CS 488/588 – Applied Data Science with Python

University of Idaho - Department of Computer Science

Instructor: Alex Vakanski (vakanski@uidaho.edu)

Teaching Assistant: Longze Li (li8975@vandals.uidaho.edu)

Semester: Fall 2024 (August 19 – December 13)

Course website: https://fall-2024-applied-data-science-with-python.readthedocs.io/en/latest/

GitHub repository: https://www.github.com/avakanski/Fall-2024-Applied-Data-Science-with-Python/blob/main/README.md

Course Syllabus

View Syllabus

Course Description

The course introduces 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.

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. 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, FastAPI, and Django, 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 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 - Statistical Data Analysis
  • 10.1 Descriptive Statistics
  • 10.2 Inferential Statistics
  • References
• Lecture 11 - Databases and SQL
  • 11.1 Introduction to SQL
  • 11.2 Using SQLite with Python
  • 11.3 Create a New Table
  • 11.4 Database Example
  • 11.5 Querying Databases with SELECT
  • 11.6 Sorting Data with ORDER BY
  • 11.7 Filtering Data
  • 11.8 Conditional Expressions
  • 11.9 Joining Multiple Tables
  • 11.10 Return Data Statistics
  • 11.11 Grouping Data
  • 11.12 Modifying Data
  • 11.13 Working with Tables
  • 11.14 Constraints
  • 11.15 Subqueries
  • 11.16 Connect to an Existing Database
  • References
• Lecture 12 - Data Exploration and Preprocessing
  • 12.1 Exploratory Data Analysis
  • 12.2 Preprocessing Numerical Data
  • 12.3 Preprocessing Categorical Data
  • 12.4 Combining Numerical and Categorical Features
  • References

Theme 3 - Model Engineering Pipelines

• 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
  • 14.1 Ensemble Methods
  • 14.2 Voting Ensemble
  • 14.3 Bagging Ensemble
  • 14.4 Boosting Ensemble
  • 14.5 Stacking Ensemble
  • References
• Lecture 15 - Artificial Neural Networks with Keras-TensorFlow
  • 15.1 Introduction to Artificial Neural Networks
  • 15.2 Classification with ANNs
  • 15.3 Regression with ANNs
  • 15.4 Saving and Loading Models in Keras
  • Appendix
  • References
• Lecture 16 - Convolutional Neural Networks with Keras-TensorFlow
  • 16.1 Introduction to Convolutional Neural Networks
  • 16.2 Loading the Dataset
  • 16.3 Creating, Training, and Evaluating a CNN Model
  • 16.4 Introduce a Validation Dataset
  • 16.5 Dropout Layers
  • 16.6 Batch Normalization
  • 16.7 Data Augmentation
  • 16.8 Transfer Learning
  • References
• Lecture 17 - Model Selection, Hyperparameter Tuning
  • 17.1 Model Selection
  • 17.2 Evaluate the Impact of the Learning Rate
  • 17.3 Callbacks
  • 17.4 Grid Search
  • 17.5 Keras Tuner
  • 17.6 AutoML
  • References
• Lecture 18 - Neural Networks with PyTorch
  • 18.1 Introduction to PyTorch
  • 18.2 Loading the Dataset
  • 18.3 Training Neural Networks: Revisited
  • 18.4 Creating, Training, and Evaluating the Model
  • 18.5 Using a Custom Dataset and a Pretrained Model
  • 18.6 Model Saving and Loading in PyTorch
  • References
• Lecture 19 - Natural Language Processing
  • 19.1 Introduction to NLP
  • 19.2 Preprocessing Text Data
  • 19.3 Text Tokenization
  • 19.4 Representation of Groups of Words
  • 19.5 Sequence Models Approach
  • References
• Lecture 20 - Transformer Networks
  • 20.1 Introduction to Transformers
  • 20.2 Self-attention Mechanism
  • 20.3 Multi-Head Attention
  • 20.4 Encoder Block
  • 20.5 Positional Encoding
  • 20.6 Using a Transformer Model for Classification
  • 20.7 Decoder Sub-network
  • 20.8 Vision Transformers
  • References
• Lecture 21 - NLP with Hugging Face
  • 21.1 Introduction to Hugging Face
  • 21.2 Hugging Face Pipelines
  • 21.3 Pipelines for NLP Tasks
  • 21.4 Tokenizers
  • 21.5 Datasets
  • 21.6 Models
  • References
• Lecture 22 - Large Language Models
  • 22.1 Introduction to LLMs
  • 22.2 Creating LLMs
  • 22.3 Finetuning LLMs
  • 22.4 Limitations and Ethical Considerations of LLMs
  • 22.5 Foundation Models
  • 22.6 AI Agents
  • Appendix: Unsloth Library for LLM Training and Inference
  • References

Theme 4 - Model Deployment Pipelines

• Lecture 23 - Introduction to Data Science Operations (DSOps)
• Lecture 24 - Deploying Projects as Web Applications
  • 24.1 Introduction to Web APIs for Model Serving
  • 24.2 Deploying a Model for Iris Flowers Classification with Flask
  • 24.3 Deploying a Model with Fast API
  • 24.4 Deploying a Model with Django
  • 24.5 Deploying a Model for MNIST Digits Classification with Flask
  • Appendix
  • References
• Lecture 25 - Deploying Projects to the Cloud
  • 25.1 Data Science using Cloud Computing
  • 25.2 Introduction to Azure Machine Learning
  • 25.3 No-Code Azure ML
  • 25.4 Code-based Azure ML
  • References

Tutorials

• Tutorial 1 - Working with Jupyter Notebooks
  • Introduction to Jupyter Notebooks
  • Jupyter Lab User Interface
• Tutorial 2 - Terminal and Command Line
  • Introduction to Command Line Interface
  • Command Prompt Basics
  • Terminal in Ubuntu (Linux) Basics
• Tutorial 3 - Python IDEs, VS Code
  • Python IDEs
  • Introduction to VS Code
• Tutorial 4 - Virtual Environments
  • Creating a Virtual Environment
  • Conda Environment
• Tutorial 5 - Web Scraping
  • Basic components of a WebSite
  • Web Scraping with Python
• Tutorial 6 - Google Colab
  • Top Menus
  • Upload Files and Mount the Google Drive
  • Enable GPU
  • Left Pane
  • Colab Subscription Plans
  • Welcome Page of Colab
  • References
• Tutorial 7- Image Processing with Python
  • Python Image Processing with Pillow
• Tutorial 8 - TensorFlow
  • TensorFlow Basics
  • Tensorflow Datasets
• Tutorial 9 - PyTorch
  • Install PyTorch
  • Basics
  • Operations
  • CUDA tensors
  • Training a Simple NN model using PyTorch with MNIST Dataset
• Tutorial 10 - Bash Scripting
  • Advantages of Bash Scripts
  • Basic Shell Interface
• Tutorial 11 - GitHub
  • Overview
  • Working with GitHub
• Tutorial 12 - TensorFlow Serving
  • Docker
  • ML Model Creation
  • TensorFlow Serving with Docker
  • Send a Request with the REST API
  • References