What is business analytics? Descriptive, diagnostic, predictive, and prescriptive analytics · The data-driven organisation: how leading companies use data to outcompete · Business analytics roles: data analyst, BI developer, business intelligence manager · KPI (Key Performance Indicator) frameworks: designing, selecting, and tracking business KPIs · Balanced Scorecard: strategy maps, perspectives, and lagging vs leading indicators · Data governance in business: data ownership, quality standards, and master data management · DAMA DMBOK framework: introduction to enterprise data management · Introduction to business analytics tools: Excel, Power BI, Tableau, SQL, Python, Google Analytics

Financial statements: income statement, balance sheet, and cash flow statement — structure and relationships · Ratio analysis: liquidity, profitability, solvency, and efficiency ratios — calculation and interpretation · Trend analysis: year-on-year and month-on-month growth, moving averages, and forecasting · Budget vs actual analysis: variance analysis, waterfall charts, and management reporting · Revenue analysis: product-level, segment-level, and geographic revenue breakdown · Cost analysis: fixed vs variable costs, cost allocation, and contribution margin analysis · Financial modelling in Excel: building a 3-statement financial model and sensitivity analysis · Cash flow forecasting: 13-week cash flow model and working capital analysis · Financial dashboards in Power BI: P&L dashboard, revenue tracker, and budget variance views · Hands-on lab: analyse a company’s 3-year financial statements and build a management dashboard

Customer segmentation: RFM analysis (Recency, Frequency, Monetary value) in Excel and Python · Customer Lifetime Value (CLV): calculation methods and strategic implications · Marketing funnel analysis: acquisition, activation, retention, referral, and revenue (AARRR) · Campaign performance analysis: CTR, conversion rate, ROI, and cost per acquisition (CPA) · A/B testing for marketing: statistical significance, test design, and interpreting results · Web analytics with Google Analytics 4: sessions, users, conversion events, and attribution · Social media analytics: engagement rate, reach, impressions, and sentiment basics · Churn analysis: customer retention analysis and churn prediction using logistic regression · Market basket analysis: association rules, support, confidence, and lift — product recommendations · Hands-on lab: conduct a full customer segmentation and marketing performance analysis

Operational KPIs: OEE (Overall Equipment Effectiveness), cycle time, throughput, and defect rates · Inventory analysis: ABC analysis, EOQ (Economic Order Quantity), and stockout risk · Supply chain analytics: lead time analysis, supplier performance, and on-time delivery tracking · Process improvement: Lean and Six Sigma data tools — control charts, Pareto, and fishbone · Demand forecasting: moving averages, exponential smoothing, and seasonal decomposition in Excel and R · Logistics and delivery analytics: route efficiency, delivery SLA compliance, and last-mile analytics · Project analytics: Gantt charts, critical path, earned value analysis (EVM) · HR analytics: headcount, turnover rate, time-to-hire, performance distribution, and payroll analysis · Operations dashboards in Power BI: production, supply chain, and workforce KPI views · Hands-on lab: build an operations analytics report on a manufacturing or logistics dataset

Power BI advanced: composite models, incremental refresh, row-level security (RLS) · Advanced DAX: time intelligence, running totals, dynamic segmentation, and SWITCH statements · Tableau advanced: LOD expressions, table calculations, blending data sources, and Tableau Prep · Business storytelling with data: structuring a data story — situation, complication, resolution · Report design principles: layout, hierarchy, white space, and mobile-first dashboard design · Self-service BI: empowering business users to explore data without analyst support · Real-time dashboards: streaming data, automatic refresh, and live connections in Power BI · Automated reporting: Power Automate for scheduled report distribution · Executive presentations: translating analytics into board-level insights and strategic recommendations · Hands-on lab: build an advanced executive dashboard in Power BI with RLS and automated delivery

Python for business: pandas for financial data, time series analysis, and business metrics · Business data cleaning in Python: handling real-world messy commercial datasets · Automated reporting with Python: generating Excel reports and PDF summaries programmatically · SQL for business: extracting revenue, customer, and operational data from relational databases · Advanced SQL for business: window functions for running totals, rankings, and cohort analysis · Python visualisation for business: matplotlib and plotly for commercial chart types · Predictive analytics for business: linear regression for sales forecasting in Python · Customer churn prediction: logistic regression model — build, evaluate, and deploy in Streamlit · Introduction to machine learning for business: decision trees for customer classification · Hands-on lab: build an end-to-end business analytics pipeline in Python — from data to automated report

