Data Science for Business Analysts Training Course

Course overview

Business analysts are increasingly expected to move beyond static dashboards and descriptive reporting: they must test whether a pattern is real, estimate the likely impact of a decision, and explain analytical findings to business owners. This course equips analysts to work confidently with operational, customer, financial and digital data when questions involve prediction, segmentation, anomaly detection or measurement. Participants learn how to turn an ambiguous business request into a structured data science problem with measurable success criteria, usable data and defensible recommendations.

The course covers the practical data science workflow for business analysis: framing hypotheses, extracting data with SQL, preparing data in Python, profiling quality issues, selecting useful variables, and applying core statistical and machine-learning techniques. Participants use regression for forecasting and driver analysis, classification for propensity and risk decisions, clustering for customer or process segmentation, and anomaly detection for operational exceptions. They also learn to interpret model performance using metrics such as precision, recall, ROC-AUC, MAE and RMSE, avoiding common errors such as data leakage, biased samples and misleading correlations.

Delivery combines instructor-led explanation with guided work in Jupyter Notebook, SQL and Microsoft Power BI. A running business case gives participants a realistic dataset and decision context, while daily exercises build toward an end-of-course analytics brief. Each participant leaves with a documented analysis notebook, a decision-focused Power BI dashboard, a model evaluation summary and a stakeholder-ready recommendation that can serve as a template for workplace projects.

The course is designed for experienced business analysts, senior analysts and product or operations professionals who already work with data and need stronger analytical methods without training as full-time data scientists.

Course objectives

By the end of this course, participants will be able to:

• Frame business questions as testable analytical problems with measurable decision criteria
• Extract and join business data using SQL queries, filters, aggregations and window functions
• Clean, profile and transform datasets in Python using pandas
• Evaluate data quality, missingness, outliers and potential sources of analytical bias
• Build and interpret regression models for forecasting and business-driver analysis
• Apply classification, clustering and anomaly-detection methods to business datasets
• Assess model suitability using confusion matrices, precision, recall, ROC-AUC, MAE and RMSE
• Produce a stakeholder-ready analytics brief with recommendations, limitations and next actions

Benefits of attending

Target competencies

Who should attend

• Business Analysts — who need to answer predictive and diagnostic questions beyond standard reporting
• Senior Business Analysts — who must defend data-led recommendations to managers and stakeholders
• Product Analysts — who analyse user behaviour, conversion and retention decisions
• Operations Analysts — who identify process bottlenecks, exceptions and demand patterns
• Commercial Analysts — who assess customer segments, pricing drivers and sales opportunities
• Business Intelligence Analysts — who want to add statistical and predictive methods to dashboard work

Requirements and prerequisites

Participants should be comfortable working with spreadsheets or BI reports and interpreting common business measures such as revenue, cost, conversion rate, volume and percentage change. Prior exposure to SQL SELECT statements, tables, joins or filters is useful, and participants should be able to follow basic algebra and chart interpretation. No prior Python programming, machine-learning experience, calculus or advanced statistics is required; Python exercises are taught step by step in Jupyter Notebook. Participants should bring a laptop capable of running a modern web browser and organisation-approved desktop tools where required.

Training methodology

The instructor introduces each method through a business decision scenario before demonstrating it in SQL, Python or Power BI. Participants then complete guided notebook exercises using a consistent case dataset, including data-quality investigation, feature preparation, model building and visual interpretation. Small-group reviews focus on selecting appropriate methods and challenging unsupported conclusions. Daily outputs are retained and refined across the week. The final session uses a presentation workshop in which participants convert their analysis into a recommendation, state model limitations and define an application plan for a live workplace question.

Course outline

Day 1: Framing business questions and preparing data

• Translating business decisions into analytical problem statements
• Defining target variables, units of analysis and success measures
• Distinguishing descriptive, diagnostic, predictive and prescriptive questions
• SQL SELECT, WHERE, GROUP BY and aggregate functions
• Joining transactional, customer and reference tables
• Data profiling with pandas DataFrames
• Missing values, duplicates, outliers and data-quality rules

Workshop: Participants profile a customer-orders dataset, write SQL extraction queries and produce a data-quality issue log with recommended treatments.

Day 2: Exploratory analysis and statistical reasoning

• Distribution analysis using histograms, box plots and summary statistics
• Cross-tabulation and cohort comparisons
• Correlation analysis and the limits of correlation
• Sampling, confidence intervals and practical significance
• Hypothesis testing for business comparison questions
• Feature engineering from dates, categories and transaction histories
• Detecting leakage, selection bias and confounding variables

Workshop: Participants investigate a falling conversion-rate case, test competing explanations and produce an evidence table separating valid findings from unsupported claims.

Day 3: Forecasting and predictive classification

• Train, validation and test dataset design
• Linear regression for driver analysis
• Regression diagnostics and residual interpretation
• Forecast error measures including MAE and RMSE
• Logistic regression for binary business outcomes
• Decision trees and feature importance
• Classification metrics including precision, recall and ROC-AUC

Workshop: Participants build and compare churn-propensity models, then recommend a decision threshold based on retention campaign costs and likely value.

Day 4: Segmentation, anomalies and visual communication

• Customer and operational segmentation use cases
• K-means clustering and cluster-profile interpretation
• Choosing the number of clusters with elbow and silhouette methods
• Anomaly detection using statistical thresholds and isolation forests
• Power BI data modelling and calculated measures
• Designing decision-focused Power BI visuals
• Communicating uncertainty, assumptions and model limitations

Workshop: Participants segment customers and create a Power BI dashboard that shows segment value, behaviour and recommended treatment actions.

Day 5: From analysis to business action

• Selecting methods that match decision risk and data maturity
• Model monitoring and performance-drift indicators
• Data governance, privacy and responsible analytical use
• Documenting assumptions, lineage and reproducible analysis steps
• Writing recommendations with quantified impact ranges
• Structuring an analytics brief for executive review
• Planning a workplace data science proof of concept

Workshop: Participants complete an end-to-end analytics brief containing a Jupyter Notebook, Power BI dashboard, model evaluation summary and implementation recommendation for the case sponsor.

Tools & standards covered

Python, Jupyter Notebook, Microsoft Power BI, SQL

A typical training day

Live online deliveries follow the same structure in the East Africa Time zone, with shorter screen blocks and longer breaks.

What the fee includes

How you can take this course

Certification

Participants who complete the full five days receive the Skillset Development Certificate of Completion, stating the course title, course code, dates and delivery format — suitable for professional-development records and employer reimbursement.

Frequently asked questions