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Data Science, Machine Learning & AI Bootcamp
This intensive bootcamp provides participants with practical, hands-on skills in data science, machine learning, and …
Short Courses
Beginner
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
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.
________________________________________
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
________________________________________
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
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Introduction to data science, machine learning, and AI6 min
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Types of data: structured, semi-structured, and unstructured6 min
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Data science lifecycle and problem framing5 min
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Tools and environments for data analysis4 min
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Real-world applications of data science and AI4 min
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Data sourcing and data collection methods7 min
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Data cleaning, pre-processing, and feature preparation5 min
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Exploratory data analysis (EDA) techniques6 min
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Data visualization and storytelling with data5 min
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Identifying patterns, trends, and anomalies4 min
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Supervised vs. unsupervised learning6 min
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Common algorithms: regression, classification, clustering5 min
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Model training, validation, and testing6 min
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Performance metrics and model evaluation5 min
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Avoiding overfitting and underfitting3 min
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Decision trees, random forests, and ensemble methods7 min
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Introduction to neural networks and deep learning concepts5 min
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Natural language processing (NLP) basics4 min
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Computer vision fundamentals5 min
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Applying models to practical datasets5 min
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Interpreting and explaining model outputs7 min
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Translating analytics into business and policy insights5 min
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Building simple dashboards and decision-support outputs6 min
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Communicating results to technical and non-technical audiences5 min
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Responsible use of AI-driven insights4 min
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Data pipelines and workflow automation6 min
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Introduction to model deployment concepts5 min
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Integrating models into applications and systems4 min
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Monitoring model performance and data drift3 min
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Scaling analytics solutions5 min
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Bias, fairness, and transparency in data and models5 min
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Data privacy and protection considerations5 min
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Ethical decision-making in AI projects6 min
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Governance frameworks for data science initiatives4 min
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Managing risks in AI deployment3 min
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End-to-end data science project6 min
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Problem definition and dataset selection5 min
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Model development and evaluation5 min
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Presentation of findings and recommendations4 min
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Peer review and feedback4 min
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