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Data Wrangler's Toolkit: A Practical Guide to Modern Data Science

Part I: Foundations and Setup​

  1. Fundamentals of Data Science

    • Understanding Data: Types, Structures, and Formats
    • The Data Science Workflow
    • Statistical Foundations for Data Analysis
    • Ethics and Governance in Data Management

  2. Setting Up Your Data Science Environment

    • Installing Core Tools (Anaconda, Conda)
    • Development Environments (Windows, Linux, WSL)
    • Package Management and Virtual Environments
    • Integrated Development Environments Overview

Part II: Core Data Skills​

  1. Data Collection and Storage

    • Database Fundamentals and SQL
    • Web Scraping with Python
    • APIs and Data Integration
    • Data Storage Solutions and Best Practices

  2. Data Wrangling and Preprocessing

    • Data Cleaning Strategies
    • Handling Missing Values
    • Data Transformation Techniques
    • Feature Engineering
    • Data Quality Assessment

  3. Exploratory Data Analysis

    • Statistical Analysis Methods
    • Data Visualization Principles
    • Pattern Recognition
    • Correlation Analysis
    • Outlier Detection

Part III: Tools and Technologies​

  1. Python for Data Science

    • Python Fundamentals for Data Analysis
    • Pandas and NumPy Essentials
    • Data Manipulation with Python
    • Visualization Libraries (Matplotlib, Seaborn)
    • Working with Jupyter Notebooks

  2. R Programming for Data Analysis

    • R Language Fundamentals
    • Data Manipulation with tidyverse
    • Statistical Analysis in R
    • R Studio Environment
    • R Markdown for Reporting

  3. Visual Analytics with Orange

    • Orange Interface and Workflow
    • Building Data Pipelines
    • Visual Programming for Data Analysis
    • Interactive Visualizations
    • Machine Learning in Orange

Part IV: Advanced Topics​

  1. Time Series Analysis and Forecasting

    • Time Series Fundamentals
    • Neural Prophet Implementation
    • Deep Learning for Time Series
    • Forecasting Best Practices
    • Model Evaluation and Validation

  2. Machine Learning Fundamentals

    • Supervised vs Unsupervised Learning
    • Model Selection and Validation
    • Neural Networks Basics
    • Model Deployment Strategies
    • Performance Optimization

Part V: Professional Practice​

  1. Data Science Workflows

    • Project Organization
    • Version Control with Git
    • Collaborative Data Science
    • Documentation Best Practices
    • Reproducible Research

  2. Cloud Computing for Data Science

    • Google Colaboratory
    • Cloud Storage Solutions
    • Scaling Data Processing
    • Cloud-Based Machine Learning
    • Deployment Strategies

Appendices​

A. Command Line Tools

  • PowerShell for Data Processing
  • Bash Scripting Basics
  • Command Line Data Processing

B. Additional Resources

  • Dataset Sources
  • Learning Resources
  • Community Forums
  • Tool Documentation
  • Reference Materials

Each chapter includes:

  • Practical examples and use cases
  • Code snippets and tutorials
  • Best practices and common pitfalls
  • Exercises and solutions
  • Further reading recommendations