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Data 8: Foundations of Data Science

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Acknowledgements

This course is adopted from Data 8, “The Foundations of Data Science” course taught to first-year students at UC Berkeley. All materials for the course, including the textbook and assignments, are available for free online under a Creative Commons license. Textbook: Computational and Inferential Thinking: The Foundations of Data Science is a free online textbook that includes interactive Jupyter notebooks and public data sets for all examples. Course Materials: The Embedded Demo, Lab and Homework Notebooks as well as linked references all come from publicly available materials used in the course during the Spring 2020 semester at UC Berkeley as well as materials used in the self-paced Data 8X course on EdX.

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Jan 1 · 0 min read
  • Data Fundamentals classes have started! .

Module 1

Lecture
1 Introduction
Embedded Notebook
Reading: 1.1, 1.2, 1.3
Lab Lab 01: Expressions
Lecture
2 Cause and Effect
Embedded Notebook
Reading: 2
Homework Homework 01

Module 2

Lecture
3 Tables
Embedded Notebook
Reading: 3, 4
Lab Lab 02: Table Operations

Module 3

Lecture
4 Data Types
Embedded Notebook
Reading: 5
Lecture
5 Building Tables
Embedded Notebook
Reading: 6.1, 6.2
Homework Homework 02

Module 4

Module 5

Lecture
7 Charts
Embedded Notebook
Reading: 7, 7.1
Lecture
8 Histograms
Embedded Notebook
Reading: 7.2, 7.3
Homework Homework 03

Module 6

Lecture
9 Functions
Embedded Notebook
Reading: 8.1
Lab Lab 04: Functions and Visualization

Module 7

Lecture
10 Groups
Embedded Notebook
Reading: 8.2, 8.3
Lecture
11 Pivots and Joins
Embedded Notebook
Reading: 8.4
Homework Homework 04

Module 8

Lecture
12 Table Examples
Embedded Notebook
Reading: 8.5
Lecture
13 Conditionals and Iteration
Embedded Notebook
Reading: 9.2
Lab Lab 05: Conditional Statements, Iteration, Tables

Module 9

Lecture
14 Chance
Embedded Notebook
Reading: 9.2, 9.3, 9.4
Homework Homework 05

Module 10

Lecture
15 Sampling
Embedded Notebook
Reading: 9.5, 10

Module 11

Lecture
16 Models
Embedded Notebook
Reading: 10.2, 10.3, 10.4
Homework Homework 06

Module 12

Lecture
17 Comparing Distributions
Embedded Notebook
Reading: 11.1, 11.2
Lab Lab 06: Assessing Models
Lecture
18 Decisions and Uncertainty
Embedded Notebook
Reading: 11.3, 11.4

Module 13

Lecture
19 A/B Testing
Embedded Notebook
Reading: 12.1
Lab Lab 07: A/B Testing

Module 14

Lecture
20 Causality
Embedded Notebook
Reading: 12.2, 12.3
Homework Homework 07

Mid Course Review

Test
Test Test

Module 16

Lecture
21 Confidence Intervals
Embedded Notebook
Reading: 13, 13.1, 13.2
Lecture
22 Interpreting Confidence
Embedded Notebook
Reading: 13.3, 13.4
Homework Homework 08

Module 17

Lecture
23 Center and Spread
Embedded Notebook
Reading: 14, 14.1, 14.2

Module 18

Lecture
24 The Normal Distribution
Embedded Notebook
Reading: 14.3, 14.4
Lab Lab 08: Sample Mean

Module 19

Lecture
25 Sample Means
Embedded Notebook
Reading: 14.5
Lecture
26 Designing Experiments
Embedded Notebook
Reading: 14.6
Homework Homework 09

Module 20

Lecture
27 Correlation
Embedded Notebook
Reading: 15, 15.1
Lecture
28 Linear Regression
Embedded Notebook
Reading: 15.2

Module 21

Lecture
29 Least Squares
Embedded Notebook
Reading: 15.3, 15.4
Homework Homework 10

Module 22

Lecture
30 Residuals
Embedded Notebook
Reading: 15.5, 15.6
Lab Lab 09: Regression

Module 23

Lecture
31 Regression Inference
Embedded Notebook
Reading: 16
Homework Homework 11

Module 24

Lecture 1
32 Classification
Embedded Notebook
Reading: 17, 17.1, 17.2, 17.3
Lecture 2
33 Classifiers
Embedded Notebook
Reading: 17.4
Homework Homework 12
Lecture 3
34 Privacy
Embedded Notebook

Module 25

Lecture
35 Numpy Fundamentals
Embedded Notebook
Lecture
36 Introduction to Pandas, Part 1
Embedded Notebook
Reading: 1.1, 1.2
Lab Lab 11

Module 26

Lecture
37 Introduction to Pandas, Part 2
Embedded Notebook
Reading: 1.3, 1.4
Lecture
38 Introduction to Pandas, Part 3
Embedded Notebook
Lab Lab 12

Module 27

Lecture
39 Join Review
Embedded Notebook
Reading: 2
Lecture
40 Data Cleaning and EDA
Embedded Notebook
Reading: 3, 4, 5
Lab Lab 13

Week 28

Project Work
Capstone Project
Project Notebook

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