Data Science Bootcamp
The AI Era needs more Data Specialists. Are you equipped? This is the best advanced Data Science course yet; built for individuals with a background in tech/math/data, ready to step into mid-level Data and AI roles. Master Data Science, Machine Learning, Neural Networks, LLMs with AI integration through hands-on projects.
2026 Intakes in Progress
Full-time Hybrid
Start Date:
August 31st, 2026
Course Duration:
31 Weeks
Mode of Learning:
3 days remote & 2 days in-person | Mon - Fri from 8am - 5pm E.A.T
Tuition Fee:
Ksh 200,000
Brochure:
Full-time Remote
Start Date:
August 31st, 2026
Course Duration:
31 Weeks
Mode of Learning:
100% Remote Classes | Mon - Fri from 8am - 5pm E.A.T
Tuition Fee:
Ksh 174,000
Brochure:
Part-time Remote
Start Date:
August 31st, 2026
Course Duration:
50 Weeks
Mode of Learning:
100% Remote Classes | Tues/Wed/Thurs from 6pm - 9 pm E.A.T
Tuition Fee:
Ksh 200,000
Brochure:
Build your future in Data and AI
Data science has always been about extracting insight from complexity. In this age, that means one more thing: knowing how to work intelligently with AI — not just as a user, but as a practitioner who understands what’s under the hood.
This Data Science with AI Bootcamp prepares you to do exactly that. In less than a year of coursework, you’ll build a rigorous foundation in Python, statistics, machine learning, and deep learning — then go further, into Large Language Models, prompt engineering, fine-tuning, and production-ready MLOps.
You won’t just learn to use AI tools. You’ll learn to evaluate them critically, integrate them responsibly, and apply them alongside your own analytical judgment — which is what employers actually want.
Course Details
Data Science is an interdisciplinary field that deploys algorithms and other scientific methods and processes to acquire insights and knowledge from data. Data Scientists are equipped with the knowledge of how to use data, tell a story, and derive insights for businesses. Many industries are now leveraging data for decision-making in their day-to-day operations and forecasting.
| Dedicated LLM Module | A full course on Large Language Models — transformer architecture, inference, fine-tuning with HuggingFace, prompt engineering, and responsible deployment. Not a footnote: a core subject. |
| MLOps with MLflow | Learn experiment tracking, model versioning, and the model registry so your work is reproducible, documented, and production-ready from day one. |
| Deeper AI Integration | AI tools are woven into every module — for EDA, SQL generation, model interpretation, and storytelling — with a consistent focus on critical evaluation, not blind trust. |
| Redesigned Part-Time Track | 50 weeks of evening sessions (6–9pm, Tues/Wed/Thur) with breathing room built in. Designed for working professionals who can’t press pause on their careers. |
Tools You’ll Work With:- Python · SQL · PySpark · Pandas · NumPy · Scikit-Learn · Keras / PyTorch · HuggingFace Transformers · LightGBM · MLflow · LangChain · DuckDB · Polars · Plotly · Dash · Jupyter Notebooks · GitHub · Claude / ChatGPT (Advanced Data Analysis) · GitHub Copilot
This programme is for you if:
-
- You’re a working professional in finance, marketing, operations, or a similar field and want to move into Data Science or ML engineering
- You are a University/College student or graduate who has IT/math/statistics background and wants to bridge the gap between academic theory and applied industry tools
- You’re a junior developer or analyst with some Python or SQL experience looking to expand into Data Science, Machine Learning, and AI
- You’ve completed Moringa’s Introduction to Data Science or an equivalent course and are ready for the next level
- Programming Experience: have some basic Python or programming familiarity. We teach Python fundamentals in Module 1, but move quickly.
- Quantitative background: Comfortable with high school algebra; basic statistics is helpful. A quantitative degree or prior exposure to data analysis will set you up well.
- Education: University/college education (ongoing or graduated) in a relevant field.
- Setup: Laptop with Intel i5 11th Gen / Ryzen 5 4000+ or better, 16GB RAM, 512 GB NVMe SSD storage. Stable internet connection.
- Application: Complete & pass the technical assessment as part of your application.
- A world-class curriculum co-developed with Flatiron School and updated with LLMs, MLOps, and agentic AI modules
- Project-based learning with real-world datasets and business problems.
