Programming
Write Python, work with notebooks and query with SQL. Learn to debug, clean messy data and explain the steps in your analysis.
Data Science · 24 weeks · Online
Online Data Science Bootcamp: learn to code, analyse and build predictive models.
Join a data science bootcamp for beginners that connects Python programming, statistics and machine learning. Over 24 weeks, learn to turn raw data into evidence, build models and explain your results through guided portfolio projects.
Your bootcamp at a glance
Enrollment is open. Confirm your cohort's class times and start date with the team on WhatsApp.
- Delivery
- Online learning with mentorship and project feedback
- Duration
- 24 weeks; cohort timetable confirmed on WhatsApp
- Course fee
- ₦350,000
- Learning path
- Foundations, guided practice and a capstone
Three connected skill areas
Understand the code, the evidence and the model
This data science and analytics boot camp builds the foundations before introducing predictive modelling.
Statistics
Understand variation, sampling and uncertainty. Interpret evidence carefully and recognise the assumptions behind statistical conclusions.
Machine learning
Build baseline, regression, classification and clustering models. Evaluate their performance and recognise when predictions are unreliable.
Your 24-week curriculum
From your first Python notebook to an evaluated model
Eight modules build on one another, with time for practice, feedback and a final case study. The teaching sequence below shows how the 24 weeks are organised.
01
Weeks 1–4
Python programming foundations
Start with variables, data types, conditions, loops and functions. Learn to read errors, work with files and organise a notebook so someone else can follow your code.
Practical outputA Python notebook that loads a practice dataset and answers simple business questions.
02
Weeks 5–7
Data preparation with pandas, NumPy and SQL
Select and transform columns, handle missing values and duplicates, join tables and check data types. Use SQL filtering, joins and aggregation to prepare structured data for analysis.
Practical outputA cleaned dataset, reusable preparation code and documented quality checks.
03
Weeks 8–10
Statistics and probability for analysis
Explore distributions, averages, variation, probability and sampling. Learn what confidence intervals and hypothesis tests can tell you, how assumptions affect conclusions, and why correlation does not establish causation.
Practical outputA statistical analysis notebook that explains uncertainty and the limits of its conclusions.
04
Weeks 11–12
Exploratory analysis and visualisation
Investigate patterns, outliers and relationships using pandas, Matplotlib and Seaborn. Choose charts for the question being asked and communicate findings without overstating the evidence.
Practical outputAn exploratory report with annotated charts, findings and questions for further investigation.
05
Weeks 13–16
Supervised machine learning
Understand features, targets and baseline models. Build regression models for numerical predictions and classification models for categories using scikit-learn, including linear models and decision trees.
Practical outputA baseline and a trained model, with a clear explanation of the prediction task.
06
Weeks 17–19
Model evaluation and responsible use
Separate training, validation and test data; fit preprocessing on training data; and recognise data leakage and overfitting. Compare regression errors and classification precision, recall and F1. Discuss privacy, bias and when a model should not be used.
Practical outputA reproducible evaluation report that compares models and documents their limitations.
07
Weeks 20–21
Unsupervised learning and segmentation
Explore clustering, feature scaling and customer segmentation. Interpret groups carefully, check whether they are useful for the business question and distinguish clusters from proven categories.
Practical outputA segmentation notebook with visual profiles and a discussion of limitations.
08
Weeks 22–24
Capstone and portfolio communication
Bring the workflow together: define a question, prepare data, explore patterns, build and evaluate a model, then explain the result. Use Git and GitHub to document your work and make the steps reproducible.
Practical outputA portfolio repository with code, a README, evaluation results and a concise presentation.
Tools and programming
What you will use, and why
The toolkit supports data preparation, statistical analysis, modelling and reproducible project work.
Python
Programming, reusable functions and the core data science workflow.
Jupyter / Google Colab
Notebook-based coding, experiments and written explanations.
pandas and NumPy
Data cleaning, table operations and numerical calculations.
SQL
Querying, joining and summarising structured datasets.
Matplotlib and Seaborn
Exploratory charts and visual explanations of patterns.
scikit-learn
Preprocessing, regression, classification, clustering and model evaluation.
Git and GitHub
Version control, project documentation and portfolio presentation.
Portfolio projects
Build evidence of your skills
Work with practice datasets. Document the question, your methods, your results and what the evidence cannot tell you.
Sales analysis and regression
What patterns appear in a practice sales dataset, and how well can a model estimate a numerical outcome?
Cleaned data and an exploratory chart report
A baseline compared with a regression model on held-out data
An explanation of prediction errors, assumptions and business relevance
Customer churn classification
Can a model identify customers at risk of leaving in a sample dataset?
