Date: 30 Jan, 1 Feb, 3 Feb 2023 | 9am-5.30pm | Classroom Learning
Duration: 3 Days
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 module is part of Professional Certificate in Business Analytics.
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
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.
- 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.
At least a polytechnic diploma. Participants are recommended to have knowledge of the fundamentals covered in Descriptive Analytics.
Participants should have a windows system that supports R packages and libraries for the practice labs.
(Click their photos to view their short biographies)
Ms Samantha Sow
Ms. Samantha Sow is currently a Senior Lecturer in the department of Information Systems and Analytics at the National University of Singapore (NUS). She has over 8+ years of experience in Business Analytics and Data Science and she conducts training for both government agencies and corporate clients. Before joining NUS, she lectures at Temasek Polytechnic, teaching Business Analytics and Data Science to professionals, managers and executives (PMEs). Prior to being an academia, Samantha worked in the research and development at Infineon Technologies. Her research and project areas include Business Analytics, Data Mining, Predictive and Prescriptive Analytics.
She has a passion for engaging and inspiring participants to enhance their workplace analytics capabilities and increase business intelligence quotient within their organisations. Her interests lie in the applications of data analytics, predictive modelling and optimization techniques to derive actionable insights for commercial effectiveness. She is familiar with typical analytics tools such as Python, R, and SAS, SPSS and Tableau. She also has working knowledge in the area of Analytics, Data Science and Machine Learning.
Samantha completed her Master of Education from University of Sheffield and graduated from the National University of Singapore with a Bachelor’s in Engineering, First Class Honours. She has also completed THEC (Teaching in Higher Education) and ACTA (Advanced Certificate in Training and Assessment). She is a member of the adult associate educator (AEN) by Institute for Adult Learning (IAL), Singapore.
Her certifications include:
Microsoft Certified Azure Data Scientist Associate. Microsoft Certified Data Analyst Associate. SAS Certified Predictive Modeller in SAS Enterprise Miner. Tableau Certified Desktop Specialist.
Total Nett Programme Fee Payable, Including GST, after additional funding from the various funding schemes
Participants must fulfill at least 75% attendance and pass all assessment components to be eligible for SSG funding.
To enquire, email email@example.com
To register, click Register
TGS-2019507053 (Classroom Learning)
For members of public and NUS Alumnus (without R&G Voucher), please follow the steps below:
Select Short Course / Modular Course -> Apply for Myself -> Browse Academic Modules / Short Courses-> Module/Course Category -> Short Courses -> Browse Courses-> Advanced Computing for Exe (Faculty/Department / Unit)
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Course Fee Breakdown
Singapore Citizens39 years old or younger
Singapore Citizen40 years old or older