Course Information

Course Description

The Diploma in Data Science with a focus on Advanced Predictive Modelling aims to equip learners with the necessary knowledge, skills, and understanding of advanced techniques in predictive analytics. This course delves into various aspects of predictive modelling, including statistical methods, machine learning algorithms, data preprocessing, model evaluation, and deployment strategies.

COURSE OUTLINE

  • Introduction to Predictive Modelling
  • Statistical Methods for Predictive Analytics
  • Machine Learning Algorithms for Prediction
  • Data Preprocessing Techniques
  • Model Evaluation and Performance Metrics
  • Deployment Strategies for Predictive Models

 

STUDENT ACQUISITIONS

Upon completing the course, students will:

  • Understand the fundamental concepts and principles of predictive modelling and its applications in data science.
  • Develop proficiency in applying statistical methods and machine learning algorithms for predictive analytics tasks.
  • Evaluate predictive models using appropriate performance metrics and interpret their results effectively.
  • Gain practical experience in preprocessing data and preparing it for predictive modelling tasks.
  • Explore various strategies for deploying predictive models in real-world scenarios.
  • Stay updated with emerging trends and advancements in predictive modelling and data science.

 

LEARNING METHODOLOGIES

The course employs a range of effective learning methodologies, including:

  • Engaging with theoretical concepts to build foundational knowledge of predictive modelling and data science.
  • Encouraging independent exploration and analysis of advanced techniques in predictive analytics.
  • Facilitating hands-on experience through practical exercises and predictive
  • modelling projects using industry-standard tools and software.
  • Promoting critical evaluation of research papers, case studies, and real-world datasets related to predictive modelling.
  • Fostering problem-solving skills through simulation exercises and real-world predictive modelling challenges.
  • Enhancing communication abilities for clear and effective presentation of analytical findings and model outcomes.