Big Data Analytics (21CSH-471)

The Iterative Nature of Data Science Projects

1. Introduction to Data Science Projects:
   1. Stages and Lifecycle;
   2. Iterative process in Data Science:
      1. Problem Definition
      2. Data collection and exploration
      3. Model development and evaluation;
   3. Refinement and deployment;
   4. Importance of Iteration:
      1. Continuous improvement and error correction;
   5. Tools supporting Iteration:
      1. Notebooks
      2. Version Control
      3. CI/CD

Notebooks in Data Science

1. Introduction to Data Science Notebooks:
   1. Characteristics –
      1. Interactive
      2. reproducible
      3. modular workflow
   2. Key benefits –
      1. Visualization
      2. Documentation
      3. Collaboration;
2. Programming Languages for Data Science:
   1. Python – Libraries like pandas, NumPy and Matplotlib
   2. R – Strengths in statistical analysis and visualization;
   3. Mechanisms and Tolls in Notebooks:
      1. Code cells
      2. Markdown
      3. Widgets
      4. Extensions,
      5. Integration with Git and other data tools

Notebooks and Data Science tools in Big Data

1. Major Data Science Notebooks:
   1. Jupyter Notebook,
   2. Google Colab
   3. Zeppelin,
2. Comparing features:
   1. Offline vs. cloud,
   2. extensions
   3. performance;
3. Getting started with Jupyter Notebook:
   1. Installation,
   2. environment setup,
   3. basic usage,
   4. Working with Python and R in Jupyter;
4. Introduction to Tableau:
   1. Key features and use-cases,
   2. Data connection,
   3. visualization building
   4. Dashboard creation;
5. Collaboration and Presentation tools for Data Insights

Data Visualization (21CSH-461)

Chapter 1: Programming and Tools for Statistical Data Visualization

1. Java language for statistical data visualization,
2. Web-based statistical graphics using XML technologies;
3. Google Maps API for geographical data visualization,
4. Google Chart for creating interactive charts and graphs,
5. Tableau for advanced visualizations and heat map generation

Chapter 2: Rank Analysis and Trend Analysis Tools