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 === 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
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 Essential shell commands and scripting Essential shell commands and scripting
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 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
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 Virtual environments and dependency management with uv Virtual environments and dependency management with uv
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 Code quality using ruff (linting and formatting) Code quality using ruff (linting and formatting)
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 Development with Visual Studio Code Development with Visual Studio Code
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 AI-assisted programming and productivity tools AI-assisted programming and productivity tools
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 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
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 Data formats: CSV, Parquet, and Apache Arrow Data formats: CSV, Parquet, and Apache Arrow
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 Data cleaning and preprocessing Data cleaning and preprocessing
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 Filtering, joins, aggregations, and missing values Filtering, joins, aggregations, and missing values
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 Performance considerations Performance considerations
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 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)
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 Descriptive statistics and data summarization Descriptive statistics and data summarization
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 Statistical inference with SciPy Statistical inference with SciPy
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 Data visualization using Matplotlib and Seaborn Data visualization using Matplotlib and Seaborn
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 Interactive dashboards with Plotly and Streamlit Interactive dashboards with Plotly and Streamlit
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 Communicating insights through visualizations  Communicating insights through visualizations 
  
-5. Version Control and Project Organization +=== 5. Version Control and Project Organization === 
 Git fundamentals Git fundamentals
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 GitHub and collaborative development workflows GitHub and collaborative development workflows
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 Branching, merging, and pull requests Branching, merging, and pull requests
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 Organizing reproducible data science projects Organizing reproducible data science projects
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 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
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 Designing REST APIs with FastAPI Designing REST APIs with FastAPI
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 Introduction to containerization with Docker (or Podman) Introduction to containerization with Docker (or Podman)
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 Building portable and reproducible applications Building portable and reproducible applications
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 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
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 Feature engineering and model evaluation Feature engineering and model evaluation
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 Introduction to deep learning Introduction to deep learning
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 Large Language Models (LLMs) Large Language Models (LLMs)
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 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
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 Model serving: batch and real-time inference Model serving: batch and real-time inference
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 Experiment tracking with MLflow Experiment tracking with MLflow
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 CI/CD for machine learning projects CI/CD for machine learning projects
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 Introduction to cloud-native ML workflows Introduction to cloud-native ML workflows
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 Model monitoring, drift detection, and retraining strategie Model monitoring, drift detection, and retraining strategie
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  • Dernière modification : 2026/09/01 12:03
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