Predictive Analytics

The Predictive Analytics Course provides learners with the essential skills to transform historical and current data into accurate forecasts and actionable insights for business decision-making. Predictive analytics is one of the most powerful applications of data science, enabling organisations to anticipate trends, understand customer behaviour, manage risks, and optimise operations. Delivered in a self-paced format with a recommended duration of 20 hours, this course balances theoretical foundations with practical applications, preparing learners to design, deploy, and manage predictive models in real-world business contexts.

Course Structure and Content

The course is organised into ten modules that progressively build expertise in predictive modelling techniques and business applications:

1. Introduction to Predictive Analytics Gain an overview of predictive analytics, its role in data-driven organisations, and common business use cases across industries.
2. Data Preparation for Predictive Analytics Learn techniques for cleaning, transforming, and preparing data to ensure quality, accuracy, and reliability in predictive models.
3. Regression Analysis for Predictive Modelling Explore linear and multiple regression techniques to identify patterns, forecast outcomes, and establish relationships between variables.
4. Classification Techniques for Predictive Analytics Study classification methods such as logistic regression, decision trees, and random forests for applications like fraud detection and customer churn prediction.
5. Time Series Forecasting Techniques Understand forecasting methods such as ARIMA, exponential smoothing, and advanced algorithms for predicting sales, demand, and market trends.
6. Big Data and Predictive Analytics Examine how Big Data frameworks and cloud platforms enable predictive analytics at scale, supporting large and complex datasets.
7. Automation and Deployment of Predictive Models Explore techniques for automating model workflows and deploying predictive models into business environments for continuous monitoring.
8. Ethical Considerations and Future Trends in Predictive Analytics Evaluate the ethical implications of predictive modelling, including fairness, bias, and transparency, and explore emerging innovations shaping the future of predictive analytics.

Learning Outcomes

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

• Prepare data, build, and evaluate predictive models using advanced statistical and machine learning techniques.
• Apply time series forecasting methods to predict future outcomes in various business contexts.
• Leverage Big Data frameworks and technologies for scalable and efficient predictive analytics.
• Design, deploy, and monitor predictive models to support decision-making in dynamic business environments.
• Critically analyse the ethical implications of predictive analytics and apply best practices to ensure fairness and compliance.

Learning Materials

The course includes a wide range of study resources to enhance engagement and practical skills:

• Recorded video lectures explaining key methods and applications.
• Visual slides to support conceptual clarity and reference.
• Case studies demonstrating real-world applications of predictive analytics.
• Practical exercises to develop and test predictive models in simulated business scenarios.

Assessment

Learners will complete an online quiz at the end of the course, comprising 20–30 multiple-choice questions (MCQs) to assess knowledge and practical understanding. Learners are provided with unlimited attempts, ensuring they can reinforce their knowledge and achieve mastery.

Mode of Study

The course is delivered entirely online in a flexible, self-paced format. Learners can access and complete the content at their own pace, making it ideal for both working professionals and students aiming to build expertise in predictive analytics.

Sample Lecture: