CS 4622/5622 – Applied Data Science with Python

University of Idaho - Department of Computer Science

Instructor: Alex Vakanski (vakanski@uidaho.edu)

Teaching Assistant: Yugandhar Kasala Sreenivasulu

Semester: Fall 2025 (August 25 – December 19)

Course website: https://fall-2025-applied-data-science-with-python.readthedocs.io/en/latest/

GitHub repository: https://www.github.com/avakanski/Fall-2025-Applied-Data-Science-with-Python/blob/main/README.md

Course Syllabus

View Syllabus

Course Description

The course introduces students to Python tools and libraries commonly used by organizations for managing the 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. This theme also introduces recent advances in large language models, multi-modal models, and agentic AI systems. The last theme focuses on 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.

Textbooks

There are no required textbooks for this course.

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. Design and implement advanced AI systems using large language models, vision-language models, and agentic AI integration.

Prerequisites

The course requires basic programming skills in Python. Prior knowledge of data science methods is beneficial but not required.

Grading

Student assessment will be based on 6 homework assignments (worth 45 pts), 6 quizzes (worth 45 marks), and class participation and engagement (worth 10 marks).

Lectures

Lecture 1 - Short History of AI

• Lecture 1 - A Short History and Current State of Artificial Intelligence

Theme 1 - Python Programming

• Lecture 2 - Data Types in Python
  • 2.1 Introduction
  • 2.2 Numbers
  • 2.3 Strings
  • 2.4 Lists
  • 2.5 Dictionaries
  • 2.6 Tuples
  • 2.7 Sets
  • 2.8 Other Data Types
  • 2.9 String Formatting
  • References
• Lecture 3 - Statements, Files
  • 3.1 Statements
  • 3.2 Files
  • Appendix: Python Interpreter
  • References
• Lecture 4 - Functions, Iterators
  • 4.1 Functions
  • 4.2 Iterators
  • Appendix: Additional Functions Info
  • References
• Lecture 5 - Object-Oriented Programming, Modules, Packages
  • 5.1 Object-Oriented Programming
  • 5.2 Modules Coding Basics
  • 5.3 Packages Coding Basics
  • Appendix 1: Additional OOP Info
  • Appendix 2: Modules and Packages Extras
  • References

Theme 2 - Data Engineering Pipelines

• Lecture 6 - NumPy for Array Operations
  • 6.1 Introduction to NumPy
  • 6.2 Array Construction and Indexing
  • 6.3 Array Math
  • 6.4 Broadcasting
  • 6.5 Random Number Generators
  • 6.6 Reshaping NumPy Arrays
  • 6.7 Linear Algebra with NumPy
  • Appendix
  • References
• Lecture 7 - Data Manipulation with pandas
  • 7.1 Introduction to pandas
  • 7.2 Importing Data and Summary Statistics
  • 7.3 Rename, Index, and Slice
  • 7.4 Creating New Columns, Reordering
  • 7.5 Removing Columns and Rows
  • 7.6 Merging DataFrames
  • 7.7 Calculating Unique and Missing Values
  • 7.8 Dealing With Missing Values: Boolean Indexing
  • 7.9 Exporting A DataFrame to csv
  • References
• Lecture 8 - Data Visualization with Matplotlib
  • 8.1 Introduction to Matplotlib
  • 8.2 The State-based Approach
  • 8.3 Figure Size, Aspect Ratio, and DPI
  • 8.4 Saving Figures
  • 8.5 Other Plotting Functions
  • 8.6 Multiple Plots in Figures
  • 8.7 The Object-oriented Approach
  • Appendix
  • References
• Lecture 9 - Data Visualization with Seaborn
  • 9.1 Relational Plots
  • 9.2 Distributional Plots
  • 9.3 Categorical Plots
  • 9.4 Regression Plots
  • 9.5 Multiple Plots
  • 9.6 Matrix Plots
  • 9.7 Styles, Themes, and Colors
  • References
• 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
  • 11.1 Exploratory Data Analysis
  • 11.2 Preprocessing Numerical Data
  • 11.3 Preprocessing Categorical Data
  • 11.4 Combining Numerical and Categorical Features
  • References

