Introduction

In today's hyper-competitive business landscape, relying solely on historical data for decision-making is no longer sufficient; organizations must harness the power of Predictive Analytics and Forecasting in BI to anticipate future trends, identify emerging opportunities, and mitigate potential risks, transforming reactive strategies into proactive, data-driven initiatives. By leveraging advanced statistical models and machine learning algorithms, businesses can move beyond "what happened" to understand "what will happen" and "why," enabling smarter resource allocation, optimized operations, and enhanced strategic planning. This training course is meticulously designed to equip business analysts, data scientists, BI developers, financial planners, marketing strategists, and operations managers with cutting-edge knowledge and practical skills in understanding predictive modeling concepts, mastering various forecasting techniques, applying machine learning algorithms for business predictions, leveraging predictive features within leading BI tools, interpreting model outputs for actionable insights, and addressing the ethical and governance considerations of predictive analytics. Participants will gain a comprehensive understanding of how to build, deploy, and interpret predictive models that drive competitive advantage and foster a truly forward-looking, data-driven organization.

Duration

10 days

Target Audience

• Business Analysts
• Data Analysts
• Data Scientists (Beginner/Intermediate)
• Business Intelligence Developers
• Financial Planners & Analysts
• Marketing & Sales Strategists
• Operations Managers
• Supply Chain Analysts
• Risk Managers
• Anyone involved in forecasting, planning, or strategic decision-making

Objectives

• Understand the fundamental concepts of predictive analytics and forecasting.
• Master various forecasting techniques, including time series models.
• Learn to apply common machine learning algorithms for business predictions.
• Develop proficiency in leveraging predictive features within leading BI tools.
• Understand how to prepare and engineer data for predictive modeling.
• Explore methods for evaluating and validating predictive models.
• Develop skills in interpreting model outputs and communicating predictions effectively.
• Learn about anomaly detection and outlier analysis for proactive insights.
• Understand the ethical considerations and governance of predictive analytics.
• Formulate strategies for integrating predictive insights into business processes.
• Apply predictive analytics to solve real-world business problems and drive proactive decisions.

Course Content

Module 1. Introduction to Predictive Analytics and Forecasting

• Defining Predictive Analytics: Beyond descriptive and diagnostic analytics
• Forecasting vs. Prediction: Understanding the nuances
• The value proposition of predictive analytics in various industries
• Common business use cases for predictive analytics (e.g., sales, churn, demand)
• Overview of the predictive analytics lifecycle

Module 2. Data Preparation for Predictive Modeling

• Data Cleaning and Preprocessing: Handling missing values, outliers, inconsistencies
• Feature Engineering: Creating new variables from existing data for better model performance
• Data Scaling and Normalization
• Encoding Categorical Variables
• Time-series specific data preparation (e.g., lag features, rolling averages)

Module 3. Statistical Foundations for Forecasting

• Basic Statistical Concepts: Mean, median, variance, standard deviation
• Correlation and Causation: Understanding relationships in data
• Introduction to Probability Distributions relevant to forecasting
• Hypothesis Testing (conceptual overview)
• Sampling techniques for large datasets

Module 4. Introduction to Time Series Forecasting

• Understanding Time Series Data: Components (trend, seasonality, cycle, randomness)
• Time Series Decomposition: Identifying underlying patterns
• Basic Forecasting Methods: Naive, Simple Average, Moving Average
• Exponential Smoothing Methods: Simple, Holt, Holt-Winters
• Evaluating Forecast Accuracy: MAE, RMSE, MAPE

Module 5. Regression Analysis for Prediction

• Simple Linear Regression: Predicting a continuous outcome from one predictor
• Multiple Linear Regression: Predicting from multiple predictors
• Understanding Regression Coefficients, R-squared, p-values
• Assumptions of Linear Regression and how to check them
• Building and interpreting regression models in BI tools (if applicable)

