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 learning, and software engineering best practices. Students learn how to build reproducible data science projects using learning, and software engineering best practices. Students learn how to build reproducible data science projects using
 contemporary Python tools while developing the practical skills required to acquire, process, analyze, visualize, and model real-world contemporary Python tools while developing the practical skills required to acquire, process, analyze, visualize, and model real-world
-data. +data. 
 The course covers the complete data science workflow, from environment setup and collaborative development to exploratory data The course covers the complete data science workflow, from environment setup and collaborative development to exploratory data
 analysis, statistical inference, machine learning, REST APIs, containerization, and the fundamentals of MLOps. Emphasis is placed on analysis, statistical inference, machine learning, REST APIs, containerization, and the fundamentals of MLOps. Emphasis is placed on
 reproducibility, scalability, and industry-standard tools widely used in professional data science.  reproducibility, scalability, and industry-standard tools widely used in professional data science. 
-1. Computing Environment and Python Fundamentals  + 
-Overview of Linux, Windows, and macOS+ 
 +=== 1. Computing Environment and Python Fundamentals Overview of Linux, Windows, and macOS ===
 File systems, paths, and permissions File systems, paths, and permissions
 +
 Essential shell commands and scripting Essential shell commands and scripting
 +
 Python refresher: scripts, modules, and packages Python refresher: scripts, modules, and packages
 +
 Command-line workflows for data science  Command-line workflows for data science 
-2. Modern Python Tooling + 
 +=== 2. Modern Python Tooling === 
 The modern Python ecosystem The modern Python ecosystem
 +
 Virtual environments and dependency management with uv Virtual environments and dependency management with uv
 +
 Code quality using ruff (linting and formatting) Code quality using ruff (linting and formatting)
 +
 Development with Visual Studio Code Development with Visual Studio Code
 +
 AI-assisted programming and productivity tools AI-assisted programming and productivity tools
 +
 Reproducible Python environments  Reproducible Python environments 
-3. Data Manipulation and Processing + 
 +=== 3. Data Manipulation and Processing === 
 Working with tabular data using NumPy and Pandas Working with tabular data using NumPy and Pandas
 +
 Data formats: CSV, Parquet, and Apache Arrow Data formats: CSV, Parquet, and Apache Arrow
 +
 Data cleaning and preprocessing Data cleaning and preprocessing
 +
 Filtering, joins, aggregations, and missing values Filtering, joins, aggregations, and missing values
 +
 Performance considerations Performance considerations
 +
 Introduction to scalable data processing with Polars, DuckDB, Dask, and Spark  Introduction to scalable data processing with Polars, DuckDB, Dask, and Spark 
-4. Exploratory Data Analysis and Statistical Thinking + 
 +=== 4. Exploratory Data Analysis and Statistical Thinking ===
 Principles of exploratory data analysis (EDA) Principles of exploratory data analysis (EDA)
 +
 Descriptive statistics and data summarization Descriptive statistics and data summarization
 +
 Statistical inference with SciPy Statistical inference with SciPy
 +
 Data visualization using Matplotlib and Seaborn Data visualization using Matplotlib and Seaborn
 +
 Interactive dashboards with Plotly and Streamlit Interactive dashboards with Plotly and Streamlit
 +
 Communicating insights through visualizations  Communicating insights through visualizations 
-5. Version Control and Project Organization + 
 +=== 5. Version Control and Project Organization === 
 Git fundamentals Git fundamentals
 +
 GitHub and collaborative development workflows GitHub and collaborative development workflows
 +
 Branching, merging, and pull requests Branching, merging, and pull requests
 +
 Organizing reproducible data science projects Organizing reproducible data science projects
 +
 Notebooks versus Python scripts Notebooks versus Python scripts
 +
 Literate programming with Marimo  Literate programming with Marimo 
-6. Building Data Applications + 
 +=== 6. Building Data Applications === 
 Consuming REST APIs with requests Consuming REST APIs with requests
 +
 Designing REST APIs with FastAPI Designing REST APIs with FastAPI
 +
 Introduction to containerization with Docker (or Podman) Introduction to containerization with Docker (or Podman)
 +
 Building portable and reproducible applications Building portable and reproducible applications
 +
 Deploying simple data services  Deploying simple data services 
-7. Introduction to Machine Learning and Artificial IntelligenceThe machine learning workflow+ 
 +=== 7. Introduction to Machine Learning and Artificial IntelligenceThe machine learning workflow ===
 Supervised and unsupervised learning Supervised and unsupervised learning
 +
 Feature engineering and model evaluation Feature engineering and model evaluation
 +
 Introduction to deep learning Introduction to deep learning
 +
 Large Language Models (LLMs) Large Language Models (LLMs)
 +
 Generative AI, prompt engineering, and AI agents  Generative AI, prompt engineering, and AI agents 
-8. MLOps and Production Machine Learning + 
 +=== 8. MLOps and Production Machine Learning === 
 From notebooks to production From notebooks to production
 +
 Model serving: batch and real-time inference Model serving: batch and real-time inference
 +
 Experiment tracking with MLflow Experiment tracking with MLflow
 +
 CI/CD for machine learning projects CI/CD for machine learning projects
 +
 Introduction to cloud-native ML workflows Introduction to cloud-native ML workflows
 +
 Model monitoring, drift detection, and retraining strategie Model monitoring, drift detection, and retraining strategie
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