Data 8: Foundations of Data Science
InclusionBridge, Bridge to Data Fundamentals 23-24
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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.
Announcements
- 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
- Lecture
- 6 Census
- Embedded Notebook
- Reading: 6.3, 6.4
- Lab Lab 03: Data Types, creating and Extending Tables
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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