Data & Artificial Intelligence · Beginner
AI & Data Science Course
Learn data analysis and machine-learning foundations: Python, statistics, data cleaning, visualization, model evaluation and a reproducible capstone.
- 6 monthslength
- 6modules
- 33+skills taught
- Discussfee with admissions
About this course
What you will study
What this course can help you do
Turn raw data into careful, useful conclusions. Begin with Python and spreadsheet-shaped data, learn core statistics and visualization, and practise cleaning and exploring datasets before building simple machine-learning models. You will compare models against baselines, communicate uncertainty, and keep your work reproducible so another person can follow it.
Designed for beginners with steady practice and instructor guidance. The course develops practical foundations rather than promising a job or a particular model result. Class schedule is arranged individually.
The plan
What you will learn, module by module
6 modules over 6 months. Each one ends with something you built, not a quiz you guessed.
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Module 1: Python and data handlingWeeks 1–4
What you will learn in this module
- Python basics
- Notebooks
- Files
- Tables
- Data types
You finish this module with: A cleaned and documented dataset
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Module 2: Statistics for analysisWeeks 5–8
What you will learn in this module
- Distributions
- Averages
- Variation
- Sampling
- Correlation
- Uncertainty
You finish this module with: An exploratory analysis that answers specific questions
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Module 3: Data cleaning and explorationWeeks 9–12
What you will learn in this module
- Missing values
- Duplicates
- Outliers
- Transformations
- Exploratory analysis
You finish this module with: A validated machine-learning baseline with honest metrics
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Module 4: Visualization and communicationWeeks 13–16
What you will learn in this module
- Chart selection
- Dashboards
- Narrative
- Audience
- Limitations
You finish this module with: A reproducible data-science capstone and presentation
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Module 5: Machine-learning foundationsWeeks 17–20
What you will learn in this module
- Train
- Test data
- Regression
- Classification
- Baselines
- Metrics
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Module 6: Reproducible capstoneWeeks 21–26
What you will learn in this module
- Question
- Dataset
- Cleaning log
- Analysis
- Model comparison
- Presentation
After the course
What you will be able to do
- Load, clean and document structured datasets
- Use descriptive statistics and visualizations to explore questions
- Build and compare basic machine-learning models
- Evaluate results with suitable metrics and honest limitations
- Present a reproducible data-science project
Included in the monthly fee
- One computer per student, every class
- Teacher feedback on your code or your files, in class
- Free access to the browser coding lab between classes
- Certificate with a QR code any employer can verify
- One free make-up sitting per month if work or family interrupts you
Money
Fee and batch
- Monthly feeDiscuss with admissions
- Duration6 months
- Admission feeNone
- Exam feeNone
- Certificate feeNone
Pay by cash, JazzCash or bank transfer at the desk. The office marks it in your ledger the same day.
Practise tonight
Test the course before you pay for it
Every course here has a matching practice track in the browser lab, and the first ten tasks are free without an account. Open it tonight, finish a few tasks, and see whether this is the subject you actually enjoy.
Common questions