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Jooi Centre - Kikuyu Town

Data Science

Fast-track your Data Science career with hands-on projects, real-world datasets, and cutting-edge skills. Learn to apply statistical modeling, machine learning, and AI to extract insights and build predictive models.

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Program Highlights

Key details at a glance — here’s what you can expect from our Data Science course.

Learning Format:

Hybrid: Online & Physical

Next Intake:

Intake Ongoing

Duration:

12 Months

About the Course

This immersive program introduces you to the field of Data Science — from statistical foundations to building and deploying machine learning models. You’ll learn programming languages like Python, explore advanced data manipulation, and master techniques to predict trends and automate decision-making. By the end, you’ll be able to develop data-driven solutions and maintain a portfolio of impactful projects.

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Sneak Peek of the Course

Course Details

This course is a practical, career-focused path into data science. You’ll work through structured modules that combine theory with applied labs and projects, producing a portfolio of analyses and models that demonstrate your ability to extract knowledge and insights from data.

What is Data Science? ▼

Data Science is an interdisciplinary field that uses scientific methods, processes, algorithms, and systems to extract knowledge and insights from structured and unstructured data. This course will teach you how to build models that predict outcomes, classify data, and identify patterns that help businesses and organizations make smarter decisions.

Who is this course for? ▼
  1. Students: Build a strong foundation in statistics, programming, and machine learning.
  2. Aspiring Data Scientists: Transition into an entry-level data science role with a solid portfolio.
  3. Analysts & Engineers: Upskill to leverage predictive modeling and advanced analytics in your current role.
  4. Curious Minds: Anyone with a passion for problem-solving and a desire to build intelligent systems.
What are the course prerequisites? ▼
  1. Basic Programming Knowledge: Familiarity with a language like Python is recommended but not required.
  2. Access to a Computer & Internet: A modern browser and stable internet connection for labs.

The course starts with foundational concepts and builds up to advanced topics, so beginners are welcome.

Why learn this course? ▼
  1. High-Impact Skills: Learn to build predictive models and automated decision systems.
  2. Create Real-World Projects: Develop a portfolio of data science projects to impress employers.
  3. Master Key Technologies: Get hands-on with Python, Pandas, Scikit-learn, and more.
  4. Career-Ready: Prepare for a versatile, in-demand role in a rapidly growing field.
  5. Problem-Solving: Strengthen analytical and statistical thinking to tackle complex challenges.
Why study at Lio College? ▼
  1. Hands-On Projects: A majority of the course is practical labs and collaborative builds.
  2. Industry-Experienced Instructors: Learn from data science professionals and researchers.
  3. Career Support: Guidance on building a portfolio, preparing CVs, and interview practice.
What is the mode of study? ▼

Choose between in-person on campus or online attendance. Classes are live and interactive, supported by an online learning platform with code labs, resources, and mentor support to reinforce learning.

Curriculum Overview

The Data Science curriculum is divided into five phases, guiding you from mathematical foundations to advanced machine learning and deployment. Each phase blends theory with hands-on projects so that by the end, you’ll have a strong portfolio and industry-ready skills.

Career Development & Professional Skills ▼
  • Portfolio Workshops: Package and present your projects for employers.
  • Career Coaching: CV, LinkedIn, interview prep, and career pathways.
  • Soft Skills: Critical thinking, communication, and teamwork.
  • Industry Mentorship: Sessions with practicing data scientists.
Phase 1 – Mathematics, Statistics & Programming Foundations ▼
  1. Mathematics: Linear algebra, probability, and calculus essentials.
  2. Statistics: Hypothesis testing, distributions, and statistical inference.
  3. Python Programming: Syntax, functions, loops, and data structures.
  4. Collaboration Tools: Git, GitHub, and version control basics.
Phase 2 – Data Handling & Visualization ▼
  1. Data Manipulation: Using Pandas and NumPy for cleaning and analysis.
  2. SQL Databases: Writing queries, joins, and managing datasets.
  3. Visualization Tools: Matplotlib, Seaborn, and Plotly.
  4. Mini Project: Perform an exploratory data analysis (EDA) and present insights.
Phase 3 – Machine Learning Fundamentals ▼
  1. Supervised Learning: Regression and classification models.
  2. Unsupervised Learning: Clustering, PCA, and dimensionality reduction.
  3. Model Evaluation: Accuracy, precision, recall, and cross-validation.
  4. Hands-On Project: Build and evaluate a predictive ML model.
Phase 4 – Deep Learning & Big Data Tools ▼
  1. Neural Networks: Introduction to deep learning and backpropagation.
  2. Frameworks: Hands-on with TensorFlow and PyTorch.
  3. Big Data: Basics of Hadoop, Spark, and scalable data processing.
  4. Applied Project: Image recognition or NLP-based project.
Phase 5 – Capstone Project & Industry Readiness ▼
  1. Capstone Project: End-to-end data science project from raw data to deployment.
  2. Model Deployment: Using Flask, FastAPI, or Streamlit to showcase work.
  3. Portfolio Building: Organizing GitHub repositories and documentation.
  4. Industry Preparation: Mock interviews, coding tests, and presentation skills.

Career Pathways


Completing this course opens doors to high-demand roles at the intersection of statistics, computer science, and business. Select a pathway below to explore how you can apply your skills.

Data Scientist

Develop predictive models and machine learning algorithms to solve complex problems and drive innovation in a variety of industries.

Machine Learning Engineer

Focus on building and deploying scalable and robust machine learning systems and pipelines in production environments.

AI Researcher

Work on developing new algorithms and models, pushing the boundaries of what is possible in artificial intelligence.

Business Intelligence Analyst

Use data science techniques to create powerful dashboards and reports, providing strategic insights to business leaders.

Ready to Launch Your Career in Data Science?

Join the next intake and gain practical, in-demand skills that will launch your career in the world of data and AI.

Apply Now