Python foundations and setup
Work in Jupyter or an IDE, understand syntax and indentation, use variables and data types, accept input, produce output, and apply operators and type conversion.
Course 02 / Coding pathway
Data Analytics with Python
Progress from your first Python program to cleaning, analysing, and visualising real datasets in a clear, repeatable notebook workflow.
Course overview
Learn Python by solving practical data problems.
This program combines core programming with applied analytics. Weekly coding practice builds your confidence first; pandas, visualisation, and a complete data project then show how Python supports real analysis and automation work.
Best forBeginners, Excel users moving into code, and aspiring data professionals
Core toolsPython, Jupyter, pandas, NumPy, and Matplotlib
You will buildA documented analytics notebook with clean data, charts, and findings
ProgressionData science, automation, machine learning, or advanced analytics
Live codingGuided exercises each module
Data workflowLoad, clean, analyse, visualise
Clean codeFunctions, errors, and reusable logic
PortfolioNotebook and capstone output
Essential curriculum
What you will learn
The supplied Python and data science material is organised into a practical progression from programming fundamentals to analysis and presentation.
Control flow and data structures
Use conditions, loops, comprehensions, strings, lists, tuples, sets, and dictionaries to organise information and solve repeatable problems.
Functions, modules, and error handling
Write reusable functions, understand scope, import modules, work with lambda expressions, and handle exceptions so programs fail clearly and safely.
Files and structured programming
Read and write text and CSV files, use context managers, organise code with classes and objects, and apply practical object-oriented concepts.
Analytics with pandas and visualisation
Load tables, inspect data quality, filter and transform columns, group and aggregate records, join datasets, and create charts with pandas and Matplotlib.
Exploratory analysis and capstone
Investigate trends, distributions, and outliers; document assumptions; explain findings; and package a complete notebook using readable code and professional charts.
Practical outcomes
Skills you can demonstrate after the course.
Write clear PythonUse readable variables, control flow, functions, and modules to solve problems.
Handle data filesImport, validate, transform, and export CSV and tabular information.
Analyse with pandasFilter, group, aggregate, merge, and reshape business datasets.
Visualise patternsCreate charts that reveal comparisons, trends, distributions, and anomalies.
Debug reliablyRead errors, handle exceptions, and test code in manageable steps.
Present a notebookCombine code, outputs, charts, and interpretation in one reviewable analysis.
Portfolio capstone
Create a Python analysis from raw file to business finding.
Choose a realistic dataset, document your objective, prepare it with pandas, explore the most important patterns, and finish with a concise set of visual findings and recommendations.
01Structured and commented notebook
02Reusable cleaning and validation steps
03Exploratory charts and summary tables
04Decision-ready findings and next steps
Admissions
Ready to learn analytics with Python?
Ask about prerequisites, the next batch, fees, and learning format on WhatsApp.