Availability
Available
Data & intelligent systems
Data Science & AI
Use Python to find patterns in data, test basic models and explain what they show.
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DATA SCIENCE / AI01 — 04
From data to models
An illustrative learning path: prepare → explore → model → evaluate.
PythonStatisticsMachine learning
Duration
Duration to be finalised
Topic focus
Python · statistics · machine learning
Practice preview
Inspect a dataset · compare simple models
Topic and practice examples are indicative; confirm the current outline with the institute.
What you'll get
Find patterns and test simple models.
These are example learning areas, not a finalized institute syllabus.
Work with structured data
Load, inspect and prepare datasets for analysis using Python workflows.
Explore patterns
Use descriptive statistics and visualisation to explain what a dataset contains.
Understand model basics
Learn the purpose and limits of common supervised and unsupervised approaches.
Evaluate responsibly
Compare model results, consider data quality and communicate uncertainty.
Example learning map
Topics, in a useful sequence.
Illustrative modules only. Final curriculum, tools and course arrangements are not confirmed on this page.
Python and data foundations
Core Python concepts, notebooks, tabular data, data types and a first look at data quality.
Exploration and statistics
Summaries, distributions, relationships, visual exploration and asking testable questions.
Machine learning concepts
Features and labels, training and test data, baseline models and evaluation concepts.
Responsible AI and communication
Model limitations, bias awareness, reproducibility and explaining findings to others.
Practice examples
Learn by investigating questions.
Possible practice exercises—not guaranteed course deliverables—could use openly available or synthetic datasets to make the steps concrete.
- 01Explore a prepared dataset and explain its quality issues.
- 02Compare simple classification approaches on a sample problem.
- 03Present a small analysis with clear limitations and visual evidence.
Career context
Where these skills can be used.
Possible role families are examples only; learning this material does not guarantee a job or placement.
Junior data analystData science associateMachine learning supportAI application development
FAQs
Before you decide.
Is the syllabus finalized?
No finalized syllabus is published here. The topics shown are an illustrative learning map; confirm current details directly with the institute.
Are fees, duration and timings listed?
No. Those details are not confirmed on this page. Use the guidance link to ask the institute about current arrangements.
Does this page guarantee a job or certification?
No. It describes learning areas and possible role contexts only; no job, placement or certification outcome is promised here.