Aleksandar (Alex) Vakanski

 

CS 404/504 Special Topics: Python Programming for Data Science

 

Course website: https://fall-2023-python-programming-for-data-science.readthedocs.io/en/latest/2

GitHub repository: https://www.github.com/avakanski/Fall-2023-Python-Programming-for-Data-Science/blob/main/README.md

Link to the course materials from previous years: Fall 2022

Course Syllabus

Syllabus

Course Description

The course is designed to introduce students to Python tools and libraries that are commonly used by organizations for managing the various phases in the life cycle of data science projects. The content is divided into four main themes. The first theme reviews the fundamentals of Python programming. The second theme focuses on data engineering and explores Python tools for data collection, exploration, and visualization. The next theme covers model engineering and includes topics related to model design, selection, and evaluation for image processing, natural language processing, and time series analysis. The last theme introduces Data Science Operations (DSOps) and encompasses techniques for model serving, performance monitoring, diagnosis, and reproducibility of data science projects deployed in production. Throughout the course, students will gain hands-on experience with various Python libraries for data science workflow management. Additional work is required for graduate credit.

Learning Outcomes

Upon the completion of the course, the students should demonstrate the ability to:

1.  Attain proficiency with commonly used Python frameworks for managing the life cycle of data science projects.

2.  Develop pipelines for integrating data from multiple sources, designing predictive models, and deploying the models.

3.  Apply Python tools for data collection, analysis, and visualization, such as NumPy, Pandas, Matplotlib, and Seaborn, to real-world datasets.

4.  Implement machine learning algorithms for image processing, natural language processing, and time series analysis using Python-based frameworks, such as Scikit-Learn, Keras, TensorFlow, and PyTorch.

5.  Understand the principles of model selection and evaluation, including hyperparameter tuning, cross-validation, and regularization.

6.  Understand the primary characteristics of current Python libraries for deployment, continuous integration, and monitoring of data science projects.

7.  Deploy data science projects as web applications using Flask, and to cloud servers using Microsoft’s Azure platform.

Course Materials

Textbooks (recommended, not required):

  1. Joel Grus, "Data Science from Scratch: First Principles with Python," 2nd Edition, O'Reilly Media, 2019, ISBN: 9781492041139.
  2. Chip Huyen, "Designing Machine Learning Systems," O'Reilly Media, 2022, ISBN: 9781098107963.

Topics

Prerequisites

The course requires to have basic programming skills in Python. While having knowledge of data science methods would be advantageous, it is not mandatory.

Evaluation Procedure

Quizzes (3)

30 %

Assignments (6)

60 %

Class participation

10 %