MLD102 · Python and pandas for Data Analysis

Categories: AI & Automation
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About Course

Use Python notebooks and pandas to load, clean, combine and summarize real business data faster and more reliably than with spreadsheets.

Explore · 4 hours · 4 lessons, a lab, an assessed assignment and a portfolio piece

Who it is for

Spreadsheet users, analysts and beginners who want a practical foundation in Python data analysis.

Prerequisites

MLD101 is recommended; no prior programming experience is required.

What you will be able to do

  • Load CSV and Excel files into pandas DataFrames and inspect their structure and types
  • Clean data by fixing types, handling missing values, removing duplicates and parsing dates
  • Summarize data with filtering, groupby aggregations and pivot tables
  • Combine tables with merges and export clean, documented results

Lessons

  • Lesson 1 · Notebooks and Python for data work
  • Lesson 2 · Loading and cleaning data with pandas
  • Lesson 3 · Summarizing, combining and exporting
  • Lesson 4 · Lab and assignment

Lab: Cleaning and summarizing a store's sales export

You will clean a deliberately messy sales export from a small home-goods store, combine it with a product table, and produce monthly sales by category and store. The data includes mixed date formats, prices stored as text, duplicates and missing values, just like real exports.

Free tools: Google Colab or JupyterLite (free), pandas and openpyxl

Assessed assignment

Using a dataset from your own work with private data removed, or the provided coffee-shop dataset, write a clean, reproducible notebook that loads raw data, documents and applies every cleaning step, builds at least three summaries that answer business questions you state at the top, and exports the results. Include a short data dictionary and a list of data quality issues you found with how you handled each one.

Criterion Weight What excellent work looks like
Correct cleaning 30% Types, dates, missing values and duplicates are handled correctly, and row counts are checked after each step.
Business summaries 25% Three or more summaries clearly answer stated business questions using groupby, pivots or merges.
Reproducibility 25% The notebook runs top to bottom on a fresh session with no manual edits, and raw data is never overwritten.
Documentation 20% A clear data dictionary, a list of quality issues and the reasoning behind each choice.

Portfolio piece

A reproducible pandas notebook that turns a messy business export into clean data and decision-ready summaries.

Self-paced, 12-month access. Questions are answered in writing through the student panel. A completion record is issued when you finish the course.

Next step

Continue with MLD103 · Exploratory Data Analysis and Visualization · 6 hours · $42.00

Continue to the next course

Prefer the whole program? Program in Machine Learning & Applied Data Science · $378.00 (10% less than buying the courses separately)

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What Will You Learn?

  • Load CSV and Excel files into pandas DataFrames and inspect their structure and types
  • Clean data by fixing types, handling missing values, removing duplicates and parsing dates
  • Summarize data with filtering, groupby aggregations and pivot tables
  • Combine tables with merges and export clean, documented results

Course Content

MLD102 · Python and pandas for Data Analysis (4 h)
Use Python notebooks and pandas to load, clean, combine and summarize real business data faster and more reliably than with spreadsheets.

  • Lesson 1 · Notebooks and Python for data work
  • Lesson 2 · Loading and cleaning data with pandas
  • Lesson 3 · Summarizing, combining and exporting
  • Lesson 4 · Lab and assignment
  • MLD102 Assessment · Python and pandas for Data Analysis
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