README.md

PSTAT 134/234 - Statistical Data Science

Instructor: Sang-Yun Oh

• Email: syoh@ucsb.edu

• Lectures: TR 9:30 am - 10:45 am

• Office: South Hall 5514

• Office hours: TBD

Course Resources

• Github (course material): https://github.com/UCSB-PSTAT-134-234/Spring2019
  Open in Jupyterhub: https://bit.ly/2JL32Z6

• Piazza (questions and discussions): https://piazza.com/ucsb/spring2019/pstat134234

• GauchoSpace (homework submission): https://gauchospace.ucsb.edu/courses/course/view.php?id=44611

Course Information

Grading

• Attendance in lectures and sections are required (10%)
  Total of five will be dropped. No exceptions

• Individual in-class midterm (25%)

• Individual assignments (35%)

• Group final project & poster session (30%)

Textbooks

• Python Data Science Handbook by Jake Vanderplas

• Other resources as needed

Tentative course outline

• Week 1 (4/1-4/5): Data and uncertainty

  • Computing: Jupyter notebook and Python primer
  • Reading: Chapter 1 (skim)-2 in Vanderplas

• Week 2 (4/8-4/12): Data scraping, transformation, and wrangling

  • Computing: Shell commands and Pandas
  • Reading: Chapter 3 in Vanderplas
    The Unix Shell by Software Carpentry

• Week 3-4 (4/15-4/26): Visualization and exploratory analysis

  • Computing: Matplotlib and Scikit-learn
  • Reading: Chapter 4 (skim) - 5

• In-class midterm (4/30)

• Week 5-6 (5/1-5/10): Finance data module

• Week 7-8 (5/13-5/23): Health data module

• Week 9 (5/27-5/31): Text data module

• Week 10 (6/3-6/7): TBD

• Final Projects (6/14): Final project poster session

Computational Environment

Github (optional but recommended)

• Github Student Account

Jupyterhub

• Course Jupyter Hub

• For PSTAT 134/234 coursework only

• Your work can be inspected by teaching staff at any time