Aimed at non-CS undergraduate and graduate students who want to learn the basics of big data tools and techniques and apply that knowledge in their areas of study. 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 massive data sets. This course provides a broad and practical introduction to big 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.

Time Tuesdays & Thursdays 1:30-2:50 PM
Location: Building 320 room 105 (Geology corner)

The CAs hold 20 hours of office hours a week, Monday-Friday, in reserved areas in the Engineering Quad. Times and places are given in the course calendar.

Professor Widom holds office hours on Wednesdays 4:00-5:00pm in the Dean's Office #227 on the 2nd floor of the Huang building. Updates to her office hours will be posted on the course calendar.

Grades for the course will be weighted equally on composite scores for projects, exams, and homework assignments. That is, the 5 homework assignments will carry the same weight as the 2 exams. There will be 5 assignments, 2 projects, a midterm exam, and a final exam. See the syllabus below for dates and times. There will be no alternate exams, so please make sure you will be available for the midterm on Feb 14 and the final exam on March 18.

Please use Piazza for all questions related to the course. We will be using 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. DO NOT post assignment code on Piazza for debugging; we will not respond to posts containing assignment code. Also check out the list of frequently asked questions.

Date	Topic and Assignments	Readings/References	Notes
Tue Jan 8	Introductions, course logistics, Big Data Overview (start)	Introductory Readings	Course Information Big Data Overview
Thu Jan 10	Big Data Overview (finish) Data Analysis & Visualization Using Spreadsheets (Part 1)	Google Spreadsheets References	Data Analysis Using Spreadsheets Slides Spreadsheet Analysis Notes (Part 1)
Mon Jan 14	Assignment 1 released: Spreadsheets Project 1 released: Personal Data Analysis
Tue Jan 15	Data Analysis & Visualization Using Spreadsheets (Part 2)	Common Visualization Mistakes	Data Visualization Using Spreadsheets Slides Spreadsheet Analysis Notes (Part 2) Spreadsheet Visualization Notes
Thu Jan 17	Advanced Data Visualization Using Tableau	Tableau References	Advanced Data Visualization Using Tableau Slides Tableau Notes
Mon Jan 21	Assignment 1 due Assignment 2 released: Tableau, SQL
Tue Jan 22	Relational Databases and Basic SQL	SQL References Project Jupyter home page	Relational Databases and SQL Slides Basic SQL Notes
Thu Jan 24	Advanced SQL		Advanced SQL Slides Advanced SQL Notes
Mon Jan 28	Project 1 proposal due
Tue Jan 29	Introduction to Python (optional if familiar with Python including lists and dictionaries)	Python References SQL vs Python Comparison	Python Slides Basic Python Notes
Thu Jan 31	Python for Data Analysis & Visualization (part 1)	PyPlot Tutorial	Python Data Manipulation Notes
Thu Jan 31	Assignment 2 due Assignment 3 released: Python
Tue Feb 5	Python for Data Analysis & Visualization (part 2)	Pandas intro SQL vs Pandas Comparison	Python Plotting/Pandas Note
Thu Feb 7	Machine Learning - Regression	ML References - Regression	Regression Slides Regression Notes
Mon Feb 11	Assignment 3 due
Tue Feb 12	Machine Learning - Classification and Clustering	ML References - Classification and Clustering	Classification Slides Clustering Slides
Thu Feb 14	Midterm Exam - in class
Mon Feb 18	Project 1 due Assignment 4 released: Machine Learning, R Project 2 released: Movie-Rating Predictions	The Netflix Prize
Tue Feb 19	Using Python for Machine Learning	ML References - Python
Thu Feb 21	The R Language - Data Analysis, Visualization, and Machine Learning	R Tutorial Quick-R: accessing the power of R Python vs. R for Data Visualization	R Slides R Notes
Tue Feb 26	Data Mining Algorithms	Data Mining References	Data Mining Slides Data Mining Notes
Thu Feb 28	Data Mining Using SQL and Python		Mining Python SQL Notes
Thu Feb 28	Assignment 4 due Assignment 5 released: Data Mining, Network Analysis, Unstructured Data
Tue March 5	Network Analysis	Network References	Network Slides Network Notes
Thu March 7	Unstructured Data		Unstructured Data Slides
Thu March 7	Project 2 due
Tue March 12	Guest lecture by TAs: Big data in the real world Introduction to Deep Learning		Big data in the real world Introduction to Deep Learning
Thu March 14	Project 2 Award Ceremony Guest lecture by TAs: Pathways after CS102 SQL, Python, R On Your Laptop		Pathways after CS102 SQL, Python, R On Your Laptop
Thu March 14	Assignment 5 due
Mon March 18	Final Exam 12:15-3:15 PM Location: Building 320 room 105 (Geology corner)

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 dated in the current quarter in which the request is being made. Students should contact the OAE as soon as possible since timely notice is needed to coordinate accommodations. 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).