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1
Introduction to Data Science
Introduction to Data Science, Data Science Tools, Job Opportunities for Data Scientists, etc.
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2
Essential Python
Python programming fundamentals tailored for data work — syntax, data structures, and libraries (Pandas, NumPy) that form the backbone of every data science workflow.
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3
Essential Mathematics & Statistics
Core statistics and linear algebra (probability, distributions, hypothesis testing, matrices) needed to understand how data science and machine learning algorithms actually work.
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4
Data Analytics
Collecting, cleaning, exploring, and visualizing data to uncover patterns and generate actionable business insights using modern tools like Pandas, Power BI, and Tableau.
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5
Machine Learning
Building supervised and unsupervised models (regression, classification, clustering) using Scikit-learn, with a focus on practical deployment rather than theory alone — including model evaluation and tuning for real-world accuracy.
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6
Deep Learning & Generative AI
Introduction to neural networks, and hands-on work with modern generative AI tools and large language models (LLMs) — including prompt engineering, fine-tuning basics, and building AI-powered applications (chatbots, content generation, RAG systems) that reflect where the industry is headed.
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7
Big Data Analytics
Working with large-scale datasets using distributed computing tools (e.g., Apache Spark) and cloud-based data warehouses, preparing learners for enterprise-scale data challenges.
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8
MLOps & Model Deployment
Taking models from notebooks to production — version control, containerization (Docker), API deployment, and monitoring models in live environments, a critical and increasingly in-demand 2026 skill set.
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9
Data Science Projects
End-to-end, real-world projects (e.g., predictive analytics, recommendation systems, AI-powered dashboards) simulating actual industry challenges across business, health, finance, and other sectors.
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10
Portfolio Building
Structured guidance to package projects, code, and case studies into a professional portfolio (GitHub, personal site) that showcases skills to employers and clients.
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11
Certification Exam
Preparation and sitting for an internationally recognized data science/analytics certification, validating skills for the global job market. Certifications/Recognitions: Google Data Analytics Professional Certificate • PCED Endorsed • IBM Data Science Professional • TEN Certified
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12
Internship
Practical placement applying data science skills to real organizational problems, building workplace experience ahead of employment.
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Data and Machine Learning · Program
Curriculum outline
What you need to join
Data Science
The Data Science program is a 6-month, intensive, project-based course that takes learners from Python and statistics fundamentals to advanced Machine Learning, Big Data, and modern AI-driven analytics. Designed around real-world industry practices — including generative AI tools, MLOps, and cloud-based data pipelines — the program equips participants to build, deploy, and communicate data-driven solutions, graduating with a strong portfolio, international certification, and hands-on internship experience.
- 24 weeks
- 260,000 FCFA
- 12 modules
- Pay in 3
- Minimum of a Higher National Diploma (or equivalent); a background in Mathematics, Statistics, Economics, or Computer Science is an advantage
- Holders of a Bachelor's degree and above or other post-secondary qualifications are also an added advantage
- Basic computer literacy is required
- Applicants must be at least 18 years old
- No prior programming experience required — the program starts from Python and statistics fundamentals
- Strong analytical mindset and comfort with numbers and logical reasoning
- Willingness to commit to a rigorous, hands-on, 6-month intensive schedule
- Access to a personal laptop (capable of running data analysis and modeling tools) is required
- Completed registration form
- Valid ID (National ID card, passport, or student ID)
- Payment of program fee (260,000 FCFA)