Career Path

Data Science

Data is the new oil, fueling competitive advantage for businesses and governments. Data Science offers lucrative, sustainable careers, with a 650% job growth since 2012. Industry analysis projects 11.5 million new jobs by 2026. This course equips students with job-ready skills, using open-source tools and real-world datasets through lectures and hands-on practice, ensuring mastery of cutting-edge data science techniques.

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What you will learn

Foundations of Data Science with Python

This first session maps the end-to-end data science workflow and gets the toolchain running. Students set up Python, Jupyter, and VS Code, manage environments with Anaconda or uv, cover version control fundamentals with Git and GitHub, and start working alongside AI coding assistants such as Claude, ChatGPT, and Copilot.

Python Programming for Data

The Python a data scientist actually writes: core data types, operators, control flow and iteration, lists, tuples, dictionaries and sets. Students define and test functions, use comprehensions, move into numerical computing with NumPy arrays, and handle files and errors safely with context managers and try/except.

Data Loading with Pandas

An introduction to pandas Series and DataFrames, and to getting data in from anywhere it lives. Students load from CSV, Excel, JSON, and Parquet, read straight from SQL databases, inspect and summarise what they have, work with basic indexing and slicing, and write results back out to files and databases.

Data Cleaning and Preprocessing with Pandas

Data quality work in full. Students identify and handle missing values, detect and treat outliers, remove duplicates, standardise types, clean text with regular expressions, parse dates, times and time zones, and reshape data with melt, pivot, stack and unstack around tidy data principles.

Exploratory Data Analysis with Pandas

Understanding a dataset before modelling it. Students work through descriptive statistics and distributions, boolean indexing and the query method, grouped aggregations, pivot tables and cross-tabulations, merges and joins across tables, and time-series fundamentals such as resampling and rolling windows — structuring an EDA from question to recommendation.

Data Visualisation and Dashboards

Building charts that carry an argument. Students create static visuals with matplotlib and seaborn, interactive ones with plotly, learn to choose the right chart for the data, apply visual design principles around labels, colour and accessibility, then design and publish a Power BI dashboard and present it to a non-technical audience.

Statistical Analysis and A/B Testing

Inference with scipy.stats and statsmodels. Students cover probability distributions, sampling and the Central Limit Theorem, confidence intervals, and the hypothesis testing framework, running t-tests, chi-square and ANOVA, sizing experiments with power analysis, and analysing an A/B test end to end while avoiding p-hacking and multiple-testing traps.

Introduction to Machine Learning

Supervised and unsupervised learning through the scikit-learn API. Students work with train/test splits and cross-validation, fit and interpret linear and logistic regression, and evaluate models with MAE, MSE, RMSE and R² for regression and accuracy, precision, recall, F1 and ROC-AUC for classification, while learning to recognise underfitting and overfitting.

Machine Learning in Practice

Moving from a fitted model to a working one. Students cover decision trees and random forests, k-means clustering and choosing k, feature engineering with scaling, encoding and binning, scikit-learn Pipelines and ColumnTransformer, hyperparameter tuning with GridSearchCV, a conceptual look at neural networks, and how to interpret models for business stakeholders.

NLP, LLMs, and Responsible AI

Working with text and large language models. Students cover tokenisation, stemming and lemmatisation, TF-IDF, embeddings and document similarity, classical sentiment analysis, then call LLM APIs from Python for classification and information extraction, meet retrieval-augmented generation conceptually, and evaluate output for hallucination, bias, fairness and privacy.

SQL Fundamentals

Relational data from design to query. Students cover database concepts, DDL and DML, SELECT with WHERE, ORDER BY and LIMIT, aggregation with GROUP BY and HAVING, every join type, subqueries and CTEs, window functions such as ROW_NUMBER, RANK, LAG and LEAD, and connect Python to databases with SQLAlchemy.

Reproducible Workflows, Capstone Project, and Career Preparation

The final session turns the programme into evidence. Students structure a reproducible project with managed dependencies, clear READMEs and a Git branching workflow, publish with Quarto, Streamlit, or a FastAPI deployment, then frame their own question, model the data, present findings, and build a portfolio ready for technical interviews.

Experiential learning with
success-based pricing

Similar Data Science programs across Africa cost $2,500-$5,000. We've already priced ours lower with the same quality: expert instructors, hands-on projects, job-ready skills, AND exclusive AI-powered job search support that literally no one else offers.

Tuition starts at

¢7,800

Pay only ¢780 commitment fee

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Commitment fee refundable if no value is delivered after 7 days of enrolment. Ts & Cs Apply.

Weekly personalized career readiness insights.

Portfolio guidance aligned with employer expectations.

Human coaching support throughout your learning journey.

Lifetime access to SFAN network and platforms.

An employer-facing profile showcasing your strengths and job readiness.

Personalized job search and opportunity guidance.

Personalized guidance for navigating your career journey..

Portfolio-backed certificate showcasing learning and applied work.

Elevator pitch video for select high-performing students.

AI-assisted career support to expand opportunities.

Program Schedule

Week 0

ONBOARDING

Week 1

FOUNDATIONAL

Week 2

DISCOVERY

Week 3

HANDS-ON

Week 4-10

UPSCALE

Week 12

CAPSTONE

Week 13

PRESENTATION

12 Weeks, Blended

Experience

Entry-Level
Mid-Level
Senior Level

Must Have

Laptop and a stable internet connection.
15 hours weekly to actively participate.
B.Sc. in Computer Science, Social sciences, or Statistics.

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Re-inventing learning

Understand your strengths and where you can grow.

Level up with tailored learning resources from subject-matter experts.

Demonstrate career readiness in ways traditional systems fail to do.

Connect with mentors, recruiters, industry professionals, and co-dreamers, bypassing traditional barriers.

Learn from practitioners

Laurent Smeets

10+ years of Data Science experience.

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