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Working with Data

Before you can create charts or build machine learning models, you need to understand your data.

In this module, you will learn how to work with datasets using Pandas, a Python library that helps you read, explore, clean, and organize data. Step by step, you will move from a raw file to useful, reusable datasets and basic analytical insights.

Why this module matters​

Real datasets are often messy. They may contain missing values, extra columns, or information that needs to be sorted and cleaned before you can analyze it.

This module helps you build the core skills needed to work with tabular data confidently. These are the same steps analysts use before creating dashboards, reports, or models in research and industry.

What you’ll learn​

In this module, you will learn how to:

  • Read CSV and Excel files into Pandas
  • Understand DataFrames as in‑memory tables
  • Explore a dataset with summary statistics
  • Select rows and columns
  • Filter and sort data
  • Create new columns and clean data
  • Group and summarize information
  • Export your results for reuse and sharing

How you’ll learn​

You will follow a simple workflow throughout the module:

  1. Load the data
  2. Explore the dataset
  3. Clean and prepare the data
  4. Analyze the data (selecting, filtering, sorting, grouping)
  5. Summarize findings
  6. Export results

By following the same workflow again and again in Google Colab, you will start to see how each Pandas skill fits into a real data analysis task.

Dataset​

Most examples in this module use a Students Performance dataset. This gives you a familiar context while you practice:

  • reading files into DataFrames
  • inspecting structure and types
  • selecting and filtering rows
  • grouping and summarizing results

You can view and download the dataset from GitHub:

StudentsPerformance.csv on GitHub

Final case study​

At the end of the module, you will complete a Student Performance Case Study that brings everything together.

You will work through a full data analysis task from start to finish and apply the skills you learned in the lessons before it: loading, cleaning, exploring, grouping, and exporting data.