 61A students have had significant prior programming experience. A typical background includes at least one course equivalent to CS 10 or a score of 3 or above on the AP Computer Science A exam.

If you find it challenging to complete all of the required coursework in the first three weeks, you are **strongly recommended** to take another course first. You'll likely have a better experience taking 61A later. Taking CS 61A one semester later is unlikely to affect your degree progress or otherwise put you behind, but continuing to struggle in CS 61A despite this recommendation can lead to difficult academic circumstances that can affect your future opportunities. It's better to finish CS 61A with strong mastery of the course material, even if that means delaying by a semester, than to rush through it. The online textbook for the course is [Composing Programs](http://composingprograms.com/), which was created specifically for this course, based on the classic textbook [Structure and Interpretation of Computer Programs](https://mitpress.mit.edu/sites/default/files/sicp/index.html). Readings for each lecture appear in the course schedule. We recommend that you complete the readings before attending lecture.

Lots of additional study materials including past exams appear on the [Resources](https://cs61a.org/resources/) page.

CS 61A: Structure and Interpretation of Computer Programs

Computer Programming

Tables

2 courses cover this concept

Data 8: The Foundations of Data Science

CS 61A: Structure and Interpretation of Computer Programs