ECE 20875: Python for Data Science
Spring 2020
Quick Links
- Piazza
- Online Lectures and Office/TA Hours
- ECE Data Science Github
- Scholar Computing
- Syllabus (Updated for Online Instruction)
Course Information
This course introduces Python programming to students through data science problems. Students learn Python concepts as well as introductory data science topics, and use their knowledge of Python (and prior programming experience) to implement data analyses. More detailed information can be found in the course syllabus.- Lecture times: 12:30-1:20 MWF
- Lecture locations: WALC 1055 (Section I), FRNY G124 (Section II)
- Lab hours: 10:30-11:30, 5:30-8:30 M-F in EE 207
- Communication: This course will rely heavily on Piazza for official announcements, student questions, and answers to questions.
Lecture Materials
- Week 1 (1/13-1/19)
- 1/13: Course introduction.
- 1/15: Slides on data science examples and Python basics. Notebook walking through Python basics (PDF form).
- 1/17: Slides on version control. Also see (updated) slides posted 1/15 on Python basics.
- Week 2 (1/20-1/26)
- Week 3 (1/27-2/2)
- 1/27: See data structure slides and notebook from 1/24. Also started on histograms.
- 1/29: Histograms. See (updated) slides from 1/27.
- 1/31: Probability and random variables (updated).
- Week 4 (2/3-2/9)
- 2/3: Probability and random variables (continued). See (updated) slides from 1/31.
- 2/5: Higher order functions. See notebook (PDF form), and slides.
- 2/7: Map/reduce/filter and list comprehensions. See notes/slides from 2/5, and additional list comprehension examples (PDF form).
- Week 5 (2/10-2/16)
- Week 6 (2/17-2/23)
- 2/17: Review for Exam 1.
- 2/19: No class (making up for Exam 1).
- 2/21: Confidence intervals. See (updated) slides from 2/14.
- Week 7 (2/24-3/1)
- Week 8 (3/2-3/8)
- Week 9 (3/9-3/15)
- Spring Break: No Class (3/16-3/22)
- Week 10 (3/23-3/29)
- 3/23: Regression and cross validation. See (updated) slides from 3/2.
- 3/25: n-grams and natural language processing. See slides.
- 3/27: n-grams and NLP continued. See (updated) slides from 3/25, and notebook on nltk (PDF version and english.txt file)
Assignments
- Homework 1: Python and Git basics, due 1/24.
- Homework 2: Functions and data structures, due 1/31.
- Homework 3: Histograms and distributions, due 2/7.
- Homework 4: Higher order functions, due 2/14.
- Homework 5: Hypothesis testing and confidence intervals, due 2/28.
- Homework 6: Regular expressions, due 3/6.
- Homework 7: Bash, due 3/13.
- Homework 8: Regression, due 4/3.
Exams
- Exam 1 (solutions): Tuesday, February 18, 8pm, STEW 183
- Exam 2: Thursday, April 9, 8pm ET, online
- Exam 3: Tuesday, May 5, 3:30pm ET, online
Practice Exams
- Fall 2019 Exam 1 (Solutions): For Spring 2020 Exam 1, problems 2-5.1 are fair game.
- Fall 2019 Exam 2: For Spring 2020 Exam 2, all problems are fair game. Problems 1 and 5.2 from Fall 2019 Exam 1 are also fair game.
Instructors
Chris Brintoncgb 'at' purdue 'dot' edu
MSEE 342
Office hours:
David Inouye
dinouye 'at' purdue 'dot' edu
EE 332
Office hours:
Graduate TAs
Somosmita MitraBrandon Kozel
Rajeev Sahay
Undergraduate TAs
Tyler BaumgartnerColin Harrison
Aidan Abbott
Sabriya Alam
Jason Chen
Isha Ghodgaonkar
Chieh-En Li
Jerome Schweitzer
Julia Taylor
Chien-Hung Wang