Course Details

This course is appropriate for a wide range of professionals but not limited to:

• Data Analysts
• Data Scientists
• Business Intelligence Analysts
• Financial Analysts
• Operations Managers
• Forecasting Specialists

• Expert-led sessions with dynamic visual aids
• Comprehensive course manual to support practical application and reinforcement
• Interactive discussions addressing participants’ real-world projects and challenges
• Insightful case studies and proven best practices to enhance learning

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

• Understand the principles and business applications of predictive analytics.
• Prepare and transform time series data for modelling and forecasting.
• Apply forecasting techniques including smoothing, regression, and ARIMA models.
• Evaluate model accuracy using appropriate performance metrics.
• Identify trends, seasonality, and anomalies within time-based datasets.
• Develop predictive models that support strategic and operational decisions.

DAY 1

Foundations of Predictive Analytics

• Introduction to predictive analytics and forecasting
• Business value of predictive modelling
• Data collection and preparation techniques
• Understanding structured and time-based datasets
• Exploratory data analysis and visualization
• Forecasting use cases across industries
   

DAY 2

Time Series Fundamentals

• Components of time series data
• Trend, seasonality, cyclical patterns, and noise
• Time series decomposition methods
• Stationarity concepts and testing
• Data transformations and differencing
• Practical exercises on time series preparation

DAY 3

Forecasting Models and Techniques

• Moving averages and smoothing methods
• Exponential smoothing models
• Regression-based forecasting
• AR, MA, ARMA, and ARIMA models
• Model parameter selection
• Forecast generation and interpretation

DAY 4

Advanced Predictive Modelling

• Seasonal forecasting with SARIMA
• Multivariate forecasting approaches
• Machine learning techniques for prediction
• Feature engineering for predictive analytics
• Handling missing values and outliers
• Forecast optimization strategies

DAY 5

Model Evaluation and Business Applications

• Forecast accuracy measurement
• Error metrics and model validation
• Scenario analysis and sensitivity testing
• Communicating predictive insights to stakeholders
• End-to-end forecasting project
• Best practices for enterprise implementation