CS 404/504 – Special Topics: Python Programming for Data Science¶
Course Syllabus¶
Course Description¶
Textbooks¶
Joel Grus, “Data Science from Scratch: First Principles with Python”, 2nd Edition, O’Reilly Media, 2019, ISBN: 9781492041139.
Chip Huyen, “Designing Machine Learning Systems”, O’Reilly Media, 2022, ISBN: 9781098107963.
Learning Outcomes¶
Upon the completion of the course, the students should demonstrate the ability to:
Attain proficiency with commonly used Python frameworks for managing the life cycle of data science projects.
Develop pipelines for integrating data from multiple sources, designing predictive models, and deploying the models.
Apply Python tools for data collection, analysis, and visualization, such as NumPy, Pandas, Matplotlib, and Seaborn, to real-world datasets.
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.
Understand the principles of model selection and evaluation, including hyperparameter tuning, cross-validation, and regularization.
Understand the primary characteristics of current Python libraries for deployment, continuous integration, and monitoring of data science projects.
Deploy data science projects as web applications using Flask, and to cloud servers using Microsoft’s Azure platform.
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.
Grading¶
Student assessment will be based on 6 homework assignments (worth 60 pts), 3 quizzes (worth 30 marks), and class participation and engagement (worth 10 marks).
Lectures¶
- Lecture 7 - NumPy for Array Operations
- Lecture 8 - Data Manipulation with pandas
- Lecture 9 - Data Visualization with Matplotlib
- Lecture 10 - Databases and SQL
- 10.1 Introduction to SQL
- 10.2 Using SQLite with Python
- 10.3 Create a New Table
- 10.4 Database Example
- 10.5 Querying Databases with SELECT
- 10.6 Sorting Data with ORDER BY
- 10.7 Filtering Data
- 10.8 Conditional Expressions
- 10.9 Joining Multiple Tables
- 10.10 Return Data Statistics
- 10.11 Grouping Data
- 10.12 Modifying Data
- 10.13 Working with Tables
- 10.14 Constraints
- 10.15 Subqueries
- 10.16 Connect to an Existing Database
- References
- Lecture 11 - Data Exploration and Preprocessing
- Lecture 12 - Data Visualization with Seaborn
- Lecture 13 - Scikit-Learn Library for Data Science
- 13.1 Introduction to Scikit-Learn
- 13.2 Supervised Learning: Classification
- 13.3 Supervised Learning: Regression
- 13.4 Unsupervised Learning: Clustering
- 13.5 Hyperparameter Tuning
- 13.6 Cross-Validation
- 13.7 Performance Metrics
- 13.8 Model Pipelines
- 13.9 Flow Chart: How to Choose an Estimator
- Appendix
- References
- Lecture 14 - Ensemble Methods
- Lecture 15 - Artificial Neural Networks
- Lecture 16 - Convolutional Neural Networks
- Lecture 17 - Model Selection, Hyperparameter Tuning
- Lecture 18 - Neural Networks with PyTorch
- Lecture 19 - Natural Language Processing
- Lecture 20 - Transformer Networks
- Lecture 21 - NLP with Hugging Face
- Lecture 22 - Diffusion Models for Text-to-Image Generation
- Lecture 23 - Large Language Models
- Tutorial 1 - Jupyter Notebooks
- Tutorial 2 - Terminal and Command Line
- Tutorial 3 - Python IDEs, VS Code
- Tutorial 4 - Virtual Environments
- Tutorial 5 - Web Scraping
- Tutorial 6 - Google Colab
- Tutorial 7- Image Processing with Python
- Tutorial 8 - TensorFlow
- Tutorial 9 - PyTorch
- Tutorial 10 - Tensorflow Datasets
- Tutorial 11 - CometML
- Tutorial 12 - GitHub