Module 6. Classification Models for Prediction

• Introduction to Classification: Predicting categorical outcomes (e.g., Yes/No, A/B/C)
• Logistic Regression: A common classification algorithm
• Decision Trees: Understanding tree-based models for classification
• Evaluating Classification Models: Confusion Matrix, Accuracy, Precision, Recall, F1-Score
• Use cases: Customer churn prediction, loan default prediction

Module 7. Clustering and Segmentation for Insight

• Introduction to Clustering: Grouping similar data points
• K-Means Clustering: Algorithm and application
• Use cases: Customer segmentation, market basket analysis
• Interpreting cluster results and deriving business insights
• Visualizing clusters in BI tools

Module 8. Predictive Features in Microsoft Power BI

• Built-in Forecasting in Power BI: Line charts with forecast
• Anomaly Detection in Power BI: Identifying unusual data points
• Q&A for Predictive Insights: Asking natural language questions
• Using Python/R scripts directly in Power BI for custom models
• Smart Narratives for automated text summaries of predictions

Module 9. Predictive Features in Tableau

• Trend Lines and Forecasts in Tableau: Automatic forecasting models
• Reference Lines and Bands: Visualizing targets and confidence intervals
• Cluster Analysis in Tableau: Identifying segments visually
• Using R/Python integration for advanced predictive models
• Building interactive dashboards with predictive elements

Module 10. Interpreting and Communicating Predictions

• Understanding Model Output: Coefficients, probabilities, confidence intervals
• Communicating Uncertainty: Presenting predictions with ranges
• Storytelling with Predictions: Crafting a narrative around future scenarios
• Visualizing predictions vs. actuals for performance monitoring
• Tailoring predictive insights to different business stakeholders

Module 11. Model Evaluation and Validation

• Splitting Data: Training, Validation, and Test sets
• Cross-Validation Techniques: K-fold cross-validation
• Overfitting and Underfitting: Identifying and mitigating
• Model Selection: Choosing the best model for the problem
• Continuous monitoring of model performance in production

Module 12. Deploying and Integrating Predictive Models

• Deployment Strategies: Batch vs. Real-time predictions
• Integrating predictive insights into operational systems
• Embedding predictive models into BI dashboards for self-service
• API exposure for model predictions (conceptual)
• Monitoring model drift and retraining strategies

Module 13. Anomaly Detection and Outlier Management

• Defining Anomalies: Contextual, collective, point anomalies
• Techniques for Anomaly Detection: Statistical, density-based, machine learning
• Visualizing anomalies in time series and other data
• Use cases: Fraud detection, system monitoring, quality control
• Alerting and actioning on detected anomalies

Module 14. Ethical Considerations and Governance of Predictive Analytics

• Bias in Data and Algorithms: Identifying and mitigating
• Fairness and Transparency in Predictive Models
• Data Privacy and Security for predictive analytics
• Responsible AI principles for predictive deployments
• Establishing governance frameworks for predictive models

Module 15. Real-World Applications and Future Trends

• Case Studies: Predictive analytics in sales, marketing, finance, operations, HR
• Prescriptive Analytics: Recommending optimal actions based on predictions
• Real-time Predictive Analytics and Streaming ML
• Automated Machine Learning (AutoML) in BI tools
• The evolving role of the citizen data scientist in predictive analytics.

Training Approach

This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.

Tailor-Made Course

This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Training Venue

The training will be held at our Skills for Africa Training Institute Training Centre. We also offer training for a group at requested location all over the world. The course fee covers the course tuition, training materials, two break refreshments, and buffet lunch.

Visa application, travel expenses, airport transfers, dinners, accommodation, insurance, and other personal expenses are catered by the participant

Certification

Participants will be issued with Skills for Africa Training Institute certificate upon completion of this course.

Airport Pickup and Accommodation

Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: info@skillsforafrica.org, training@skillsforafrica.org Tel: +254 702 249 449

Terms of Payment: Unless otherwise agreed between the two parties’ payment of the course fee should be done 7 working days before commencement of the training.