Data Science is the field focused on extracting insights, building predictive models, and turning raw data into actionable knowledge to solve real-world problems, support decision-making, and drive business intelligence and AI applications.
Data Science Bootcamp
Data Science is one of the most in-demand careers globally. Companies in finance, health, logistics, manufacturing, and technology rely on Data Scientists to uncover insights, build predictive models, and drive strategic decisions.
This Data Science Program teaches you how to think like a scientist, using data, statistics, programming, and machine learning to solve real-world problems. You’ll learn the full cycle: collecting data, cleaning it, analyzing it, visualizing patterns, and building intelligent models.
This 12-weeks course is ideal for beginners and intermediate learners. No advanced math is required, just curiosity and basic computer skills.
What is Data Science?
- Physical – 8 hours a week
- Mon, Tue, Wed, Thur
- During the day 9AM to 11 AM or 2:00PM to 4:00PM
- Saturday option available
- Virtual – 8 hours a week
- Mon, Tue, Wed, Thur
- One-on-one virtual instructor consultations during the day.
Course Outline/ Curriculum
1. Python for Data Science
2.Statistics & Probability for Data Science
3. Exploratory Data Analysis (EDA)
| 4. SQL for Data Science
5. Machine Learning Foundations
6. Deep Learning Foundations
7. Final Capstone Project |
| High Demand | AI engineers are among the most sought-after professionals worldwide. |
| Future-Proof Career | AI is driving the next wave of technological advancement. |
| Lucrative Opportunities | AI skills open doors to high-paying global roles. |
| Real Impact | Build solutions that solve real-world problems. |
Kenya: KES 150,000–400,000/month, depending on experience and company.
Entry-level: ~KES 150,000–200,000
Mid-level: ~KES 200,000–300,000
Senior/Lead: ~KES 300,000–400,000+
Global benchmark: Data Scientists often earn $90,000–150,000/year in higher-income countries.
Frequently Asked Questions (FAQ)
Do I need coding experience to take this course?
Not necessarily.
Basic programming experience is helpful, but it isn’t a strict requirement. We start from the fundamentals and gradually build up your skills in Python, SQL, and data tools—even if you’ve never coded before. What matters most is your commitment to learning.
By the time you complete the program, you’ll confidently write production-level code for data pipelines, automation scripts, and analytics workflows.
What is the mode of learning?
Hybrid & Virtual Combined
Can I get a job after this course and will you help me?
Can I get a job after this course, and will you help me?
Yes – Data Scientists are highly in demand globally. Companies across industries rely on data to make decisions, build predictive models, and improve operations, creating strong demand for skilled professionals.
Career Paths You Can Pursue
With the skills gained in this program, you can confidently apply for roles such as:
- Data Scientist
- Machine Learning Engineer
- Business Intelligence (BI) Analyst
- Data Analyst
- Predictive Analytics Specialist
- Research/Data Science Associate
We Support You Until You Get the Job
We provide ongoing support, including:
- Interview preparation and mock interviews
- Portfolio and GitHub project development
- CV and LinkedIn optimization
- Job alerts and application guidance
- Practical capstone projects aligned with real industry needs
What are some of the activities of an Data Scientist?
Data Collection & Cleaning: Gathering data from multiple sources and preparing it for analysis.
Exploratory Data Analysis (EDA): Identifying patterns, trends, and anomalies in data.
Model Building & Machine Learning: Developing predictive models and algorithms to solve business problems.
Data Visualization & Reporting: Creating dashboards, charts, and reports to communicate insights.
Business Insights & Decision Support: Translating data findings into actionable recommendations for strategy and operations.
Collaboration: Working with stakeholders, product teams, and engineers to apply data-driven solutions.
Real-World Examples
- Banking: Building models to detect fraudulent transactions or predict customer churn.
- Healthcare: Analyzing patient data to support diagnosis, treatment planning, or drug research.
- Retail & E-commerce: Designing recommendation systems to personalize product suggestions.
- Marketing: Using analytics to optimize campaigns, segment customers, and predict trends.
- Logistics & Supply Chain: Forecasting demand, optimizing routes, and reducing operational costs.
What skills will I gain from this course?
Programming & Scripting: Python for analysis, data manipulation, and automation.
Data Cleaning & Preparation: Handling messy or missing data to create analysis-ready datasets.
Exploratory Data Analysis (EDA): Discovering patterns, trends, and insights using statistics and visualization.
Machine Learning & Predictive Modeling: Building models to forecast outcomes and solve business problems.
Data Visualization & Reporting: Creating dashboards and visualizations to communicate insights effectively.
Business Insights & Decision Support: Translating data into actionable recommendations.
Real-World Projects & Portfolio Development: Hands-on experience with real datasets, capstone projects, and professional documentation.
Collaboration & Communication: Working with teams and presenting data-driven insights to stakeholders.
Will I get a certificate after completing the course?
Yes. You’ll receive a recognized and verifiable certificate of completion that can be shared with employers, added to LinkedIn, or used in startup pitches.
What makes this course different from others?
- Hands-on learning: Work with real-world datasets, predictive models, and analytics projects.
- African context: Case studies from local data-driven success stories like M-KOPA, Flutterwave, Branch, Twiga Foods, Kobo360, and Apollo Agriculture.
- Expert mentorship: Guidance from experienced Data Scientists and tech startup founders who have built scalable analytics and AI systems in Africa.