CIS 545: Big Data Analytics (Fall 2018)
Time & location Location: Meyerson Hall B1
Mondays + Wednesdays 1:30pm - 3:00pm.
Instructors Zachary Ives
Location: 305 Levine Hall
Office hour: Wed 3:30-4:30pm
 Clayton Greenberg
Location: 506 Levine Hall
Office hour: Mon 3:30-4:30pm
Teaching assistants
TA Name, PennKey Office Hour Time Office Hour Location
Digvijaysinh Chauhan, dchauhan Mon 11am-12pm Levine 6th floor bump space
Soonbo Han, soonbo Tue 5-7pm Levine 6th floor bump space
Zhengchao Ni, nizc0616 Wed 9-11am Levine 5th floor bump space
Nanthini Balasubramanian, nanthini Thu 12-2pm Levine 5th floor bump space
Hanlin Xiao, hlxiao Thu 5-7pm Levine 6th floor bump space
Youjia Li, youjiali Fri 5-7pm Levine 6th floor bump space
Leshang Chen, leshangc Fri 7-9pm Levine GRW (North) 5th floor bump space
Andrew Cui, andrewc Sun 8-10pm Levine 5th floor bump space
Course description

In the new era of big data, we are increasingly faced with the challenges of processing vast volumes of data. Given the limits of individual machines (compute power, memory, bandwidth), increasingly the solution is to process the data in parallel on many machines. This course focuses on the fundamentals of scaling computation to handle common data analytics tasks. You will learn about basic tasks in collecting, wrangling, and structuring data; programming models for performing certain kinds of computation in a scalable way across many compute nodes; common approaches to converting algorithms to such programming models; standard toolkits for data analysis consisting of a wide variety of primitives; and popular distributed frameworks for analytics tasks such as filtering, graph analysis, clustering, and classification.
Format The format will be two 1.5-hour lectures per week, plus assigned readings from books and handouts. There will be regular homework assignments and a substantial implementation project with a hypothesis, evaluation, and a report. There will also be an in-class midterm and a final exam.
Prerequisites This course expects broad familiarity with probability and statistics, as well as programming in Python. CIS 110, MCIT 590, or the equivalent is required. Additional background in statistics, data analysis (e.g., in Matlab or R), and machine learning (e.g., CIS 519) is helpful.
Texts and readings

We recommend several books for students of different skill levels. The tentative list is:

For students who do not have at least 2 years of a CS degree: You should get the book Data Science from Scratch, by Grus, from O'Reilly. This book provides a quick refresher in Python, probability, statistics, and linear algebra. An online version can be accessed from O'Reilly's Safari service.

For all students: Python for Data Analysis, by McKinney, from O'Reilly.

For advanced students: Python Machine Learning, by Raschka, from Packt.

If you are new to Python and data science, you may find the UC Berkeley free book The Foundations of Data Science useful.
Grading Homework and projects 55%, midterm 15%, final 25%, participation 5%.
Important sites

We will be using Piazza for course-related discussions; please sign up.

Likewise, please register your SEAS or Google account with the homework submission site.

Occasionally we will make information, such as recordings, available via Canvas.
Lecture Recodings You will learn more if you actually attend class!
Assignments The homework assignments will be available here.
Project option

You may elect to take a homework option involving the completion of 6 homeworks, or a project option involving the completion of 3 advanced homeworks plus a term project. For this project, you will be expected to work in small teams and choose a data analysis task with a suitably large dataset, and to define and execute a series of clustering and modeling tasks over it.

You can find interesting data sets at:

Schedule
Previous iterations Spring 2018 Spring 2017