A documented target and checks for data leakage
A classification pipeline with precision, recall and a confusion matrix
A discussion of false positives, missed cases and responsible use
Customer segmentation
What useful customer groups can you explore using a practice purchasing dataset?
Prepared and scaled features for a clustering exercise
Charts and written profiles describing the resulting groups
Recommendations that distinguish observed patterns from assumptions
Who it is for
A starting point for curious, committed beginners
The course is for graduates, career switchers and analysts who want to move into Python and predictive modelling. You do not need prior machine-learning experience, but you should be ready to practise coding and work through numerical ideas.
Prepare a laptop or desktop, reliable internet and time for exercises between lessons. Final device requirements, software access and study commitments will be shared before enrollment.
Schedule and support
Choose a cohort that fits your actual availability
The programme runs for 24 weeks. Class days and times are confirmed through WhatsApp for your cohort. Before enrolling, ask for the timetable in West Africa Time (WAT, UTC+1), live attendance requirements, recording access and expected independent study hours.
If you plan to study alongside a job, confirm that the actual weekly commitment fits your availability. Online delivery does not automatically mean part-time or self-paced.
Mentorship, project feedback, portfolio guidance and a certificate of completion are included. Ask the team about completion requirements and the support available between classes.
Chat on WhatsAppTuition and enrollment
₦350,000 for the full programme
The course fee includes training, mentorship, guided project feedback and a certificate after successful completion.
Pay in full or use the academy's part-payment option: 70% (₦245,000) upfront and 30% (₦105,000) before mid-course. Confirm your cohort and payment arrangements on WhatsApp first.
Your learning outcomes
Finish with work you can explain
Write Python code and SQL queries to prepare data.
Use statistical reasoning to interpret findings and uncertainty.
Build and evaluate models against a meaningful baseline.
Present reproducible projects with clear limitations and recommendations.
Choose your learning path
Data Science or Data Analysis?
Choose Data Analysis if your immediate goal is Excel, SQL, dashboards and business intelligence reporting. Explore Data Science if you want a deeper programming and statistics foundation for building and evaluating models.
View Data AnalysisCareer direction
Build foundations you can demonstrate
The course develops skills relevant to analytics work and further study in data science. Portfolio projects can help you explain your reasoning, coding and model evaluation in applications and interviews.
Data scientist roles vary in their experience and qualification requirements. The goal is a credible foundation and practical work to discuss, with continued learning beyond the bootcamp.
Data science boot camp FAQ
Know what to expect before you commit
Answers about coding, statistics, remote learning and enrollment.
Is this online data science bootcamp open for enrollment?
Yes. Chat on WhatsApp to discuss your goals, confirm the cohort timetable and fee, and get help with enrollment. The team can explain the weekly study commitment and the equipment you will need.
Is the data science bootcamp for beginners?
The learning path starts with Python and statistics foundations, so prior machine-learning experience is not expected. You should be comfortable using a computer and willing to practise coding and numerical reasoning. Basic algebra, percentages and chart reading are useful starting skills.
How much programming does a data science coding bootcamp involve?
Programming is central to this course. You will work towards writing Python functions, cleaning data with pandas, querying with SQL and building scikit-learn workflows. The emphasis is on understanding and explaining your code as well as running it.
What statistics and machine learning will I learn?
The statistics coverage includes distributions, variation, probability, sampling, confidence intervals and hypothesis testing. Machine learning covers regression, classification, clustering, preprocessing and evaluation, with attention to leakage, overfitting and responsible interpretation. Advanced deep learning is outside this introductory outline.
Can I take the data science bootcamp part-time while working?
The course runs for 24 weeks. Class times are confirmed through WhatsApp for your cohort. Before enrolling alongside work, ask for teaching days and times in WAT, live attendance requirements, recording access and expected independent study hours. Use those details to check that the weekly commitment fits your job and other responsibilities.
Can I join this remote data science bootcamp from outside Lagos?
Yes. Classes are online for learners in Nigeria and elsewhere. You will need a laptop or desktop, reliable internet and the ability to attend classes in West Africa Time (WAT, UTC+1). Ask the team about equipment and software setup before your cohort starts.
How does a data science and analytics boot camp differ from Data Analysis?
Both involve preparing data, investigating questions and communicating findings. Our available Data Analysis course focuses on Excel, SQL, dashboards and business intelligence. The Data Science course adds a stronger Python, statistics and machine-learning focus for building and evaluating predictive models.
Are mentorship, a certificate and career support included?
The programme includes guided project feedback, mentorship, portfolio support and a certificate after meeting completion requirements. Ask the team for assessment criteria and support arrangements when you enroll. Completing a bootcamp does not guarantee a data scientist role.