ECE 20875: Python for Data Science
Fall 2021
Quick Links
- Piazza
- ECE Data Science Github
- Scholar Computing
- Syllabus
- Lab Hours and Office Hours: See Piazza
- Instructions on using Discord
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 to implement data analyses. More detailed information can be found in the course syllabus.- Lecture days: M/W/F
- Lecture times/locations: Section I: 11:30-12:20, WALC 1018 | Section II: 1:30-2:20, EE 170 | Section III: 1:30-2:20, WTHR 320
- Virtual lab hours: M-F 9-11am, 1-2pm, 5-8pm
- Limited in-person lab hours: T/Th/F 5-8pm, EE 207/206
- Communication: This course will rely heavily on Piazza for official announcements, student questions, and answers to questions.
Lecture Materials
- Week 1 (8/23-8/29)
- 8/23: Course introduction.
- 8/25: Python basics. See slides and notebook (PDF version).
- 8/27: Version control and more Python basics. See (updated) slides posted 8/25, and the associated notebook/PDF.
- Week 2 (8/30-9/5)
- 8/30: Functions and data structures. See slides posted 8/25, and the associated notebook/PDF. Also see the notebook on data structures (PDF version).
- 9/1: Histograms.
- 9/3: Histograms continued. Also starting probability and random variables.
- Week 3 (9/6-9/12)
- 9/6: Labor day. No class.
- 9/8: Probability and random variables (continued). See (slightly updated) slides from 9/3.
- 9/10: Probability and random variables (continued). See (slightly updated) slides from 9/3.
- Week 4 (9/13-9/19)
- Week 5 (9/20-9/26)
- Week 6 (9/27-10/3)
- 9/27: Review for Exam 1.
- 9/29: Exam 1.
- 10/1: Confidence intervals and more hypothesis testing. See materials from 9/22.
- Week 7 (10/4-10/10)
- Week 8 (10/11-10/17)
- Week 9 (10/18-10/24)
- 10/18: Regression continued: numpy tutorial and least squares equations. See materials from Week 8.
- 10/20: Regression continued: Solving the least squares equations, and normalization. See slides from 10/13, and notebook on sklearn (PDF version).
- 10/22: Regression continued: Polynomial regression, regularization, and cross validation. See slides from 10/13, and sklearn notebook from 10/20.
- Week 10 (10/25-10/31)
- 10/25: Regression continued: Regularization and cross validation. See slides from 10/13, and sklearn notebook from 10/20.
- 10/27: Finished regression, started n-grams and natural language processing. See slides on NLP and notebook on nltk (PDF version and universal_decl_of_human_rights.txt file).
- 10/29: NLP continued. See materials from 10/27.
- Week 11 (11/1-11/7)
- Week 12 (11/8-11/14)
- Week 13 (11/15-11/21)
- Week 14 (11/22-11/28): No class (Makeup for Exam #2 and Thanksgiving Break).
- Week 15 (11/29-12/5)
- Week 16 (12/6-12/12)
Instructors
Chris Brintoncgb 'at' purdue 'dot' edu
MSEE 342
Qiang Qiu
qqiu 'at' purdue 'dot' edu
MSEE 358
Mahsa Ghasemi
mahsa 'at' purdue 'dot' edu
MSEE 238
Graduate TAs
Somosmita MitraLaura M Cruz
Nadir Mohamedraf Alawadi
Jhanvi Saraswat
Undergraduate TAs
Joseph BushagourJulia Taylor
Minjun Zhang
Jude Adham
Harsh Ajwani
Sabrina Chang
Jhen-Ruei Chen
Noah Criswell
Vaishakh Deshpande
Hsuan-Chen Fang
Yiming Fu
Alex Gieson
Ishaan Jain
Ruibin Jiang
Robert Ketler
Esther Lee
Shuihan Liu
Wang-Ning Lo
Maximilian Manzhosov
Jan-Adriel Nacpil
Kahaan Patel
Kartik Pattaswamy
Ayush Praharaj
Abhirakshak Raja
Runjia Shen
Siddharth Srinivasan
Avik Wadhwa
Runlin Wang
Henry Lee Wong
Bo-Yang Wu
Tim Zhou