Live Course Module: Jupyter Notebook Course for Data Analytics

Total Duration: 24 Hours (4 Weeks)

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Week 1: Introduction to Jupyter Notebook and Setup (6 Hours)

• Topics:

  • Introduction to Jupyter Notebook and its importance in data analytics

  • Installing Anaconda and Jupyter Notebook

  • Overview of the Jupyter interface and essential shortcuts

  • Creating, running, and organizing notebook cells (code vs markdown)

  • Using Markdown for documentation and rich text formatting

  • Managing projects and working directories

• Outcome:
  Learners will be able to set up and navigate Jupyter Notebook, execute Python code, and document analysis workflows effectively.

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Week 2: Working with Data in Jupyter (6 Hours)

• Topics:

  • Importing and exporting datasets (CSV, Excel, JSON, SQL)

  • Using pandas for data manipulation and exploration

  • Data cleaning and preprocessing within Jupyter

  • Integrating NumPy for numerical analysis

  • Interactive coding and debugging techniques

• Outcome:
  Learners will perform data wrangling and exploration using Jupyter as their main analytical workspace.

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Week 3: Data Visualization and Interactive Analysis (6 Hours)

• Topics:

  • Creating plots using matplotlib and seaborn

  • Embedding charts directly in notebooks

  • Interactive visualization with plotly and bokeh

  • Combining visualizations and tables for better insights

  • Inline vs. interactive plotting modes (%matplotlib inline)

• Outcome:
  Students will create clear, interactive visual reports and data dashboards directly within Jupyter Notebook.

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Week 4: Automation, Reporting, and Integration (6 Hours)

• Topics:

  • Using magic commands (%time, %run, %history, %store)

  • Converting notebooks to other formats (HTML, PDF, slides)

  • Using JupyterLab for multi-file data projects

  • Version control and Git integration within Jupyter

  • Case Study: End-to-end data analysis project in Jupyter Notebook

• Outcome:
  Learners will automate repetitive tasks, generate professional analytics reports, and integrate Jupyter with collaborative workflows.

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🎯 Final Deliverables & Outcomes

• Set up and manage data analytics projects in Jupyter Notebook.

• Perform complete data analysis workflows — data import, processing, and visualization.

• Create and share reproducible analytical reports and presentations.

• Integrate Python libraries and Jupyter tools for professional data analytics pipelines.