Python for Data Science

Python for Data Science refers to the application of the Python programming language to execute tasks across the entire data science workflow, from data manipulation and analysis to machine learning and visualization. Its dominance in the field is fueled by a combination of its simple, readable syntax and a vast ecosystem of powerful libraries. Core libraries such as Pandas and NumPy provide robust tools for data wrangling and numerical computation, Matplotlib and Seaborn enable effective data visualization, and frameworks like Scikit-learn and TensorFlow facilitate the development of sophisticated machine learning models, making Python a versatile and essential tool for extracting insights from data.

1. Introduction to Python for Data Science
   1. The Role of Python in the Data Science Ecosystem
      1. Popularity and Community Support
      2. Integration with Other Languages and Tools
      3. Use Cases in Data Science
         1. Data Analysis and Exploration
         2. Machine Learning and AI
         3. Statistical Computing
         4. Data Visualization
         5. Web Scraping and APIs
   2. Overview of Key Libraries and Frameworks
      1. Core Scientific Computing Libraries
         1. NumPy
         2. SciPy
         3. SymPy
      2. Data Manipulation and Analysis
         1. Pandas
         2. Polars
      3. Data Visualization Libraries
         1. Matplotlib
         2. Seaborn
         3. Plotly
         4. Bokeh
      4. Machine Learning Frameworks
         1. Scikit-learn
         2. XGBoost
         3. LightGBM
      5. Deep Learning Frameworks
         1. TensorFlow
         2. Keras
         3. PyTorch
      6. Big Data and Parallel Computing
         1. Dask
         2. Ray
         3. PySpark
      7. Specialized Libraries
         1. Statsmodels
         2. NetworkX
         3. NLTK
         4. SpaCy
   3. Setting Up the Development Environment
      1. Python Installation Options
         1. Standard Python Installation
         2. Anaconda Distribution
            1. Anaconda vs Miniconda
            2. Installing Anaconda on Windows
            3. Installing Anaconda on macOS
            4. Installing Anaconda on Linux
         3. Python Version Management
            1. pyenv
            2. conda environments
      2. Package Management
         1. Understanding Package Managers
         2. Using Conda
            1. Creating Environments
            2. Installing Packages
            3. Updating Packages
            4. Removing Packages
            5. Environment Export and Import
         3. Using Pip
            1. Installing from PyPI
            2. Requirements Files
            3. Virtual Environments with venv
         4. Resolving Package Conflicts
         5. Best Practices for Environment Management
      3. Development Tools and Interfaces
         1. Jupyter Ecosystem
            1. Jupyter Notebooks
               1. Installation and Setup
               2. Launching Notebooks
               3. Interface Navigation
               4. Cell Types and Execution
               5. Keyboard Shortcuts
               6. Magic Commands
               7. Extensions and Widgets
            2. JupyterLab
               1. Interface Overview
               2. File Browser and Tabs
               3. Terminal Integration
               4. Extension Manager
            3. Jupyter Hub for Team Environments
         2. Integrated Development Environments
            1. Visual Studio Code
               1. Python Extension Installation
               2. Code Editing Features
               3. Debugging Tools
               4. Git Integration
               5. Jupyter Integration
            2. PyCharm
               1. Community vs Professional Edition
               2. Project Setup
               3. Code Navigation
               4. Refactoring Tools
               5. Database Integration
            3. Spyder
               1. Scientific Computing Features
               2. Variable Explorer
               3. IPython Console Integration
         3. Cloud-Based Development
            1. Google Colab
            2. Kaggle Kernels
            3. Azure Notebooks
            4. AWS SageMaker Studio