A/B Testing

Bayes' theorem

Causality

Census

Center and Spread

Chance

Charts

Classification

Classifiers

Comparing Distributions

Conditional (computer programming)

Conditional probability

Confidence Intervals

Correlation

Data type

Decisions

Function (computer programming)

Groups

Histograms

Iteration 

Joins

Least Squares

Linear regression

Models

Normal distribution

P-Value

Pivots

Privacy

Regression Inference

Residuals

Sample Means

Sampling

Tables

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 is a multidisciplinary field that combines aspects of Computer science, Statistics, and domain expertise to analyze and interpret data. It uses techniques from [*Machine Learning*](/topic/machine-learning), data mining, and statistical analysis to extract insights and knowledge from data, and uses Data Visualization and communication tools to present the findings.

Data Science

Computer Science

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

CSE 512 Data Visualization University of Washington Spring 2022 This course from the University of Washington dives deep into the principles of data visualization, exploring techniques derived from graphic design, perceptual psychology, and cognitive science. The course enhances understanding of visualization techniques, exposure to common data domains, and offers practical experience in building and evaluating visualization systems.  The world is awash with increasing amounts of data, and we must keep afloat with our relatively constant perceptual and cognitive abilities. Visualization provides one means of combating information overload, as a well-designed visual encoding can supplant cognitive calculations with simpler perceptual inferences and improve comprehension, memory, and decision making. Furthermore, visual representations may help engage more diverse audiences in the process of analytic thinking.

In this course we will study techniques for creating effective visualizations based on principles from graphic design, perceptual psychology, and cognitive science. The course is targeted both towards students interested in using visualization in their own work, as well as students interested in building better visualization tools and systems.

In addition to class discussions, students will complete visualization design and data analysis assignments, as well as a final project. Students will share the results of their final project as both an interactive website and a video presentation. This course is designed to provide students with the foundations necessary for understanding and extending the current state of the art in data visualization. By the end of the course, students will have gained:
- An understanding of key visualization techniques and theory, including data models, graphical perception and methods for visual encoding and interaction.
- Exposure to a number of common data domains and corresponding analysis tasks, including multivariate data, geo-spatial data, and networks.
- Practical experience building and evaluating visualization systems.
- The ability to read and discuss research papers from the visualization literature.  - [The Visual Display of Quantitative Information](http://www.amazon.com/gp/product/0961392142), E. Tufte. Graphics Press, 2001.
- [Envisioning Information](http://www.amazon.com/Envisioning-Information-Edward-R-Tufte/dp/0961392118), E. Tufte. Graphics Press, 1990.
- [Vega-Lite Visualization Notebook Curriculum](https://observablehq.com/@uwdata/data-visualization-curriculum). Jeffrey Heer, Dominik Moritz, Jake VanderPlas, and Brock Craft.
- *Optional*: [Interactive Data Visualization for the Web](http://alignedleft.com/work/d3-book-2e), 2nd Edition. Scott Murray, O'Reilly Press.
    - To read online, use the "O'Reilly (Formerly Safari)" link on the [UW library page](https://guides.lib.uw.edu/c.php?g=341544&p=2299182).
    - Code examples available on [GitHub](https://github.com/alignedleft/d3-book/).

CSE 512 Data Visualization

Data 8: The Foundations of Data Science

Overview

Prerequisites

Learning objectives

Textbooks and other notes

Other courses in Data Science

CSE 163 Intermediate Data Programming

CSE 512 Data Visualization

Courseware availability

Covered concepts