CS 838 Spring 2018 (Also Known as CS 839)

Data Science: Principles, Algorithms, and Applications

WedFri 2:30-3:45pm in 1221 CS Building, 3 Credits

Announcements

• The class mailing list compsci839-1-s18@lists.wisc.edu

  • The class's Piazza page is available here. This is a forum for the students. We will monitor occasionally but do not have enough man power to answer all questions posted to this page.

Instructor & TAs

• AnHai Doan, contact information available from my homepage.

• Office hours: Tue and Fri 5-5:45pm and by appointment (pls send email, thanks)

  • TA: Sidharth Mudgal <sidharth@cs.wisc.edu>, office hours: to be decided.

Course Description, Prerequisites, and FAQs

Course Format & Grading

• • See the above course description

  • Midterm: Wed March 21, in class at usual time/room

  • Final: Fri May 4, in class at usual time/room

  • Grading: midterm: 30%, final: 30%, project: 40%

Lecture Slides (tentative)

Note: the slides below are those from the previous offering of the course. I will update slides AFTER the lectures. When a slide set has been updated, I will indicate so.

• Introduction

• Background: tools, data models/formats, and attributes

• Problem definition and data acquisition

  • Data extraction

  • Information extraction from text

    • Extraction from template-based data (aka wrapper-based extraction)

• Data exploration, profiling, cleaning, transformation

  • Data integration

  • String matching

  • Data matching

  • Schema matching and putting it all together

  • Data exploration and analysis

  • OLAP

    • classification, clustering

    • association rule mining (see the book chapter)

    • anomaly detection

• Building data-intensive artifacts & designing data-intensive experiments

• Cross-cutting issues

  • • cross-cutting techniques, execution stages, workflow management, team organization

    • the three Ss: stages, steps, stacks

    • scaling, quality monitoring, crowdsourcing, etc.

    • implementation/architectures

Project

• • Students will form teams for a multi-stage project that addresses a data science problem.

  • Project page.

Resources

• • Click here for resources to learn Python, pandas, machine learning, more data science, etc.

• If you want to study more data science at UW

Misc

• • dotdatascience.org UW-madison student organization focused on data science