CIS 545: Big Data Analytics (Spring 2019)
Current Semester This website is for a previous iteration of CIS 545. To access the current one, click here!
Time & location Location: Meyerson Hall B1
Mondays + Wednesdays 12:00pm - 1:30pm.

There are optional recitation / lab sessions on Fridays 1:30pm - 3:00pm in Towne 100.
If you have a conflict, it is okay to not attend the recitation; you may remain in the class!
Instructors Susan Davidson
Location: 566 Levine Hall
Office hour: Mon 2:00-3:00pm
 Clayton Greenberg
Location: 506 Levine Hall
Office hour: Wed 2:00-3:00pm
Teaching assistants
TA Name, PennKey Office Hour Time Office Hour Location
Aditya Srivatsan, adisri
Jeffrey Zhou, jmzhou		 Mon 5:30pm-7:30pm Levine 512
Vatsal Chanana, chanana
Brian Sandler, bms		 Tue 5pm-7pm Levine 512
Bhavna Saluja, bsaluja
Leonardo Murri, murri		 Wed 6pm-8pm Levine 512
Isha Gupta, isgupta
Roshan Santhosh, roshansk		 Thu 7pm-9pm Levine 512
Nanthini Balasubramanian, nanthini
Craig Fan, fancraig		 Fri 2pm-4pm Towne 100
Gauri Pradhan, gpradhan Sat 12pm-2pm Levine 512
Leshang Chen, lechangc
Hanlin Xiao, hlxiao		 Sun 7pm-9pm Levine 512
Andrew Cui, andrewc
Benjamin Fineran, fineran		 By Appointment By Appointment
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.
Lecture Recodings Available via Canvas, but you will learn more if you actually attend class!
Assignments Links to homework assignments will be available in the schedule below.
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 Fall 2018 Spring 2018 Spring 2017