- Access to industry mentors and live instructor-led sessions.
- 12 months of graduate support
- Career coaching woven into the programme
- Hybrid and remote options with flexible fee payment plans
Dive into the fast-growing world of data science
Get StartedCurriculum Overview
Full-time Pacing
- Onboarding week
- Course Overview
- Accounts setup
- Tools, system configurations, and installations
- Learn how to develop professional soft skills, build your professional brand with a standout resume, LinkedIn profile, and portfolio
- Introduction to Python Programming:Build a solid foundation in Python for data science. Master control flow, loops, functions, and core data structures, then apply file handling and logging to real scripts.
- Catch-up & Assessment Week
- Introduction to Data Science: Learn how to turn raw data into insight. Work with different data types and collection methods, object-oriented programming, apply statistical analysis, and build visualizations using Pandas and Seaborn.
- Introduction to SQL: Gain the database skills every data professional needs. Write SQL queries to filter, group, and join data, then combine SQL with Pandas for deeper analysis. Covers database design fundamentals and the data engineering lifecycle.
- Catch-up & Assessment Week
- Cloud Computing, Generative AI & Dashboards: Explore the scalable ecosystem of cloud computing for distributed data processing. Master PySpark and evaluate Python-native big-data alternatives. Learn advanced dashboarding with Plotly and Dash for deployable analysis applications. Leverage generative AI tools to accelerate workflows while critically evaluating outputs
- Inferential Statistics: Develop a strong grasp of probability, distributions, and statistical inference. Apply hypothesis testing and A/B testing, and run inference on means, proportions, and categorical data — while learning when to use parametric versus non-parametric methods.
- Regression: Master linear, multiple, and logistic regression. Learn model fitting, statistical inference, regularization, and model selection methods.
- Catch-up & Assessment Week
- Prepare to land your dream Data Scientist role through targeted interview coaching.
- Practice technical and behavioral questions to refine your portfolio and gain confidence through mock interviews and personalized feedback.
- Introduction to Machine Learning: Learn how machines learn. Master supervised learning fundamentals and statistical learning theory, then build and compare models — logistic regression, decision trees, support vector machines — using metrics like ROC AUC.
- Machine Learning with Scikit-Learn:Go hands-on with the algorithms behind modern ML. Build k-Nearest Neighbors for classification, use SVD to power recommender systems, and apply k-means clustering and PCA for dimensionality reduction.
- Natural Language Processing, Time Series & Neural Networks:Build the models shaping AI today. Master NLP techniques like text classification and vectorization, learn to model trends with time series analysis, and get hands-on with neural networks using Keras.
- Catch-up & Assessment Week
- Neural Networks & Similar Models: Advance your deep learning skills with techniques such as normalization and regularization. Learn Convolutional Neural Networks (CNNs) for computer vision, Recurrent Neural Networks (RNNs) and attention mechanisms for sequence modeling, Transformers, and Generative AI.
- Large Language Models :This module equips you with the skills to deploy and optimize cutting-edge machine learning systems in real-world scenarios. You will explore the open-source MLOps stack and learn to manage the entire ML lifecycle, including deployment, monitoring, and version control. You will also master techniques for fine-tuning pre-trained models and leveraging prompt engineering to optimise output for specific tasks.
- Catch-up & Assessment Week
- Summative technical assessment week
- Bring all skills together to develop two distinct projects framing around solving real-world business problems. Integrate supervised learning (classification) and unsupervised learning (with LLM integration) methodologies. Demonstrate end-to-end professional data science capability with AI-augmented analysis.
- Data Science with AI Final Project
Part-time Pacing
- Onboarding week
- Course Overview
- Accounts setup
- Tools, system configurations, and installations
- Introduction to Python Programming: Build a solid foundation in Python for data science. Master control flow, loops, functions, and core data structures, then apply file handling and logging to real scripts.
- Introduction to Data Science: Learn how to turn raw data into insight. Work with different data types and collection methods, object-oriented programming, apply statistical analysis, and build visualizations using Pandas and Seaborn.
- Introduction to SQL: Gain the database skills every data professional needs. Write SQL queries to filter, group, and join data, then combine SQL with Pandas for deeper analysis. Covers database design fundamentals and the data engineering lifecycle.