Why STATA and EViews matter for business economists, policy analysts, and development finance · STATA for business data: importing financial, market, and firm-level datasets · Regression analysis in STATA: OLS for sales, pricing, and demand modelling · Panel data in STATA: fixed effects and random effects for firm-level longitudinal business data · Difference-in-differences in STATA: evaluating the impact of business policy changes · Introduction to EViews: interface, workfile structure, and business time-series data · Unit root tests in EViews: ADF and KPSS stationarity tests for economic time series · OLS regression in EViews: estimation, diagnostic tests (heteroscedasticity, autocorrelation) · ARIMA modelling in EViews: Box-Jenkins methodology for business forecasting · VAR model in EViews: modelling interactions between macroeconomic and business variables · Granger causality test: testing lead-lag relationships between business and economic variables · GARCH model in EViews: volatility modelling for stock prices, exchange rates, and commodity prices · Forecasting in EViews: producing and evaluating business and economic forecasts · Hands-on lab: complete a business economic analysis in EViews — stationarity, regression, ARIMA, and forecast for a financial or commodity dataset

Africa’s business data landscape: unique opportunities, challenges, and key data sources · Mobile money and fintech analytics: M-Pesa, Flutterwave, OPay, and mobile payment transaction data · Informal sector analytics: measuring and modelling informal market activity in Nigeria and Africa · SME financial analysis in emerging markets: micro-enterprise income, expenditure, and profitability · Agricultural value chain business analytics: farm-to-market price and trader margin calculation · Telecoms and digital economy: subscriber data, ARPU, churn, and data consumption trends · Energy analytics in Africa: electricity access, load shedding impact, and off-grid solar market data · Poverty and consumer analytics: living standards, expenditure patterns, and market segmentation · Cross-border trade: ECOWAS trade flows, import/export data, and informal commerce · Nigeria-specific data: NBS (National Bureau of Statistics), CBN, NIMC, and open government datasets · Donor and development finance analytics: tracking ODA flows, DFI investment, and development impact · Hands-on lab: analyse a Nigerian or African business dataset — mobile money transaction analysis or SME profitability study

Competitive intelligence: using data to understand market position and competitor performance · Scenario analysis and sensitivity modelling: what-if analysis for business decisions · Data-driven strategy: translating analytics findings into strategic recommendations · Building a data culture: how to present analytics to sceptical executives and non-data leaders · Ethics in business analytics: data privacy, GDPR compliance, and responsible use of customer data · Consulting frameworks for data analysis: MECE, issue trees, and hypothesis-driven analysis · Capstone project: choose a real business dataset (sales, finance, marketing, or HR) — conduct a full analysis, build an executive dashboard, and present strategic recommendations in a written report

Conduct financial, marketing, operations, and HR analytics using industry-standard business tools · Build executive-level BI dashboards in Power BI and Tableau with advanced DAX and LOD expressions · Apply Python and SQL to automate business reporting and build predictive commercial models · Conduct regression, panel data, and time-series analysis using STATA and EViews · Apply business analytics frameworks to African and Nigerian market contexts including mobile money and SME data · Design KPI frameworks and balanced scorecards aligned to business strategy · Communicate data findings to executive and board-level audiences with impact · Achieve a credential aligned to Microsoft PL-300, Tableau, Google Analytics, EViews, STATA, and DAMA DMBOK standards

Complete all 9 modules, pass a proctored examination (minimum 75%), and submit a business analytics project including a cleaned business dataset, an executive Power BI dashboard, financial analysis, and a 1,500-word strategic recommendations report.

Business Analyst, Data Analyst (Commercial), Marketing Analyst, Financial Analyst (Junior), Operations Analyst, BI Developer, Strategy Analyst, Management Consultant (Junior), Product Analyst. Average starting salary: $50,000–$90,000 USD.