Data Science

Logistics

Location Kim 1200
Time Tue/Thu 11:00-12:15
Mailing List https://piazza.com/umd/spring2018/inst414/home
Required Text Think Stats
Syllabus https://docs.google.com/document/d/1YEd5ZK0G-OzLUwK1LardjR8FNciwNPRXYQ9oQz112tA/edit?usp=sharing
Grades and Submission ELMS

People

Professor

Jordan Boyd-Graber
ECCS 111B
Office Hours (AVW 3155): Starting 30. January, Tuesday 13:00 - 14:00 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 (Eastern) the day listed on the schedule.
  • Grinstead and Snell,
  • TS
  • Date Topic Assignment Due Materials
    Thu 25. Jan Insights from Data, Course Introduction, Python Hello World [PDF A B C D] [Video A B C D] [Classroom]
    Readings:
    • TS 1.1-1.2
    • Python (optional)
    Tue 30. Jan Python Review, Lab [PDF A B C D] [Video A B C D] [Lab] [DATA] [Classroom]
    Readings:
    Thu 1. Feb HW 0 Lab [Video] [Classroom]
    Readings:
    • TS 1.3-1.8
    Mon 5. Feb Homework 0 Due: Kaggle Exploration [Kaggle]
    Tue 6. Feb Probability Refresher: Definitions, Notation [PDF A B C D] [Video AB C D] [Practice] [Classroom]
    Readings:
    Thu 8. Feb Conditional Probability [PDF A B] [Video A B] [Practice] [Classroom]
    Readings:
    Tue 13. Feb HW 1 Lab [Classroom]
    Thu 15. Feb Discrete Distributions [PDF A B C] [Video A B C] [Practice]
    Readings:
    Thu 15. Feb Homework 1 Due: Data Wrangling [Github Kaggle]
    Tue 20. Feb Visualization [PDF A B C] [Video A B C]
    Tue 27. Feb HW2 Lab [PDF]
    Tue 27. Feb Continuous Distributions [PDF A B C] [Practice]
    Readings:
    Thu 1. Mar Naive Bayes [Video]
    Readings:
    Thu 1. Mar Homework 2 Due: Tell a Story with Data [Github]
    Tue 6. Mar Decision Trees [Video]
    Readings:
    Thu 8. Mar Linear Regression
    Readings:
    • TS 10
    Tue 13. Mar Midterm Review
    Thu 15. Mar Midterm
    Tue 27. Mar Logistic Regression I
    Readings:
    • TS 11
    Thu 29. Mar Regression (Logistic and Linear) in Python
    Tue 3. April HW3 Lab
    Thu 5. Apr Feature Engineering
    Readings:
    • TS 10
    Sun 6. Apr Homework Due: Regression
    Tue 10. Apr K-Means Clustering
    Readings:
    Thu 12. Apr Topic Models
    Tue 17. Apr Project Lab
    Thu 19. Apr Slack Day
    Tue 24. Apr HW4 Lab
    Thu 26. Apr Support Vector Machines
    Readings:
    Thu 26. Apr Homework: Classification
    Tue 1. May Ensemble Methods
    Thu 3. May Deep Learning
    Tue 8. May Social Implications
    Thu 10. May Project Presentations
    Sat 12. May, 8:00 (Yuck!) Final Exam