| WEEK | DATE | READING | TOPIC | MATERIALS |
|---|---|---|---|---|
| 1 | Mon, Jun 23 | MSDR, Chapter 1: Prologue: Why Data Science? IMS, 1.2.2: Types of Variable |
Introduction to Data | Lec01 Slides |
| Tue, Jun 24 | R4DS, Chapter 5: Data tidying R4DS, Chapter 3: Data transformation Hadley Wichkam Tidy Data, Journal of Statistical Software (2014) |
Data Structures and Tidy Data | Lec02 Slides Lab01: Welcome to the tidyverse! |
|
| Wed, Jun 25 | MDSR, Chapter 3: A Grammar for Graphics R4DS, Chapter 9: Layers Hadley Wichkam A Layered Grammar of Graphics, Journal of Computational and Graphical Statistics (2010) |
Visualizations, Part I | Lec03 Slides | |
| Thu, Jun 26 | R4DS, Chapter 11: Communication MDSR, Chapter 2: Data Visualization IMS, Chapter 6: Applications: Explore |
Visualizations, Part II | Lec04 Slides Lab02: Bobabase (Databases and Joins) |
|
| Sun, Jun 29 | HOMEWORK 1 DUE | |||
| 2 | Mon, Jun 30 | AMAW, Chapter 3: Geometric Duality MDSR, Chapter 12.2: Dimension Reduction |
Geometry of Data | Lec05 Slides |
| Tue, Jul 1 | Chapter 10 (Principal Components Analysis) of Introduction to Statistical Learning with Applications in R |
PCA | Lec06 Slides Lab03: Boots the House Down, Mama (PCA) PCA Addendum |
|
| Wed, Jul 2 | Review/Catch-up | Lec07 Slides | ||
| Thu, Jul 3 | IN-CLASS ASSESSMENT 01 | LAB CANCELLED | ||
| 3 | Mon, Jul 7 | IMS, Chapter 2: Study Design |
Study Design / Sampling Techniques | Lec08 Slides |
| Tue, Jul 8 | Selected Sections from IMS, “Foundations of Inference” and “Statistical Inference” | Sampling Distributions | Lec09 Slides (PDF Version) Lab04: Care For a Sample? (Sampling Techniques and Distributions) |
|
| Wed, Jul 9 | MDSR, Chapter 9: Statistical Foundations IMS, Chapter 12: Confidence Intervals with Bootstrapping |
Estimation / Confidence Intervals | Lec10 Slides | |
| Thu, Jul 10 | IMS, Chapter 13: Inference with Mathematical Models | Hypothesis Testing, I | Lec11 Slides Lab05: The Count of Monte Carlo (Simulations and Monte Carlo Methods) |
|
| Sun, Jul 13 | MID-QUARTER PROJECT DUE | |||
| 4 | Mon, Jul 14 | Selected Sections from IMS, “Foundations of Inference” and “Statistical Inference” | Hypothesis Testing, II / Introduction to Statistical Modeling | Lec12 Slides |
| Tue, Jul 15 | Section 2.1 (What is Statistical Learning?) of Introduction to Statistical Learning with Applications in R |
More Statistical Modeling (KDE, Regression) | Lec13 Slides Lab06: Don’t Test Me! (Hypothesis Testing) |
|
| Wed, Jul 16 | MDSR, Appendix E: Regression Modeling | Regression, Part I | Lec14 Slides | |
| Thu, Jul 17 | MDSR, Appendix E: Regression Modeling | Regression, Part II | Lec15 Slides Lab07: Rent Due, Lights Due, Mountain Dew (Regression) |
|
| Sun, Jul 20 | HOMEWORK 2 DUE | |||
| 5 | Mon, Jul 21 | MDSR, Appendix E: Regression Modeling | Regression, Part III | Lec16 Slides |
| Tue, Jul 22 | MDSR, Chapter 10: Predictive Modeling IMS, Chapter 9: Logistic Regression MDSR, Chapter 11.1: Non-Regression Classifiers |
Classification | Lec17 Slides (PDF Version) Lab08: Speed Date-Ing (Classification) |
|
| Wed, Jul 23 | Review/Catch-Up | Lec18 Slides | ||
| Thu, Jul 24 | IN-CLASS ASSESSMENT 02 | Lab09: Regular [Expressions] Show (Regular Expressions and Text Wrangling) | ||
| 6 | Mon, Jul 28 | MDSR, Chapter 12.1: Clustering Bhaskaran and Smeeth What is the difference between missing completely at random and missing at random?, International Journal of Epidemiology (2014) |
Clustering / Missing Data | Lec19 Slides |
| Tue, Jul 29 | Emmert-Streib et al. An Introductory Review of Deep Learning for Prediction Models With Big Data, Front. Artif. Intell. 3:4 (2020) Selected Portions of Chapters 4 - 8 from Deep Learning: Foundations and Concepts by Bishop and Bishop AMAW, Chapter 6: Gradient Descent |
Neural Networks / Gradient Descent | Lec20 Slides Lab10: Lab-ubu (Clustering) |
|
| Wed, Jul 30 | A First Course in Causal Inference by Peng Ding Causal Inference for Statistics, Social, and Biomedical Sciences: An Introduction by Rubin and Imbens |
Causal Inference | Lec21 Slides | |
| Thu, Jul 31 | MDSR, Chapter 8: Data Science Ethics | Data Ethics / Closing Remarks | Lec22 Slides Lab11: Bonus Lab |
|
| Fri, Aug 1 | FINAL PROJECT DUE |
PSTAT 100: Data Science Concepts and Analysis
Course Schedule and Calendar
Course Schedule
Note
This page will be updated as we progress through the quarter; please check back regularly for updates!
Textbook Abbreviations and Icon Meanings
- MDSR = Modern Data Science with R
- IMS = Introduction to Modern Statistics, 2nd Ed.
- R4DS = R for Data Science
- ISL = An Introduction to Statistical Learning with Applications in
R - AMAW = All Models are Wrong
- = Lecture
- = Lab
- = Paper