Applied Analytics Using Predictive Modelling

Dates: 25 Jan, 26 Jan, 29 Jan 2024 | 9am-5.30pm | Classroom Learning 

Duration: 3 Days

Course Overview

Modern predictive analytics uses data to model a specific domain, isolate key factors and use the models or algorithms built using this process to predict likely future outcomes from new data. Predictive analytics is not a new concept and businesses have been using decision trees and regression to help correlate and classify their data and make predictions.

Mathematical modeling tools are applied to data in order to generate predictions about an unknown fact, characteristic, or even event not yet available in the dataset. To put it simply, we use statistical techniques to derive sophisticated predictive models and algorithms from large data sets without requiring explicit programming.

This course covers the life cycle of the data mining project and the key concepts of various predictive analytics techniques. It focuses on performing predictive analytics using supervised and unsupervised machine learning models on an open-source programming platform and how to communicate the results to audiences.

This course is part of Professional Certificate in Business Analytics.

Course Objectives

Course will enable participants to:

• Understand how to embark on a data mining project
• Understand the fundamental key concepts of various predictive analytics techniques
• Acquire basic proficiency on popular data analytics tools such as R
• Understand how to prepare data for doing various predictive analytics techniques
• Apply predictive analytics using popular tools
• Identify which predictive analytics approaches to use for different situations

Learning Outcomes

This course will introduce the foundational concepts behind the various predictive analytics models and techniques. It covers data wrangling, visualisation, supervised and unsupervised predictive models.

Participants will gain the skills to perform predictive analytics on an open-source programming platform, using R packages and libraries. At the end of the course, you will be able to understand the data mining process cycle, build, assess and evaluate the predictive analytics models and techniques, and eventually communicate the findings to business audiences.

Topics

• Understand and design the data mining lifecycle process
• Data wrangling using dplyr
• Data visualization using ggplot
• Assess and perform predictive analytics using supervised and unsupervised machine learning models on an open-source programming platform
• Assess and evaluate metrics to determine the optimal predictive model
• Analyse the results and communicate the decision to facilitate deployment

Who Should Attend

Any working professional who is interested to learn concepts, methodologies, and techniques in predictive analytics to improve business processes, and/or keen to use R open-source programming platform for predictive modeling.

Prerequisites

At least a polytechnic diploma. Participants are recommended to have knowledge of the fundamentals covered in Descriptive Analytics.

Technical Requirements

Participants should have a windows system that supports R packages and libraries for the practice labs.

Course Convener

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