CS 838 Spring 2017

Data Science: Principles, Algorithms, and Applications

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

Announcements

• The class mailing list is compsci838-1-s17@lists.wisc.edu

  • The class's Piazza page is 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: Friday 11-noon and by appointment (pls send email, thanks)

  • TA: Sidharth Mudgal <sidharth@cs.wisc.edu>, office hours: Wed 11-12:30 Room 1351.

Course Description, Prerequisites, and FAQs

Course Format & Grading

• • See the above course description

  • Midterm: March 15 Wed, in class at usual time/room

  • Final: May 3 Wed

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

Lecture Slides (tentative)

• • Logistic discussion throughout the course (not a set of slides)

• 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 understanding, 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.

  • Project stage 2 discussion (board shots)

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