Data Analytics for Managers

Data analytics have been touted as the most important technology which will bring organizations to their next frontier.

Many success stories have been shared on how data analytics can help organizations make better decisions, including understanding their customers better, predicting outcomes, understanding public sentiments on the social media, and optimizing resources to achieve the best results.


Managers who are leading their organizations have to play the lead role in shaping the direction and planning the strategies on who, what, when, where and how should data analytics be applied in the different parts of the organization.


The proposed training is designed for managers covering several topics in the Data Analytics area. The training will be conducted by the senior faculty members from the School of Information Systems, who are experts in their respective areas.


The topics covered will include:



Topics and concepts

Sample Data and Problem Sets

Software Tools


Quick overview of Data Analytics

  • Demystifying Data Analytics
  • Data Analytics Methods: Statistical Learning, Data Mining & machine Learning
  • Big Data (mention only, point to relevant references)




Solution process flow to solve data analytics problems

  • Data Analytics Lifecycle
  • Data Analytics Technologies: Commercial off-the-shelf (COTS) vs open source




Data Manipulation and Visualization

  • Data exploration
  • Data cleaning and manipulation
  • Data merging, joining
  • Data visualization
  • Geospace
  • MyTransport.SG Data Mall
  • Tableau 10


Data Analytics Techniques

  • Supervised learning (1 hands-on + mention a few)
  • Unsupervised learning (1 hands-on + mention a few)
  • Pros and cons of each technique
  • Select the best technique
  • Train, test and validate model
  • Results assessment and interpretation
  • IEEE VAST Challenge 
  • Rapid Miner


Social Network Analysis

  • Extract information and interpretation
  • Terrorist data from IEEE VAST Challenge 
  • Gephi


Text Mining

  • Text pre-processing
  • Text classification
  • Text clustering (including topic modelling)
  • Sentiment analysis
  • Forums, blogs, public Facebook, or AirBnB
  • Rapid Miner


Operations – Optimization

  • Optimization theory
  • LP, IP, MIP, BIP
  • Model formulation
  • Solver definition
  • Heuristic algorithm
  • Police patrol problem
  • Nurse scheduling problem
  • MS Excel 2013 with Solver Add-in installed





2 days


29-30 January 2018 (Fully Subscribed)

9-10 April 2018 (Fully Subscribed)


An assessment will be conducted at the end of the course.


SkillsFuture Series

Course fee grant at 70% of course fees (excluding GST) for participants who are successfully enrolled by SMU into approved courses under the Programme. Participants must be Singapore Citizens or Singapore Permanent Residents.

SkillsFuture Mid-Career Enhanced Subsidy ("MCES")
Up to 90% of course fees for Singapore Citizens aged 40 years and above

Enhanced Training Support for SMEs ("ETSS")
Up to 90% of course fees; and 80% of basic hourly salary capped at $7.50/hr for local employees of SMEs

SkillsFuture Credit
Singapore Citizens aged 25 and above, and self-funding the course may use their SkillsFuture Credit (up to S$500) to defray part of the course fee. Please click User Guide on how to submit your claim. SkillsFuture Credit claims may be submitted by logging in via MySkillsFuture.



Full Fee: S$1,712 per participant (including GST)


Nett fee payable after SkillsFuture Singapore (SSG) Funding for Individuals:

  • S$513.60 (including GST) for Singapore Citizens and Singapore Permanent Residents
  • S$193.60 (including GST) for Singapore Citizens aged 40 years and above



The cost of training will include the following:

  • 8-hours/day 

  • Training materials in soft copy

  • List of software to be installed in the laptop PCs for the training


Instructors for Workshop:


Dr. Michelle Cheong

Associate Professor of Information Systems

Associate Dean, SIS Post-Graduate Professional Education

Singapore Management University


Prior to joining SMU, Dr. Michelle Cheong had 8 years of industry experience leading teams to develop complex IT systems which were implemented enterprise-wide covering business functions from sales to engineering, inventory management, planning, production, and distribution. She joined SMU in 2005 where she teaches the Business Modeling with Spreadsheets course at the undergraduate and master levels. She is the co-author of a book on the same topic. Michelle was instrumental in designing and launching the Master of IT in Business (Analytics) programme in January 2011, which is the first Analytics master’s programme in Asia. The programme remains the leading one of its kind today. She also designed and teaches the Operations Analytics course in the MITB (Analytics) programme, and several executive courses in Data Analytics for other programmes including Master of Science in Communication Management, IE-SMU MBA, and Master of Science in Innovation.


Dr. Jiang Jing

Associate Professor of Information Systems

Singapore Management University


Dr. Jiang Jing is an Associate Professor of Information Systems at the Singapore Management University. Her general research areas are in text mining and natural language processing. Her recent work focuses on topic modeling, information extraction and social media text mining. She currently serves on the editorial board of Computational Linguistics and Information Processing & Management. Her work has been regularly published in top journals of natural language processing and data mining.