A note from Prof. Jennifer Widom, June 2020:

This was the last offering of CS 102. Congratulations to the students who were able to persevere through a pandemic and horrific racism to complete the course and gain some mastery of working with data, and a big thanks to the teaching assistants for their tremendous efforts. I'm hopeful that within a few years Stanford will offer a cohesive curriculum in data science. In the meanwhile, all of the material from CS 102, including Jupyter notebooks and data sets, is being kept current on the website of Prof. Widom's Instructional Odyssey.

Aimed at non-CS undergraduate and graduate students who want to learn a variety of tools and techniques for working with data. Many of the world's biggest discoveries and decisions in science, technology, business, medicine, politics, and society as a whole, are now being made on the basis of analyzing data sets. This course provides a broad and practical introduction to working with data: data analysis techniques including databases, data mining, machine learning, and data visualization; data analysis tools including spreadsheets, Tableau, relational databases and SQL, Python, and R; introduction to network analysis and unstructured data. Tools and techniques are hands-on but at a cursory level, providing a basis for future exploration and application. Prerequisites: comfort with basic logic and mathematical concepts, along with high school AP computer science, CS106A, or other equivalent programming experience.

Tuesdays & Thursdays 1:30-2:50 PM
Delivered via Zoom. Link found on Canvas.
We prefer students participate live, but lectures will also be recorded.

The five TAs hold office hours throughout the week, and Professor Widom's office hours are usually Wednesdays 4:00-5:00 PM. All office hours are via Zoom, with each week's times and links posted on the course calendar. For TA office hours logistics, please refer to this Piazza post.

Some Wednesdays, 11:30-12:30PM
Bootcamp sessions are recorded and made available afterwards on Canvas. They provide extra setup help for the tools we are using, and additional programming examples for those who may have a weaker background in programming or seek additional practice.

There are 5 assignments, 2 projects, and 2 exams. The final grade is an equal weighting on composite scores for assignments, projects, and exams, i.e., 33.3% each for the the 5 homework assignments (weighted equally), the 2 projects (weighted equally) and the 2 exams (weighted equally). In spring quarter 2020, all courses are graded on an S/NC basis. We will compute a letter grade for each student -- all students who receive a C- or better will be assigned a grade of S, while D+ and below will be assigned a grade of NC.

Exams are held during the class period; see syllabus below for dates. Please make sure you will be available for both of the exam dates. Alternate times (but not dates) may be possible by petition for extenuating circumstances.

Please use Piazza for all questions related to the course. We use Piazza as our primary portal for course-related announcements, so make sure to sign up! For all Piazza posts, we guarantee that we will respond within 24 hours. Also check out the list of frequently asked questions.

Students who may need an academic accommodation based on the impact of a disability must initiate the request with the Office of Accessible Education (OAE). Professional staff will evaluate the request with required documentation, recommend reasonable accommodations, and prepare an Accommodation Letter for faculty. Unless the student has a temporary disability, Accommodation letters are issued for the entire academic year. For CS102 we require accommodation letters to be filed with the instructor a minimum of two weeks before the requested accommodation. This policy is strictly enforced. The OAE is located at 563 Salvatierra Walk (phone: 723-1066, URL: http://oae.stanford.edu).

Date	Topic and Assignments	Readings/References	Notes
Tue April 7	Introduction & Course Logistics Working with Data - Overview	Introductory Readings	Course Information Working With Data - Overview Slides
Wed April 8	Bootcamp: Google Sheets Setup		Google Sheets Setup Instructions
Thu April 9	Working with Data - Overview (cont'd) Data Analysis & Visualization Using Spreadsheets	Google Spreadsheets References	Data Analysis Using Spreadsheets Slides Spreadsheet Analysis Notes (Part 1)
Mon April 13	Assignment #1: Spreadsheets Project #1: Personal Data Analysis
Tue April 14	Data Analysis & Visualization Using Spreadsheets (cont'd)		Spreadsheet Analysis Notes (Part 2)
Wed April 15	Bootcamp: Tableau Setup		Tableau Setup Instructions
Thu April 16	Data Analysis & Visualization Using Spreadsheets (cont'd) Advanced Data Visualization Using Tableau	Common Visualization Mistakes Tableau References	Data Visualization Using Spreadsheets Slides Data Visualization Using Spreadsheets Notes Advanced Data Visualization Using Tableau Slides Advanced Data Visualization Using Tableau Notes
Mon April 20	Bootcamp: Instabase Setup		Instabase Setup Instructions
Mon April 20	Assignment #1 due Assignment #2: Tableau, SQL
Tue April 21	Relational Databases and Basic SQL	SQL References Project Jupyter home page	Relational Databases and SQL Slides Basic SQL Notes
Thu April 23	Advanced SQL		Advanced SQL Notes
Mon April 27	Project #1 proposal due
Tue April 28	Introduction to Python Python for Data Analysis & Visualization	Python References	Python for Data Analysis & Visualization Slides Python Basics Notes Python Data Notes
Wed April 29	Bootcamp: SQL		SQL Bootcamp Slides
Thu April 30	Python for Data Analysis & Visualization (cont'd)	Pandas References	Python Pandas Notes
Thu April 30	Assignment #2 due Assignment #3: Python
Tue May 5	Python for Data Analysis & Visualization (cont'd)	PyPlot Tutorial	Python Plotting Notes
Wed May 6	Bootcamp: Python		Bootcamp Notebooks
Thu May 7	Machine Learning - Regression	ML References - Regression	Regression Slides Regression Notes
Sat May 9	Assignment #3 due (no late submissions)
Tue May 12	Exam #1
Thu May 14	Machine Learning - Classification and Clustering	ML References - Classification and Clustering	Classification Slides Clustering Slides Classification & Clustering Notes
Mon May 18	Project #1 due Assignment #4: Machine Learning, R Project #2: Movie-Rating Predictions	The Netflix Prize
Tue May 19	Using Python for Machine Learning	ML References - Python	Python Machine Learning Notes
Thu May 21	The R Language - Data Analysis, Visualization, and Machine Learning	R Tutorial Choosing R or Python for data analysis? An infographic	R Slides R Notes
Fri May 22	Bootcamp: R
Mon May 25	Assignment #4 due
Tue May 26	Data Mining Algorithms	Data Mining References	Data Mining Slides Data Mining Notes
Thu May 28	Assignment #5: Data Mining, Network Analysis
Thu May 28	Data Mining Using Python		Mining Python Notes
Fri May 29	Bootcamp: Data Mining using SQL
Mon June 1	Project #2 due
Tue June 2	Network Analysis	Network References	Network Slides Networks Notes
Thu June 4	Project #2 results and discussion Unstructured Data		Unstructured Data Slides
Sat June 6	Assignment #5 due
Tue June 9	Exam #2