Objective

  1. Familiarization with Jupyter Notebook: Understand the core features of Jupyter Notebook as an interactive development environment for data science tasks.
  2. Dataset Loading and Exploration: Load a dataset into the notebook and perform basic exploratory analysis to identify trends, patterns, and anomalies.
  3. Data Visualization: Create meaningful visualizations using Python libraries such as Matplotlib and Seaborn to represent the dataset graphically.
  4. Data Analysis: Perform basic statistical computations to derive insights from the dataset.
  5. Documentation and Sharing: Document the workflow within the notebook using Markdown cells for easy understanding and sharing of results.
  6. Skill Development: Enhance proficiency in Python programming for data analysis and visualization while learning to use Jupyter Notebook effectively.