- Catch-up & Assessment Week
- Cloud Computing, Generative AI & Dashboards: Explore the scalable ecosystem of cloud computing for distributed data processing. Master PySpark and evaluate Python-native big-data alternatives. Learn advanced dashboarding with Plotly and Dash for deployable analysis applications. Leverage generative AI tools to accelerate workflows while critically evaluating outputs
- Catch-up & Assessment Week
- Inferential Statistics: Develop a strong understanding of probability, distributions, and statistical inference. You’ll apply hypothesis testing, A/B testing, and inference techniques for means, proportions, and categorical data, while learning when to use parametric versus non-parametric methods. The module concludes with a summative assessment.
- Regression: Master regression techniques used in real-world data science, from linear and multiple regression to logistic regression. You’ll learn model fitting, statistical inference, regularization, and model selection methods, reinforced through practical exercises and summative assessments.
- Catch-up and Assessment Week
- Introduction to Machine Learning: Learn how machines learn. Master supervised learning fundamentals and statistical learning theory, logistic regression, decision trees, support vector machines — using metrics like ROC AUC.
- Machine Learning with Scikit-Learn: Go hands-on with the algorithms behind modern ML. Build k-Nearest Neighbors for classification, use SVD to power recommender systems, and apply k-means clustering and PCA for dimensionality reduction.
- Natural Language Processing, Time Series & Neural Networks: Build the models shaping AI today. Master NLP techniques like text classification and vectorization, learn to model trends with time series analysis, and get hands-on with neural networks using Keras.
- Catch-up & Assessment Week
- Neural Networks & Similar Models: Advance your deep learning skills with techniques such as normalization and regularization. Learn Convolutional Neural Networks (CNNs) for computer vision, Recurrent Neural Networks (RNNs) and attention mechanisms for sequence modeling, Transformers, and Generative AI.
- Large Language Models :This module equips you with the skills to deploy and optimize cutting-edge machine learning systems in real-world scenarios. You will explore the open-source MLOps stack and learn to manage the entire ML lifecycle, including deployment, monitoring, and version control. You will also master techniques for fine-tuning pre-trained models and leveraging prompt engineering to optimise output for specific tasks.
- Catch-up & Assessment Week
- Bring all skills together to develop two distinct projects framing around solving real-world business problems. Integrate supervised learning (classification) and unsupervised learning (with LLM integration) methodologies. Demonstrate end-to-end professional data science capability with AI-augmented analysis.
- Data Science with AI Final Project
Learning data science opens the door to a fulfilling and dynamic career, enabling you to leverage data to drive innovation and solve real-world problems.
Career opportunities
- Data Scientist
- Data Analyst
- Machine Learning Engineer
- Data Engineer
- AI Research Scientist
- Big Data Engineer/Architect
- Product Analyst
Analyze complex data sets to discover patterns, build predictive models, and drive business decisions using machine learning and statistical methods.
Collect, process, and perform statistical analysis on data to help organizations make informed decisions, often focusing on reporting and visualization.
Design, develop, and deploy machine learning models and algorithms that can automate tasks and make predictions from data.
Build and maintain the infrastructure and architecture for data generation, ensuring that data is accessible and well-organized for analysis.
Develop new algorithms and models in artificial intelligence and machine learning, pushing the boundaries of what’s possible with data-driven technologies.
Design and manage large-scale data processing systems to handle massive data sets, typically using technologies like Hadoop, Spark, or cloud services.
Analyze product usage data to help companies improve their products and customer experience based on user behavior and feedback.
Analyze complex data sets to discover patterns, build predictive models, and drive business decisions using machine learning and statistical methods.
Collect, process, and perform statistical analysis on data to help organizations make informed decisions, often focusing on reporting and visualization.
Design, develop, and deploy machine learning models and algorithms that can automate tasks and make predictions from data.
Build and maintain the infrastructure and architecture for data generation, ensuring that data is accessible and well-organized for analysis.
Develop new algorithms and models in artificial intelligence and machine learning, pushing the boundaries of what’s possible with data-driven technologies.
Design and manage large-scale data processing systems to handle massive data sets, typically using technologies like Hadoop, Spark, or cloud services.
Analyze product usage data to help companies improve their products and customer experience based on user behavior and feedback.
Ready to take a step in transforming your career?
