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Introduction

This course is designed to expose the participants with the concept of machine learning models for predictive analytics (theory + case study). This course includes hand on approach using real-world datasets,  to test different models and visualize the outputs with different machine learning models which can later be directly applied by the participants to the job.  

Objectives

Upon completion of this course, participants will be able:

• Explore the predictive analytic which is useful for managerial decision making
• Develop a constructive approach to solve business queries with R
• Understand the implementation of Big Data Analytics that can help in improving management decision-support effectiveness

   

Trainers

Dr. Mohd Hilmi Hasan, is a senior lecturer at Universiti Teknologi PETRONAS (UTP), Malaysia. He obtained his Ph.D in Information Technology from UTP in 2017 with a research in the area of artificial intelligence, specifically Interval Type-2 fuzzy. He also graduated with a Master of Information Technology (eScience) from The Australian National University in 2004 and a Bachelor of Technology (Hons.) Information Technology from UTP in 2002. He has been teaching and being an active researcher at UTP since 2004. His research interest covers areas of artificial intelligence and data analytics. He has been appointed as a reviewer for Artificial Intelligence Review Journal (Q2 Journal) and many other international conferences. Dr. Izzatdin, is a researcher at the High Performance Cloud Computing Centre (HPC3) in the Universiti Teknologi PETRONAS (UTP), where he focuses in solving complex upstream Oil and Gas (O&G) industry problems from the view point of computer sciences. Dr. Izzatdin currently serves as the deputy head of the Computer and Information Sciences Department in UTP.  He obtained his Ph.D in Information Technology from Deakin University, Australia working in the domain of hydrocarbon exploration and cloud computing. He is working closely with O&G companies in delivering solutions for complex problems such as Offshore O&G pipeline corrosion rate prediction, O&G pipeline corrosion detection, securing data on clouds and designing and implementing Metocean prediction system and bridging upstream and downstream oil and gas businesses through data analytics. Additionally, he is also working on Big Data transmission, security and optimization problems on High Performance Clouds. Dr. Said Jadid, is currently a Lecturer at the Department of Computer and Information Sciences, Universiti Teknologi PETRONAS (UTP). He holds a Degree in Computer Science from Moi University (2009), Master’s Degree in Computer Science from Universiti Teknologi Malaysia (2012) and a PhD in Information Technology from Universiti Teknologi PETRONAS (2016). His research interests are in the areas of Machine Learning (Supervised Learning) and Data Analytics (Predictive and Streaming Analytics). He has been appointed as a reviewer for Artificial Intelligence Review (Q2 Journal) and a technical committee for various international conferences.  

Course Contents

This two-day course will cover the following aspects (case studies may be included in some topics):

Topic	Sub-topic
Module 1: Introduction to Data Science and Predictive Analytics	1. Fundamental of Data     Science 2. Introduction to       Predictive Modelling Using R Programming
Module 2: Machine Learning Models for Predictive Analytics	1. Definition, Methods, and Algorithms 2. Predictive Modelling  Techniques
Module 3: Predictive Analytics using R Programming	1. Introduction to R 2. Data Manipulation in R 3. Create and Customize Visualizations using ggplot2 4. Perform Predictive      Analytics using R

 

Download the Course Brochure:

Big Data Predictive Analytic Using R (Intermediate Level)

June Session

July Session

October Session

REGISTRATION IS NOW OPEN

To register, download the CAPE Professional Short Course Registration Form and email the completed form to cape@utp.edu.my.

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