Theme 3 - Model Engineering Pipelines

• Lecture 12 - Scikit-Learn Library for Data Science
  • 12.1 Introduction to Scikit-Learn
  • 12.2 Supervised Learning: Classification
  • 12.3 Supervised Learning: Regression
  • 12.4 Unsupervised Learning: Clustering
  • 12.5 Hyperparameter Tuning
  • 12.6 Cross-Validation
  • 12.7 Performance Metrics
  • 12.8 Model Pipelines
  • 12.9 Flow Chart: How to Choose an Estimator
  • Appendix
  • References
• Lecture 13 - Ensemble Methods
  • 13.1 Ensemble Methods
  • 13.2 Voting Ensemble
  • 13.3 Bagging Ensemble
  • 13.4 Boosting Ensemble
  • 13.5 Stacking Ensemble
  • References
• Lecture 14 - Artificial Neural Networks with Keras-TensorFlow
  • 14.1 Introduction to Artificial Neural Networks
  • 14.2 Classification with ANNs
  • 14.3 Regression with ANNs
  • 14.4 Saving and Loading Models in Keras
  • Appendix
  • References
• Lecture 15 - Convolutional Neural Networks with Keras-TensorFlow
  • 15.1 Introduction to Convolutional Neural Networks
  • 15.2 Loading the Dataset
  • 15.3 Creating, Training, and Evaluating a CNN Model
  • 15.4 Introduce a Validation Dataset
  • 15.5 Dropout Layers
  • 15.6 Batch Normalization
  • 15.7 Data Augmentation
  • 15.8 Transfer Learning
  • References
• Lecture 16 - Model Selection, Hyperparameter Tuning
  • 16.1 Model Selection
  • 16.2 Evaluate the Impact of the Learning Rate
  • 16.3 Callbacks
  • 16.4 Grid Search
  • 16.5 Keras Tuner
  • 16.6 AutoML
  • References
• Lecture 17 - Artificial Neural Networks with PyTorch
  • 17.1 Introduction to PyTorch
  • 17.2 Loading the Dataset
  • 17.3 Training Neural Networks: Revisited
  • 17.4 Creating, Training, and Evaluating the Model
  • 17.5 Using a Custom Dataset and a Pretrained Model
  • 17.6 Model Saving and Loading in PyTorch
  • References
• Lecture 18 - Natural Language Processing
  • 18.1 Introduction to NLP
  • 18.2 Preprocessing Text Data
  • 18.3 Text Tokenization
  • 18.4 Representation of Groups of Words
  • 18.5 Sequence Models Approach
  • References
• Lecture 19 - Transformer Networks
  • 19.1 Introduction to Transformers
  • 19.2 Self-attention Mechanism
  • 19.3 Multi-Head Attention
  • 19.4 Encoder Block
  • 19.5 Positional Encoding
  • 19.6 Using a Transformer Model for Classification
  • 19.7 Decoder Sub-network
  • 19.8 Vision Transformers
  • References
• Lecture 20 - NLP with Hugging Face
  • 20.1 Introduction to Hugging Face
  • 20.2 Hugging Face Pipelines
  • 20.3 Pipelines for NLP Tasks
  • 20.4 Tokenizers
  • 20.5 Datasets
  • 20.6 Models
  • References
• Lecture 21 - Large Language Models
  • 21.1 Introduction to LLMs
  • 21.2 Creating LLMs
  • 21.3 Finetuning LLMs
  • 21.4 Finetuning Example: Finetuning LlaMA-2 7B
  • 21.5 Chat Templates for Formatting LLM Data
  • 21.6 LLM Evaluation
  • 21.7 Prompt Engineering
  • 21.8 Foundation Models
  • 21.9 Limitations and Ethical Considerations of LLMs
  • Appendix: Unsloth Library for LLM Training and Inference
  • References
• Lecture 22 - Large Language Models (Part 2)
  • 22.1 Mixture of Experts
  • 22.2 Retrieval Augmented Generation
  • 22.3 Vision-Language Models
  • Appendix: VLM Finetuning with Unsloth Library
  • References
• Lecture 23 - Reasoning Models
  • 23.1 Introduction to Reasoning Models
  • 23.2 Inference-based Reasoning Models
  • 23.3 Supervised Finetuning Reasoning Models
  • 23.4 Reinforcement Learning-based Reasoning Models
  • 23.5 Reasoning Models Limitations
  • References
• Lecture 24 - Agentic AI
  • 24.1 Introduction to Agentic AI
  • 24.2 Tool Calling Agents
  • 24.3 Coding Agents
  • 24.4 Creating Custom Tools
  • 24.5 RAG Agents
  • 24.6 Multi-Agent Systems
  • 24.7 Vision Agents
  • 24.8 Applications
  • 24.9 Challenges and Risks in Agentic AI
  • Appendix
  • References

Theme 4 - Model Deployment Pipelines

• Lecture 25 - Introduction to Data Science Operations (DSOps)
• Lecture 26 - Deploying Projects as Web Applications
  • 26.1 Introduction to Web APIs for Model Serving
  • 26.2 Deploying a Model for Iris Flowers Classification with Flask
  • 26.3 Deploying a Model with Fast API
  • 26.4 Deploying a Model with Django
  • 26.5 Deploying a Model for MNIST Digits Classification with Flask
  • Appendix
  • References

Tutorials

• Tutorial 1 - Working with Jupyter Notebooks
  • Introduction to Jupyter Notebooks
  • Jupyter Lab User Interface
• Tutorial 2 - Python IDEs, VS Code
  • Python IDEs
  • Introduction to VS Code
  • References
• Tutorial 3 - Terminal and Command Line
  • Introduction to Command Line Interface
  • Command Prompt Basics
  • Terminal in Ubuntu (Linux) Basics
• Tutorial 4 - Virtual Environments
  • Python venv Module
  • Conda Environment
• Tutorial 5 - Google Colab
  • Top Menus
  • Upload Files and Mount the Google Drive
  • Enable GPU
  • Left Pane
  • Colab Subscription Plans
  • Welcome Page of Colab
  • References
• Tutorial 6 - Image Processing with Python
  • Python Image Processing with Pillow
• Tutorial 7 - TensorFlow, TensorFlow DataSets
  • TensorFlow Basics
  • Tensorflow Datasets
• Tutorial 8 - PyTorch
  • Install PyTorch
  • Basics
  • Operations
  • CUDA Tensors
• Tutorial 9 - GitHub
  • Overview
  • Working with GitHub
  • Working with a Local Git Repository
• Tutorial 10 - Docker Containers
  • Introduction
  • Docker
  • Hello World in Docker
  • Scikit-learn Models in Docker
  • Docker Registries and Repositories
  • References