Data Science & Analytics
A practical analytics and data science program covering Excel, SQL, Python, Power BI, statistics, machine learning, artificial intelligence, storytelling, and end-to-end portfolio projects.
Career paths
Roles this prepares you for
- Junior Data Analyst
- Business Intelligence Analyst
- Reporting Analyst
- Product Analyst
- Operations Analyst
- Junior Data Scientist
- Junior Machine Learning Practitioner
Intended audience
Who it is for
- Complete beginners entering data careers
- Business professionals who want analytical skills
- Aspiring data analysts
- Aspiring business intelligence analysts
- Learners progressing toward data science and machine learning
Curriculum
Module by module
Month 1 — Data Analysis Foundations
- Week 1 — Excel for Data Analytics: data types, tables, cleaning, functions, lookups, PivotTables, charts and dashboards
- Week 2 — Data Analysis and Business Thinking: analytical questions, KPIs, quality, missing values, duplicates, outliers and findings
- Week 3 — SQL Fundamentals: relational concepts, SELECT, filtering, aggregations, joins, subqueries, CTEs, dates and cleaning
- Week 4 — Advanced SQL Analytics: windows, ranking, running totals, cohorts, segmentation, views and optimisation
Project: Kenya Retail Intelligence and Performance Analysis
Month 2 — Python Analytics and Business Intelligence
- Week 5 — Python Fundamentals: variables, conditions, loops, functions, collections, files, exceptions, modules, environments and Jupyter
- Week 6 — Pandas and NumPy: DataFrames, importing, filtering, missing data, grouping, merging, reshaping, features and time series
- Week 7 — Visualisation and Storytelling: Matplotlib, Seaborn, chart choices, visual hierarchy, EDA and analytical narratives
- Week 8 — Power BI: Power Query, modelling, relationships, star schemas, DAX, time intelligence, security and publishing
Project: Executive Sales and Profitability Dashboard
Month 3 — Statistics and Machine Learning
- Week 9 — Statistics: populations, samples, central tendency, variance, probability, confidence intervals, hypotheses, correlation and A/B tests
- Week 10 — Machine Learning Foundations: workflows, supervised and unsupervised learning, splits, leakage, preprocessing, features and pipelines
- Week 11 — Regression and Classification: linear/logistic regression, trees, forests, metrics, cross-validation and tuning
- Week 12 — Clustering and Interpretation: K-means, segmentation, dimensionality reduction, explainability, fairness and limitations
Project: Customer Churn Prediction and Segmentation
Month 4 — Applied AI, Deployment and Capstone
- Week 13 — Applied AI: generative AI, LLMs, prompts, RAG, embeddings, semantic search, responsible use and evaluation
- Week 14 — Model Deployment: saving models, inference, FastAPI endpoints, validation, Docker and monitoring concepts
- Week 15 — Capstone Development: definition, collection, cleaning, EDA, features, models, dashboards, recommendations and documentation
- Week 16 — Presentation and Portfolio: storytelling, GitHub documentation, CV descriptions, interviews and stakeholder communication
Project: Kenya Business Intelligence and Predictive Analytics Platform
Capstone project
Kenya Business Intelligence and Predictive Analytics Platform
What you build
- Analyse customers, products, revenue and operational performance
- Build a PostgreSQL analytical database
- Perform SQL and Python analysis
- Create a Power BI executive dashboard
- Develop a predictive machine-learning model
- Expose predictions through a small API
- Document data limitations and ethical considerations
- Present business findings and recommendations
What you hand in
- Clean analytical dataset
- Excel analysis
- SQL scripts
- Python analysis notebook
- Statistical analysis
- Power BI dashboard
- Machine-learning pipeline
- Model evaluation report
- Prediction API
- Complete GitHub repository
- Executive presentation
Assessment
How your progress is assessed
- Weekly practical assignments
- Technical articles and research
- Mini-projects
- Code and project reviews
- Monthly build assessments
- Peer presentations
- Capstone milestones
- Final technical presentation
- GitHub portfolio quality
- Documentation and communication
FAQ
Frequently asked questions
How long are the full data programmes?
A typical journey is 24 weeks: 4 months of instructor-led learning followed by a 2-month internship. Students who need longer usually finish within 8–9 months.
What do the data programmes cost?
Online: KES 7,500 per month (KES 30,000 total). In person: KES 12,500 per month (KES 50,000 total). Paying upfront saves 10%.
When is the next intake?
The next full Data Science & Analytics and Data Engineering intake begins in January 2027.
What is the difference between Data Science & Analytics and Data Engineering?
Data Science & Analytics focuses on analysing data and building predictive models. Data Engineering focuses on the pipelines, warehouses and streaming systems that make data reliable.
How is Machine Learning here different from the School of Artificial Intelligence?
In this school, machine learning is taught as data-driven modelling and analytical methods. The School of Artificial Intelligence focuses on using and integrating AI tools in everyday work.
Is the certificate accredited?
Certificates are issued by LuxDevHQ. They are not externally accredited.
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