ECE 20875: Python for Data Science

Fall 2021

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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)
  • 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)
    • 9/20: Sampling and estimation continued. See slides and notebook from 9/17.
    • 9/22: Hypothesis testing. See slides and example notebook (PDF form).
    • 9/24: Hypothesis testing (continued). See materials from 9/22.
  • 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)
    • 10/4: Regular expressions. See slides and notebook (PDF form).
    • 10/6: Regular expressions (continued). See materials from 10/4.
    • 10/8: Regular expressions (continued) and file I/O. See materials from 10/4 and slides on file I/O.
  • Week 8 (10/11-10/17)
    • 10/11: Fall break. No class.
    • 10/13: Started regression. See slides.
    • 10/15: Continued regression, linear algebra review. See slides from 10/13, and notebook on numpy (PDF version).
  • 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)
    • 11/1: Finished NLP, started objects and classes. See slides and associated notebook (PDF version).
    • 11/3: Finished objects. See materials from 11/1.
    • 11/5: Clustering and kMeans. See slides on clustering and notebook on kMeans (PDF version).
  • Week 12 (11/8-11/14)
    • 11/8: Review for Exam 2.
    • 11/10: Finished kMeans. See slides and notebook from 11/5. Exam 2.
    • 11/12: Started GMMs. See (updated) slides from 11/5, and notebook on GMMs (PDF version).
  • 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)
    • 11/29: Naive Bayes continued. See material from 11/19.
    • 12/1: kNN and Classification Metrics. See slides.
    • 12/3: Logistic regression. See slides, and gradient descent example notebook (PDF version).
  • Week 16 (12/6-12/12)
    • 12/6: Introduction to deep learning. See slides.
    • 12/8: Gradient descent for model training. See materials from 12/3 and 12/6.
    • 12/10: Conclusion and review for Exam #3. See slides.
    • Supplementary material on CNN: slides and notebook (PDF version).


Instructors

Chris Brinton
cgb '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 Mitra
Laura M Cruz
Nadir Mohamedraf Alawadi
Jhanvi Saraswat

Undergraduate TAs

Joseph Bushagour
Julia 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