CIS 545: Big Data Analytics (Spring 2019)
|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!
Location: 566 Levine Hall
Office hour: Mon 2:00-3:00pm
Location: 506 Levine Hall
Office hour: Wed 2:00-3:00pm
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%.|
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.|
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:
|Previous iterations||Fall 2018 Spring 2018 Spring 2017|