PSTAT 100: Data Science Concepts and Analysis

Course Schedule and Calendar

Instructor

Quarter

Ethan Marzban

Spring 2024

Course Schedule

Note

This page will be updated as we progress through the quarter; please check back regularly for updates!

Note

Please try to complete the readings before coming to the specified lecture/starting the specified lab.

Textbook Abbreviations and Emoji Meanings

• R4DS = R for Data Science
• I2R = An Introduction to R
• IMS = Introduction to Modern Statistics, 2nd Ed.
• 💻 = Lab
• 🧑‍🏫 = Lecture
• 📖 = Textbook Reading
• 📄 = Paper Reading

WEEK	DATE	READING	TOPIC	MATERIALS
1	Mon, Apr 1	Optional Reading: 📖 R4DS, Chapter 2: Workflow basics 📖 R4DS, Chapter 27: A field guide to base R 📖 I2R, Chapter 2: Some R Basics 📖 I2R, Chapter 3: Data in R	💻 Lab00: Intro to R and Dataframes	Intro to R: (click here) Intro to Dataframes: (click here) Intro to R: (click here) Intro to Dataframes: (click here)
	Tue, Apr 2	📖 R4DS, 1.2: First Steps 📖 IMS, 1.2.2: Types of Variables	🧑‍🏫 Lec01: Intro to Datascience	🛝 Lec00 Slides 🛝 Lec01 Slides
	Thu, Apr 4	📖 R4DS, Chapter 5: Data tidying 📖 R4DS, Chapter 3: Data transformation 📄 Hadley Wichkam Tidy Data, Journal of Statistical Software (2014)	🧑‍🏫 Lec02: Tidy Data	🛝 Lec02 Slides
2	Mon, Apr 8	📖 R4DS, Chapter 19: Joins	💻 Lab01: Tidy Data and Databases
	Tue, Apr 9	📖 R4DS, Chapter 1: Data visualization 📖 R4DS, Chapter 9: Layers 📄 Hadley Wichkam A Layered Grammar of Graphics, Journal of Computational and Graphical Statistics (2010)	🧑‍🏫 Lec03: Graphics, Part I	🛝 Lec03 Slides
	Wed, Apr 10			Lab01 Due Extended to Friday
	Thu, Apr 11	📖 R4DS, Chapter 11: Communication 📖 Chapters 2 - 5 of Fundamentals of Data Visualization, by Claus Wilke 📖 IMS, Chapter 6: Applications: Explore	🧑‍🏫 Lec04: Graphics, Part II	🛝 Lec04 Slides
	Sun, Apr 14			Homework 01 Due
3	Mon, Apr 15		💻 Lab02: Statistical Visualizations
	Tue, Apr 16	📖 R4DS, Chapter 10: Exploratory Data Analysis	🧑‍🏫 Lec05: Exploratory Data Analysis	🛝 Lec05 Slides Lab02 Partial Walkthrough
	Wed, Apr 17			Lab02 Due Extended to Friday
	Thu, Apr 18	📖 IMS, Chapter 2: Study Design	🧑‍🏫 Lec06: Sampling Techniques and Study Design	🛝 Lec06 Slides
	Sun, Apr 21			Mini-Project 01 Due
4	Mon, Apr 22		💻 Review for ICA01 (NOT TURNED IN)
	Tue, Apr 23		🧑‍🏫 Lec07: Regular Expressions Prep for ICA01
	Wed, Apr 24
	Thu, Apr 25		🧑‍🏫 In-Class Assessment 01
5	Mon, Apr 29	📖 R4DS, Chapter 14: Strings 📖 R4DS, Chapter 15: Regular expressions	💻 Lab03: RegEx
	Tue, Apr 30		🧑‍🏫 Lec09: Statistics, Part I	🛝 Lec09 Slides
	Wed, May 1			Lab03 Due
	Thu, May 2	📖 Selected Chapters from IMS: “Statistical Inference”	🧑‍🏫 Lec10: Statistics, Part II	🛝 Lec10 Slides
	Sun, May 5			Homework 02 Due
6	Mon, May 6		💻 Lab04: Two- and Multi-sample Tests
	Tue, May 7	📖 Selected Chapters from IMS: “Regression Modeling”	🧑‍🏫 Lec11: Introduction to Statistical Modeling and Regression	🛝 Lec11 Slides
	Wed, May 8			Lab04 Due
	Thu, May 9	📖 Selected Chapters from IMS: “Regression Modeling”	🧑‍🏫 Lec12: More Regression	🛝 Lec 12 Slides
	Sun, May 12			Mini-Project 02 Due
7	Mon, May 13		💻 Lab05: Regression
	Tue, May 14	📖 TBD	🧑‍🏫 Lec13: Regression, Part III	🛝 Lec 13 Slides
	Wed, May 15			Lab05 Due
	Thu, May 16	📖 TBD	🧑‍🏫 Lec14: PCA	🛝 Lec 14 Slides
	Sun, May 19
8	Mon, May 20		💻 Lab06: PCA/KDE, and Review for ICA02
	Tue, May 21	📖 TBD	🧑‍🏫 Lec15: Finishing up PCA; Review for ICA02	🛝 Lec 15 Slides Homework 03 Due
	Wed, May 22			No Lab Due
	Thu, May 23		🧑‍🏫 In-Class Assessment 02
9	Mon, May 27		HOLIDAY: No Lab or Sections
	Tue, May 28	📖 TBD	🧑‍🏫 Lec17: PCA, Part III	🛝 Lec 17 Slides MP03 Released
	Wed, May 29
	Thu, May 30	📖 TBD	🧑‍🏫 Lec18: Classification, and Nonparametrics	🛝 Lec 18 Slides Final Project Released
10	Mon, Jun 3		💻 Lab07: Open OH for MP03
	Tue, Jun 4	📖 TBD	🧑‍🏫 Lec19: KDE, and Clustering	“🛝 Lec 19 Slides Mini-Project 03 Due
	Wed, Jun 5			NO Lab 07 due; everyone will just receive a 100%
	Thu, Jun 6	📖 R4DS, Chapter 18: Missing Values Additional Readings to be posted	🧑‍🏫 Lec20: Classification, Clustering, and Missingness	“🛝 Lec 20 Slides
Finals	Tue, Jun 11			Final Project Due

Course Calendar