Data Analytics

• Install Python and VS Code, terminal basics, your first program and reading error messages
• Variables, numbers, strings, booleans and type conversion
• User input, f-strings and string methods
• Labs: naira to dollar converter and a transfer-fee calculator

• Comparison and logical operators; if, elif and else
• while and for loops, range, break and continue
• Nested loops, input validation and simple menus
• Labs: grade classifier, FizzBuzz and an ATM simulator with a 3-attempt PIN limit

• Functions, parameters, return values and scope
• Lists, tuples, dictionaries and sets, including lists of dictionaries
• Labs: shopping-cart total with a discount code and a student result sheet

• Read and write text files; try, except, else and finally
• Modules, the standard library, JSON and CSV, pip and virtual environments
• Git basics and pushing a project to GitHub
• Capstone build, demo and trainer code review: the console expense tracker

• The analysis cycle: question, data, clean, explore, conclude, communicate
• Jupyter and VS Code, comprehensions, reading CSV and JSON
• Git, GitHub and a standard analysis project layout

• NumPy arrays and vectorisation
• pandas Series and DataFrames, loc and iloc, filtering and sorting
• New columns, aggregates, Excel import and export

• Missing data and duplicates
• Types, text and dates, including messy ₦ amounts and phone numbers
• Outliers and simple data-quality checks

• groupby, agg, pivot_table and crosstab
• merge and concat, and checking row counts after every join
• Method chaining and a mid-course practical (2 hours, unaided)

• matplotlib and seaborn; choosing a chart that fits the question
• A repeatable EDA checklist
• Interactive charts with Plotly and storytelling with data

• SELECT, WHERE, GROUP BY and HAVING in SQLite and PostgreSQL
• Joins, subqueries and CTEs
• Window functions: ranking, running totals and month-over-month change

• Resampling, rolling averages and seasonality
• Descriptive statistics and confidence intervals
• Hypothesis testing and A/B tests explained honestly

• Dashboard design in Power BI (Windows) or Streamlit (any OS)
• Data privacy and the Nigeria Data Protection Act
• Stakeholder presentations and final evaluation

• Advanced pandas, Parquet, databases and larger-than-memory files
• Data validation and reusable cleaning functions
• Missing-data strategies, encoding, scaling, date and lag features, and spotting data leakage

• The scikit-learn workflow, Pipeline and ColumnTransformer
• Regression and classification with the right metrics and a baseline to beat
• Cross-validation, overfitting and a 3-hour mid-course practical

• Random Forest and gradient boosting with XGBoost, tuning and comparison
• Imbalanced data (fraud and default), clustering and model interpretation with SHAP
• Forecasting with baselines, exponential smoothing and time-based validation

• ETL design, loading PostgreSQL and scheduling pipelines
• A PySpark awareness session, reproducible projects and MLflow experiment tracking
• Serving a model with FastAPI and Streamlit, Docker, CI and monitoring

• Ethics, bias and data protection
• Capstone build, testing, documentation and a model card
• Portfolio, GitHub profile, CV, mock interviews and demo day

Start with Basic if you are new to programming. Join Intermediate if you can write functions, use lists and dictionaries, read CSV and JSON files and handle errors with try/except. Join Advanced if you are comfortable with pandas, SQL joins and window functions, descriptive statistics and hypothesis testing. Admissions can give you a short placement test if you are unsure.

Basic is 4 weeks (12 sessions of 3 hours) at ₦150,000. Intermediate is 2 months (8 weeks, 24 sessions of 3 hours) at ₦250,000. Advanced is 3 months (12 weeks, 36 sessions of 3 hours) at ₦300,000.

After Basic you can write, run and debug your own Python programs that read and save data. After Intermediate you can take a messy dataset, clean it, analyse it with pandas and SQL, visualise it, test a hypothesis and present findings in a dashboard and a short report. After Advanced you can build, evaluate and deploy a predictive model and the automated data pipeline that feeds it.

Python, VS Code and Jupyter, pandas, NumPy, seaborn and Plotly, SQLite and PostgreSQL, and Power BI Desktop (Windows) or Streamlit for dashboards. Advanced adds scikit-learn, XGBoost, FastAPI, Docker and MLflow.

Relatable Nigerian data wherever possible, such as retail sales, POS and transfer transactions, school results, fuel prices, exchange rates and telecom churn. We use public, synthetic or anonymised data, and every level includes a session on data protection and the Nigeria Data Protection Act.

Yes. You receive a certificate of completion when you finish a level.

Classes start from 10am. The exact schedule depends on the package you choose, and our admissions team will confirm it when you apply.

Classes are live and instructor-led, taught by certified, experienced trainers. You are assessed through quizzes, assignments and a capstone project that you present and defend live.

Complete the application form at the bottom of this page or message us on WhatsApp. Our payment terms and conditions are linked in the tuition card at the top of this page.