Section outline

• Select section Day 1: Foundations of Data Analysis with Python

  Day 1: Foundations of Data Analysis with Python

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  Module 1: Introduction to Python for Data Analysis

  • Python syntax and data types

  • Data structures: lists, dictionaries, tuples, sets

  • Functions and control flows

  • Installing and managing libraries with pip

  Module 2: Data Wrangling and Exploration with Pandas

  • Working with DataFrames and Series

  • Reading and writing CSV, Excel, and JSON files

  • Data cleaning: handling missing values, duplicates, renaming columns

  • Data filtering, sorting, indexing

  • Aggregations and groupby operations

  Module 3: Numerical Data Analysis with NumPy

  • Understanding NumPy arrays

  • Array creation, indexing, and slicing

  • Vectorized operations and broadcasting

  • Using statistical and mathematical functions for data analysis
• Select section Day 2: Advanced Visualization and Real-World Data Storytelling

  Day 2: Advanced Visualization and Real-World Data Storytelling

  Module 4: Data Visualization Using Matplotlib and Seaborn

  • Creating line plots, bar charts, histograms, and scatter plots

  • Customizing visual elements: titles, labels, legends, colors, and styles

  • Leveraging Seaborn for statistical visualizations: boxplots, heatmaps, pairplots

  • Best practices for choosing the right chart type

  Module 5: Building Interactive Dashboards with Plotly

  • Introduction to Plotly Express and graph objects

  • Developing interactive charts with hover information, sliders, and zoom

  • Exporting interactive visuals to HTML and static images

  Capstone Project: Complete Data Analysis and Visualization Project

  • Import, clean, and explore a real-world dataset

  • Apply analysis techniques and create both static and interactive plots

  • Present findings through a polished Jupyter Notebook report

  Outcome: Delivery of a comprehensive, end-to-end data analysis and visualization project.