CIS 610, Data Science

CIS 610, Winter 2018

Data Science

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Course Description: Big data becomes a well-used term due to collections of real life data in both research and industry are so large and so complex that it is difficult to process them using traditional database and data analysis tools. Methods that scale to Big Data are of particular interest in data science. The challenges include capture, curation, storage, search, sharing, transfer, integration, analysis, and visualization. This course will introduce the state-of-arts of data science with the focus on how to utilize them in processing (managing and analyzing) Big data from various resources. We will discuss the parallel and distributed databases. We will then discuss data integration, data fusion, and NoSQL databases. We will discuss the MapReduce (Hadoop) and Apache Spark frameworks, which provide the most accessible and practical means of computing in the Cloud. We will also introduce the deep learning into the data science. Finally, we will utilize several key data analysis tasks, including frequent pattern mining, data clustering, and information retrieval as the case study for Big data processing.
Time and Place: Wednesdays and Fridays at 2:00pm - 3:20pm in 200 Deschutes Hall.
Instructor: Dejing Dou, 303 Deschutes, phone 6-4572, email dou@cs.uoregon.edu.
Office hours:
- Dejing Mondays 4-5pm or by appointment (I like to talk with students).
Directory: The (unix) directory for the class is /cs/classes/www/18W/cis610bdds.
News: Keep an eye on the course news for miscellaneous announcements.
Evaluation:
- The homework will be a combination of written and programming work.
- The Course Project will be due in parts: Part 1 (proposal) is due Friday Nov 11 and Part 2 (final report) on Friday Dec 9. Project presentation will be hold on Friday morning, Dec 9. We assume that you will work on it individually. If you wish to work in a group, please check with the instructor.
The approximate weighting of the work done will be

Homework Assignments 40%

Midterm (March 9) 30%

Course Project 30%

References:

Silberschatz, Korth and Sudarchan Database System Concepts, 6th Edition, McGraw-Hill, 2010. (text website)
Ramakrishnan and Gehrke Database Management Systems, 3rd Edition, McGraw-Hill, 2003. (text website)
Elmasri/Navathe, Fundamentals of Database Systems (Third Edition), Addison-Wesley, 2000.
Sadalage (blog), Fowler (blog) NoSQL Distilled, Addison-Wesley, 2013
Han, Kamber, and Pei Data Mining: Concepts and Techniques, Third Edition, Morgan Kaufmann, 2011 (see text website.)
M. Lenzerini Data Integration: A Theoretical Perspective
J. Bleiholder and F. Naumann Data Fusion

Approximate Syllabus and Lecture Slides

Week of	Chapter	Description and Slides	Events and Readings

January 8		intro, big data management, integration, and analysis
January 15	Silberschatz et al. Chapter 18	parallel databases
January 22	Silberschatz et al. Chapter 19	distributed databases
January 29	Silberschatz et al. Chapter 19	data warehouse, heterogeneous DBs, data integration	Data Integration Paper
February 5		data fusion, deep learning	Deep Learning, Data Fusion, and Multimodal Fusion Papers
February 12	Sadalage & Fowler	NoSQL Databases
February 19	Sadalage & Fowler	MapReduce (Hadoop) and Apache Spark	Course Project Proposal: Friday, February 23
February 26	Han et al. Chapter 1,6,7	data mining, midterm review
March 5		midterm review (Wednesday), midterm (Friday)	Midterm: Friday, March 9
March 12		project implementation, no lecture
March 19		finals week;	Project Presentation Monday, 2:45pm - 5pm, March 19; Project Report Friday, March 23

Late policy: All assignments are due by 11:59pm of the stated due date, though it is much safer to turn them in at class time. Late papers are subject to a 10% per day penalty. Late assignments will not be accepted once the graded papers are returned or once sample solutions are handed out. It is your responsibility to turn in the assignments on time. Make sure that electronically submitted assignments are actually delivered.

Course resources (to be updated):

Big data repository

Homework Assignments	40%
Midterm (March 9)	30%
Course Project	30%