README.md

PSTAT 134/234 - Statistical Data Science

Announcements

April 18, 2019

• https://ucsb.zoom.us/j/918248601

• Add codes will be sent today

• Installing packages: e.g., jassign
  Open terminal and type pip install git+https://github.com/okpy/jassign
  User installed packages are purged when your session expires

• Generated pdf appears empty in Chrome browser (bug?)

• In 14b, it should read
  Funtion bootstrap_error_bars is to be called by using bootstrap_election_100_agg.apply(bootstrap_error_bars, ...).

• Homework 1 diff

April 16, 2019

• https://ucsb.zoom.us/my/sy.oh

• Midterm: May 9 during class time

• okpy.org and Homework 1

• Waiting lists delays

• PSTAT 234 graduate class petition forms are ready for pick up

April 11, 2019

• https://ucsb.zoom.us/j/957723787

• Homework 1 has been posted (due Saturday, April 20)
  https://bit.ly/2Ku27wH

• Petition to Enroll in a Graduate Course forms will be processed by tomorrow morning

• Request access to GauchoSpace: https://piazza.com/class/jttf1pa8is5eb?cid=12

April 9, 2019

• https://ucsb.zoom.us/j/275657667

April 5, 2019

• TAs' Office hours updated

• Third section location updated

April 4, 2019

• Sections and office hours
  Please check for updates to TBD items

• Course material link: https://bit.ly/2JL32Z6
  Modifying the directory may cause merge conflict. You can create a working copy before modifying files

• Jupyter notebook server: https://pstat134.lsit.ucsb.edu
  Server errors: report on Piazza

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Instructor: Sang-Yun Oh

• Email: syoh@ucsb.edu

• Lectures: TR 9:30 am - 10:45 am @ PHELP 3515

• Office hours: Wednesdays 5:00 - 7:00 pm @ South Hall 5514

TA: Javier Zapata

• Email: jzapata@ucsb.edu

• Sections:

  Wednesdays 9:00 - 9:50 am @ PHELP1513
  Wednesdays 12:00 - 12:50 am @ PHELP1513

• Office hours: Monday 3:00 - 5:00 pm @ South Hall 5431-Q

TA: Zhipu (Aaron) Zhou

• Email: zhipu@ucsb.edu

• Sections:

  Fridays 9:00 - 9:50 am @ PHELP1513

• Office hours: Friday 10:00 - 11:00 am @ South Hall 5431-J

Course Information

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

• Jupyter notebook server: https://pstat134.lsit.ucsb.edu
  Note: The course notebook server is for this course only. Your work can be inspected by teaching staff at any time

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