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Data Science, Machine Learning & AI Bootcamp

60 hours
0 students
Added Apr 14, 2026
Course Overview:
This intensive bootcamp provides participants with practical, hands-on skills in data science, machine learning, and artificial intelligence (AI). It focuses on building end-to-end data capabilities—from data collection and analysis to model development, evaluation, and deployment—while linking technical outputs to real-world business and public sector use cases.
Participants will gain a strong foundation in data-driven problem-solving and applied AI, enabling them to contribute effectively to analytics, innovation, and digital transformation initiatives across sectors.
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Learning Objectives:
By the end of this bootcamp, participants will be able to:
Understand core concepts in data science, machine learning, and AI
Collect, clean, and analyse structured and unstructured data
Build and evaluate basic machine learning models
Apply data analytics and AI techniques to real-world problems
Interpret model outputs and communicate insights effectively
Understand ethical, governance, and practical considerations in AI use
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Target Audience:
Aspiring data scientists and analysts
ICT and digital transformation professionals
Business analysts and M&E professionals
Engineers and technical professionals
Public sector analysts and planners
Researchers and graduate students
Private sector professionals transitioning into data roles
Entrepreneurs and innovation teams

Who Should Take This Course

This course is ideal for beginners and those new to the field. No prior experience is required.

Course Content

  • Introduction to data science, machine learning, and AI
    6 min
  • Types of data: structured, semi-structured, and unstructured
    6 min
  • Data science lifecycle and problem framing
    5 min
  • Tools and environments for data analysis
    4 min
  • Real-world applications of data science and AI
    4 min

  • Data sourcing and data collection methods
    7 min
  • Data cleaning, pre-processing, and feature preparation
    5 min
  • Exploratory data analysis (EDA) techniques
    6 min
  • Data visualization and storytelling with data
    5 min
  • Identifying patterns, trends, and anomalies
    4 min

  • Supervised vs. unsupervised learning
    6 min
  • Common algorithms: regression, classification, clustering
    5 min
  • Model training, validation, and testing
    6 min
  • Performance metrics and model evaluation
    5 min
  • Avoiding overfitting and underfitting
    3 min

  • Decision trees, random forests, and ensemble methods
    7 min
  • Introduction to neural networks and deep learning concepts
    5 min
  • Natural language processing (NLP) basics
    4 min
  • Computer vision fundamentals
    5 min
  • Applying models to practical datasets
    5 min

  • Interpreting and explaining model outputs
    7 min
  • Translating analytics into business and policy insights
    5 min
  • Building simple dashboards and decision-support outputs
    6 min
  • Communicating results to technical and non-technical audiences
    5 min
  • Responsible use of AI-driven insights
    4 min

  • Data pipelines and workflow automation
    6 min
  • Introduction to model deployment concepts
    5 min
  • Integrating models into applications and systems
    4 min
  • Monitoring model performance and data drift
    3 min
  • Scaling analytics solutions
    5 min

  • Bias, fairness, and transparency in data and models
    5 min
  • Data privacy and protection considerations
    5 min
  • Ethical decision-making in AI projects
    6 min
  • Governance frameworks for data science initiatives
    4 min
  • Managing risks in AI deployment
    3 min

  • End-to-end data science project
    6 min
  • Problem definition and dataset selection
    5 min
  • Model development and evaluation
    5 min
  • Presentation of findings and recommendations
    4 min
  • Peer review and feedback
    4 min

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