Python for Data Analysis – Practical Approach

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About Course

This is a live online cohort course. You will attend scheduled live classes with an instructor, complete a final project, and receive a certificate of completion after meeting the course requirements.

Classes are live and are not recorded unless expressly stated otherwise. Please make sure you can attend the scheduled sessions before enrolling.

Python is the most widely used programming language in data analysis, data science and machine learning. This 3-week live cohort teaches you how to use Python practically for real data analysis tasks. You will work with Pandas for data manipulation, Matplotlib and Seaborn for data visualisation, and Jupyter Notebook as your working environment – progressing from Python fundamentals through to a complete data analysis project.

Every class is hands-on. You will write Python code, work with real datasets and build a complete data analysis portfolio project by the end of the course.

This course provides education and practical frameworks. It does not guarantee employment, promotion or business results. Results depend on your practice, application and individual circumstances.

This is a live cohort course

  • Format: Live Google Meet classes with an instructor
  • Duration: 3 weeks live classes (26 October – 13 November 2026) plus 1 week for the Final Project (due 20 November 2026)
  • Live Classes: 9 (3 per week – Mon, Wed, Fri)
  • Sessions: Early Morning 07:00–09:00 or Late Evening 20:30–22:30 SAST
  • Project: Final Python Data Analysis Project due Friday, 20 November 2026
  • Certificate: Non-accredited certificate of completion
  • Recordings: No recordings unless stated
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What Will You Learn?

  • Set up a Python data analysis environment using Anaconda and Jupyter Notebook or Google Colab
  • Write Python code for data analysis using variables, data types, lists, dictionaries, loops and functions
  • Load datasets into Pandas DataFrames from CSV and Excel files
  • Explore a dataset using Pandas methods including head, describe, info, value_counts and dtypes
  • Clean a dataset by handling missing values, duplicates, incorrect data types and inconsistent formatting
  • Conduct structured exploratory data analysis to uncover patterns, distributions and relationships
  • Use groupby, agg and pivot_table to summarise and answer business questions from data
  • Merge and combine multiple DataFrames using merge, join and concat
  • Create professional data visualisations using Matplotlib and Seaborn
  • Apply basic statistical analysis including correlation and hypothesis testing
  • Communicate data findings clearly using structured Markdown in Jupyter Notebook
  • Build a complete, well-documented Python data analysis project as a portfolio piece

Course Content

Orientation
Welcome to the live cohort. Learn how live classes work, understand the final project and certificate requirements, and set up your Python environment before Live Class 1.

  • Welcome to Python for Data Analysis — Practical Approach
  • How Live Google Meet Classes Work on EduMzansi
  • Final Project and Certificate Rules
  • Python Setup Checklist

Week 1: Python Fundamentals, Jupyter Notebook & Introduction to Pandas
Get comfortable with Python syntax and Jupyter Notebook, understand core Python data structures and load your first dataset into Pandas for exploration.

Week 2: Data Exploration, Grouping, Aggregation & Merging
Use Pandas to explore patterns in data, group and aggregate records for summary analysis and combine multiple datasets for richer insights

Week 3: Data Visualisation, Statistical Analysis & Final Project
Visualise your data analysis findings using Matplotlib and Seaborn, apply basic statistical analysis and bring everything together in a complete, well-documented Python data analysis project.

Final Project & Certificate
Submit your complete Python Data Analysis Project and qualify for your certificate.

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