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Who should attend
- IT professionals who need to apply predictive analytics for improving business processes and decision making
- Data/business analysts who are interested in obtaining knowledge in business analytics to add more value and insights to their recommendations
- Domain specialists and anyone planning to undertake business analytics projects
- Participants should have basic knowledge in business, marketing and be familiar in using computer/statistical software
Knowledge or familiarity with basic statistics or analytics techniques will be useful
About the course
There has been an increasing demand for business analytics, especially in the recent years and this trend is set to experience a continual rise. Predictive analytics is one of the important areas of business analytics. It is all about extracting information from data and using it to predict future trends and behaviour patterns in businesses. In other words, predictive analytics offers actionable business predictions through mining abundant historical data. It has been widely used in many industries such as insurance, telecom, retail, travel, healthcare and has shown positive impact on business decision making. Many companies have been turning to predictive analytics to thrive and compete against their competitors.
This course will directly help participants utilise business data more effectively by deriving insights of trends and irregularities from data and applying them for forward-looking predictions. This is realised through building predictive models with appropriate analytical techniques. Ultimately, the company will gain a competitive advantage over its competitors as it would become more proactive in the way it does business and marketing and thereby reduce costs and increase return on investments.
The objective of this course is to introduce participants to the concepts, methods and techniques of predictive analytics. Participants will gain the requisite skills to perform predictive analytics in real-life business scenarios through workshops using either R or SPSS or JMP. The course will assume that the participants have some preliminary knowledge of statistical concepts like regression and logistic regression and some hands on experience of modelling using these techniques, though the course will revisit these concepts to refresh the theory. In case participants have no prior statistical knowledge, it is strongly recommended that they attend the Statistics Bootcamp using R and Tableau prior to attending this course.
This course is part of the Analytics & Intelligent Systems Series offered by NUS-ISS.
At the end of the course, participants will be able to:
- Identify where predictive analytics can be applied and the benefits which can be derived
- Evaluate the predictive model's objectives and data available
- Design the predictive analytics process
- Assess and select the appropriate testing methods to validate the predictive models
- Analyse the results and communicate the decision to end users
Lectures and workshops
What Will Be Covered
- Introduction to predictive analytics
- How to make predictions using multiple regression models
- Times series modelling and applications
- Introduction to logistic regression modelling
- Predictive modelling using decision trees
- Predictive modelling using neutral networks
- Practical case studies in workshops conducted in R/excel / SPSS / JMP
Dr. Zhu Fangming is with the Institute of Systems Science of the National University of Singapore (NUS-ISS). He currently lectures in the Master of Technology programme in the areas of evolutionary computation, neural networks and data mining. Prior to joining ISS, he was a postdoctoral fellow i...
Rita as a seasoned analytics professional has 25+ years of experience in Financial Services, Insurance and Market Research specializing in Scoring (operational & regulatory), Risk Management, Marketing Analytics, Analytics Strategy Development, Analytics Infrastructure, Analytics team managem...
Tian Jing currently lectures in the Analytics and Intelligent Systems Practice in the areas of artificial intelligence, data analytics, and machine learning. He received his Ph.D. degree from School of Electrical and Electronic Engineering, Nanyang Technological University, Singapore. Prior to j...
Eric has about 15 years of analytics and data science experience in the financial services, start-ups and web analytics. Prior joining ISS, he was an Enterprise Data Scientist with Thomson Reuters and was also director of quantitative data science in a China fintech start-up with 4 million users....
Prakash is an Associate Lecturer in Institute of Systems Science, National University of Singapore (NUS-ISS). He taught for various analytics modules at School of Information Systems, SMU Singapore for nearly three years prior to joining NUS-ISS. He holds a MS degree in IT in Business Analytics ...
16 years of experience in marketing insights and analytics across Asia, with special focus on consumer, retailing, brand research and policy development and evaluation. Expertise to integrate data from multiple sources. Extensively worked on segmentation, brand positioning, pricing decision, ROI,...
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