Data & AnalyticsBeginnerContent updated Oct 2026

Data Analysis Using Python

Learn just enough Python to be productive, then use Pandas and Matplotlib to clean, reshape, summarise and chart real data — no computer-science background required.

Duration: Confirmed at enrollment
Structure: 8 modules
Level: Beginner
Mode: Physical or live online

About this course

A practical introduction to analysing data with Python. You set up Jupyter, learn just enough Python to be productive, then use Pandas to import, clean, reshape and summarise data, and Matplotlib to chart it — finishing with a complete cleaning-to-visualization exercise.

Who this is for

Beginners who want to analyse data beyond spreadsheets
Excel users tired of doing the same cleaning by hand
Students and graduates heading into data roles
Professionals who want repeatable, scripted analysis

What you’ll be able to do

Set up Python and Jupyter Notebook and work with Pandas and Matplotlib
Write basic Python: variables, lists, dictionaries, conditions, loops and functions
Import, inspect and export Excel and CSV data with Pandas
Clean data: missing values, duplicates, data types, text, dates and categories
Filter, sort, merge, group, pivot and cross-tabulate data
Summarise data with frequencies, descriptive statistics and percentages
Build and customise bar, line, pie, histogram, scatter and box plots
Use an AI assistant to draft analysis code, and verify the numbers yourself

Tools you’ll use

Python
Jupyter Notebook
Pandas
Matplotlib
Google Colab
An AI assistant (ChatGPT, Claude or Copilot)

Curriculum

The complete content, exactly as taught. Download it as a PDF.

Module AIntroduction and setup5 topics

Python installation
Jupyter Notebook
Introduction to Pandas and Matplotlib
Importing libraries
Google Colab: Python in the browser, nothing to install

Module BPython basics6 topics

Variables and data types
Operators
Lists and dictionaries
If–else statements
Loops
Functions (basic)

Module CWorking with data using Pandas6 topics

Series and DataFrame
Import Excel and CSV files
Data properties
Export data
Selecting rows and columns
A first look at Polars for larger files

Module DData cleaning9 topics

Handling missing values
Removing duplicates
Changing data types
Find and replace
Rename columns
String processing
Date and time
Data categorization
Apply functions

Module EData manipulation7 topics

CRUD operations
Filtering data
Sorting
Merge and join
GroupBy
Pivot table
Crosstab

Module FExploratory data analysis6 topics

Frequencies
Descriptive statistics
Value counts
Percentage calculation
Basic data summary
Using an AI assistant to draft Pandas code — and checking the result

Module GData visualization8 topics

Bar chart
Line chart
Pie chart
Histogram
Scatter plot
Box plot
Chart customization
Export charts

Module HPractical exercise4 topics

Complete data cleaning
Basic analysis
Visualization
Q&A

Your project

Module H is a complete practical exercise: you take a raw dataset through cleaning, basic analysis and visualization, then walk through your results in a Q&A.

Where you’ll use this

Cleaning and combining messy exports from several systems
Repeatable monthly analysis that runs in seconds
Exploring a new dataset before building a dashboard
Survey and operational data summaries
A foundation for machine learning and AI work later

Before you start

None. The course starts with installing Python and covers the programming basics you need. A laptop you can install software on.

Who teaches it

The course is designed and taught by data and AI practitioners — people who build data pipelines, dashboards and AI systems for businesses — so every exercise reflects how the tool is used on real work.

Certificate

You receive a GeXOn Training Center certificate when you complete the course and its project.

Every certificate carries an ID that employers can check at trainingcenter.gexonai.com/verify.

Frequently asked questions

Do I need programming experience?

No. Module B covers the Python basics you need — variables, lists, conditions, loops and simple functions — before you touch real data.

Python or R — which should I choose?

Both handle this work well. Python is more general-purpose and common in industry and AI; R is especially strong for statistics and survey research. If you work with questionnaires and hypothesis tests, look at R.

What do I need to install?

Python and Jupyter Notebook. We set these up together in Module A.

Do you use AI tools in this course?

Yes, once you know the basics. You’ll use an AI assistant to draft Pandas code and explain errors — and then check its output against the data, because it is often confidently wrong. You learn the skill first; the AI makes you faster, not dependent.

What does it cost, and when is the next batch?

We share the fee and schedule when we confirm your batch. Register below and we’ll contact you within one working day — registering doesn’t commit you to anything.

Can my organisation send a group?

Yes. We run this course as a private batch for teams, and can build exercises around your own data. See corporate training.

Pairs well with

Data & Analytics

Data Analysis Using R

Survey and research data, end to end — cleaning through hypothesis testing and reporting.

7 modules
Beginner → Intermediate

View course