Artificial Intelligence for Data Analysts Training Course

Course overview

Data analysts are increasingly expected to do more than produce dashboards and retrospective reports. They must identify patterns in large, messy datasets, forecast likely outcomes, explain drivers behind performance changes, and assess where generative AI can safely accelerate analysis. This course addresses the practical gap between conventional analytics work and AI-enabled decision support: selecting appropriate models, preparing reliable data, validating outputs, and presenting results that business stakeholders can act on and trust.

Over five days, participants build an applied AI workflow for analytics. They use Python and Jupyter notebooks to prepare data, train and evaluate supervised machine-learning models, segment records with clustering, and create time-series forecasts. They also learn how to use generative AI for SQL drafting, exploratory analysis, documentation and narrative reporting while checking outputs for factual errors, bias and data leakage. The course covers model evaluation metrics, feature engineering, prompt patterns for analysts, retrieval-augmented analysis concepts, Power BI integration, and AI governance controls.

Teaching combines instructor demonstrations, guided notebook labs, analyst-focused case studies and peer review of model results. Participants work with a realistic commercial dataset and progressively assemble an AI-enabled analysis pack: a documented data preparation pipeline, model comparison, forecast or segmentation output, stakeholder-facing Power BI visualisation, prompt library, and risk-control checklist. This deliverable gives attendees a repeatable framework for applying AI to a live reporting, customer, operations or commercial analysis problem after the course.

The course is designed for experienced analysts who already work with structured data and need a disciplined route into machine learning and generative AI. It is equally relevant to managers seeking analysts who can improve speed and analytical depth without introducing ungoverned AI use.

Course objectives

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

• Prepare analysis-ready datasets using profiling, cleansing, encoding and feature-engineering techniques
• Build and compare regression and classification models in Python using scikit-learn workflows
• Evaluate predictive models using cross-validation, confusion matrices, ROC-AUC, precision, recall and error metrics
• Create customer or operational segments using k-means clustering and interpret segment characteristics
• Produce time-series forecasts and communicate forecast uncertainty to decision-makers
• Apply prompt patterns to generate and verify SQL, Python analysis code and executive data narratives
• Design AI-assisted Power BI outputs that combine model results, business measures and explanatory visuals
• Produce an AI analytics implementation pack containing model documentation, validation evidence and governance controls

Benefits of attending

Target competencies

Who should attend

• Data Analysts — who need to add predictive, generative and automated analysis methods to established reporting work
• Senior Data Analysts — who must recommend defensible AI use cases and validate analytical outputs
• Business Intelligence Analysts — who want to embed forecasts, segments and AI-generated explanations in dashboards
• Commercial Analysts — who analyse customer, sales, pricing or campaign data and need stronger prediction methods
• Operations Analysts — who need to forecast demand, identify process patterns and prioritise operational interventions
• Analytics Managers — who oversee analyst teams and need practical controls for trustworthy AI-enabled reporting

Requirements and prerequisites

Participants should be comfortable working with tabular data in Excel, SQL, Power BI or a similar reporting environment, including joins, filters, aggregations and basic data-quality checks. They should understand descriptive statistics such as averages, distributions and correlation, and be able to interpret business KPIs. Prior Python experience is helpful but not essential: notebook exercises provide guided code and explain the required syntax. Participants do not need prior machine-learning experience, advanced calculus, data-science qualifications or experience building AI models. A laptop capable of running a modern web browser and Python notebooks is required.

Training methodology

Each day combines short instructor-led explanations with guided work in Jupyter notebooks, using a commercial analytics case that develops across the week. Participants profile data, write and adapt Python code, compare model outputs, test generative-AI prompts and build decision-ready visuals in Power BI. Case discussions focus on choosing methods that fit a business question rather than applying algorithms by default. Peer review sessions require participants to challenge assumptions, metrics and recommendations. The final workshop converts each participant's work into a practical implementation plan for an identified workplace use case.

Course outline

Day 1: AI foundations for the working data analyst

• AI, machine learning and generative AI use cases in business analytics
• Framing analytical questions as prediction, classification, segmentation or optimisation problems
• Data quality profiling with pandas and exploratory data analysis
• Missing-value treatment, outlier handling and duplicate-record controls
• Train, validation and test data splits for analyst-built models
• Data leakage, target leakage and proxy-variable risk
• Jupyter notebook structure, reproducibility and analytical documentation

Workshop: Participants profile a commercial dataset in Jupyter and produce a documented data-quality and AI-use-case assessment.

Day 2: Predictive modelling and performance evaluation

• Feature engineering for dates, categories, ratios and behavioural measures
• Linear regression for revenue, cost and demand prediction
• Logistic regression and decision trees for classification problems
• Cross-validation and baseline-model comparison
• Confusion matrices, precision, recall, F1 score and ROC-AUC
• Mean absolute error, root mean squared error and residual analysis
• Model interpretability using feature importance and partial dependence concepts

Workshop: Participants build and evaluate competing churn or conversion models, then recommend one model using a business-facing scorecard.

Day 3: Segmentation, forecasting and analytical storytelling

• K-means clustering for customer and operational segmentation
• Selecting cluster counts with inertia and silhouette-score evidence
• Cluster profiling with descriptive statistics and business labels
• Time-series components: trend, seasonality and irregular variation
• Forecasting with lag features and regression-based time-series models
• Forecast intervals, scenario assumptions and forecast-error communication
• Power BI visuals for segments, predictions and forecast narratives

Workshop: Participants create a segment profile or demand forecast and publish a Power BI page that explains the recommended business action.

Day 4: Generative AI for analyst workflows

• Prompt patterns for analytical task definition, constraints and output formats
• Using generative AI to draft and inspect SQL queries
• Using generative AI to explain, refactor and document Python analysis code
• AI-assisted exploratory analysis and hypothesis generation
• Generating executive summaries from validated analytical results
• Hallucination detection, source checking and numerical verification
• Retrieval-augmented generation concepts for governed enterprise data

Workshop: Participants build a tested prompt library for SQL, analysis-code review and executive reporting against the case-study dataset.

Day 5: Governed deployment and workplace application

• Bias assessment across customer, demographic and operational data segments
• Privacy, confidential-data handling and access-control considerations
• Model cards, data dictionaries and analytical decision logs
• Monitoring data drift, performance drift and prompt-output changes
• Human review checkpoints for AI-assisted reporting
• Prioritising AI analytics use cases by value, feasibility and risk
• Communicating AI recommendations, limitations and ownership to stakeholders

Workshop: Participants complete and present an AI analytics implementation pack containing a use-case canvas, model evidence, prompt controls and 90-day action plan.

Tools & standards covered

Python, Jupyter Notebook, Microsoft Power BI, OpenAI API

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