Comprehensive course analysis
Who should attend
This course is designed for the non-technical business professional who aspires to gain practice skills and practical knowledge through hands-on experience in using business analytics to solve real life business problems:
- Anybody who is just starting to analyse data and need a kick start
- Anybody who wants to learn business analytics from the ground up
- Anybody who is exploring career opportunities in business analytics
- Anybody who is interested to learn how to make data-driven decisions
About the course
This is an introductory course to help the non-technical users perform a variety of data analysis in order to derive factual insights and ultimately to gain experience in making evidence-based business decisions.
Over the course of three days, you will explore multiple statistical concepts and learn how to apply them to business use cases. You will work on cleaning and preparing the data for analysis and generating insights. You will experience the use of data visualisation to find answers to business questions. You will learn how to discover correlations in data and to build simple linear regression models. Finally, you will learn about supervised and unsupervised machine learning techniques. Here you will practice building classification trees to predict customer behavior or condition; and using cluster analysis methods to create customer groupings that would lead to more effective customer management and engagement.
All the topics taught will be delivered through a problem-based approach and active discussions that are driven by use of numerous business scenarios. Emphasis is placed on critical thinking, practice skills and practical knowledge with the use of software and tools only as delivery mechanisms.
At the end of the course, participants will learn:
- How statistics is being applied to solve a variety of business problems
- How to compute and interpret data summaries- numerically as well as visually
- How visual data exploration is used to find answers to business problems
- How to present data visually for better understanding of relationships between variables
- How to statistically analyze whether there is a relationship between two variables and determine the size of the relationship
- How to predict one variable in terms of another to make improvement in business performance
- How to perform classification and prediction using decision tree modeling approach
- How to cluster data into homogenous subsets to enable focused group-based customer management
What Will Be Covered
Day 1: How can we use statistics to solve business problems?
1.1: Introduction to Statistics 1.2: How to describe data? 1.3: How to do data preparation before analysis?
Day 2: How can we predict what is likely to happen?
2.1: Making sense of data visually 2.2: How to determine if one variable is related to another? 2.3: How to predict one variable in terms of another?
Day 3: How can we group business products/customers?
3.1: How to group/segment your customers using DT (Supervised learning)? 3.2: How to group/segment customers using clustering (Unsupervised learning)?
Charles lectures and consults on artificial intelligence, knowledge engineering and knowledge management. He has been a principal investigator, project manager, supervisor and developer for many knowledge based systems project in Canada and Singapore. Previously, he was a research scientist with ...
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,...
Brian is currently a Senior Lecturer and Consultant with the NUS ISS. Prior to joining NUS ISS, he was an Assistant Director with the Infocomm Media Development Authority (IMDA). In IMDA, he led a team of scientists, engineers and project managers in the area of artificial intelligence and data a...
Christine is currently a Principal Lecturer and Consultant with the NUS ISS. She has worked in both public and private sectors (including tourism, fast moving consumer goods, transport & logistics sectors, education etc) where she led commercial excellence and analytics as well as market res...
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Because of COVID-19, many providers are cancelling or postponing in-person programs or providing online participation options.
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