Data Science

Logistics

Location ECCR 200
Time Tue/Thu 11:00-12:15
Webpage http://cs.colorado.edu/~jbg/teaching/CSCI_3022/
Mailing List https://piazza.com/colorado/fall2016/csci3022
Required Text Think Stats
Syllabus https://github.com/ezubaric/jbg-web/blob/master/teaching/CSCI_3022/syllabus.md
Grades and Submission Moodle

People

Professor

Jordan Boyd-Graber
ECCS 111B
Office Hours (ECCS Lobby, outside ECCS 111): Starting 6. September, Tuesday 9:30 - 10:30 and by appointment

Course Staff

Schedule

How to read this table:

  1. Do the reading under the corresponding date.
  2. Homeworks are due at 11:55 PM (Mountain) the day listed on the schedule.
Date Topic Assignment Due Lecture
Tue 23. Aug LECTURE: Insights from Data, Course Introduction [PDF ABCD]
Readings:
  • TS 1.1-1.2
  • Python (optional)
Thu 25. Aug LAB: Importing Data [PDF] [DATA]
Readings:
Tue 30. Aug LECTURE: Probability Basics [JBG Gone] [PDF ABCDEF]
Readings:
Thu 1. Sep LAB: Representing Probabilities, Probability Concept Practice [PDF]
Readings:
  • TS 2
Tue 6. Sep LECTURE: Conditional Probabilities [PDF ABC]
Readings:
Thu 8. Sep LAB: Conditional Probabilities [PDF]
Readings:
Thu 8. Sep Homework 1 Due: Data Wrangling [Moodle] [Github]
Tue 13. Sep LECTURE: Discrete Distributions [PDF ABCDEF]
Readings:
Thu 15. Sep LECTURE: Continuous Distributions [PDF ABCDE]
Readings:
Tue 20. Sep LAB: Sampling, Parameterization [PDF]
Thu 22. Sep LAB: Pandas, Python Debugging
Tue 27. Sep LECTURE: Maximum Likelhood Estimation [JBG Out of Town] [PDF ABCDE]
Readings:
Thu 29. Sep LAB: Maximum Likelhood Estimation (examples) [PDF]
Thu 29. Sep Homework 2 Due: Estimating Probabilities [Moodle] [Github]
Tue 4. Oct LECTURE: Statistical Tests (Discrete) [PDF ABCDE]
Readings:
Thu 6. Oct LECTURE: Statistical Tests (Continuous) [PDF ABCDEF]
Readings:
Tue 11. Oct LAB: Statistical Tests Examples [PDF] [Data]
Tue 13. Oct LECTURE: Linear Regression [PDF ABCD]
Readings:
  • TS 10
Thu 13. Oct Homework 3 Due: Statistical Tests [Moodle] [Github]
Tue 18. Oct Midterm Review (Pedro)
Thu 20. Oct Midterm
Tue 25. Oct LECTURE: Logistic Regression [PDF ABC] [Video AB]
Readings:
  • TS 11
Thu 27. Oct Class Canceled: JBG Sick
Tue 2. Nov LECTURE: Feature Engineering [PDF ABCDE] [Video A B C D] [Code Train Test]
Readings:
  • TS 10
Thu 4. Nov LAB: Classification, Regression, Feature Engineering [PDF]
Sun 6. Nov Homework 4 Due: Poll Combination [Moodle] [Github]
Tue 8. Nov LECTURE: Clustering [PDF A B C]
Readings:
Thu 10. Nov Discussing Predictions/Election PDF Not Available
Readings:
Tue 15. Nov LAB: Clustering [PDF]
Thu 17. Nov LECTURE: Support Vector Machines [PDF A B C]
Readings:
Tue 29. Nov LAB: SVM Example [PDF]
Thu 1. Dec Classroom Learning Interview Process
Thu 1. Dec Homework 5 Due: Logistic Regression [Moodle] [Github]
Tue 6. Dec LECTURE: Advanced Topics (Structured Prediction) [PDF A B C D E]
Readings:
Thu 8. Dec Final Review (Pedro)
Tue 13. Dec, 16:30 Final Exam