Data Science and Decision Making

Part of the ModularMaster in Technology and Management programme

This course focuses in the creation of value through application of data science and analytics toolbox in the business decision making. The focus of the course is in building a capability to bridge the gap between the business domain understanding and data science expertise, this is a fundamental task for any contemporary business.

The course provides an overview of the data science toolbox including topics such as causality, predictive analytics with machine learning, simulation and optimisation. We study and practise the application of the data science tools in business decisions over many application areas.

We approach the theme from two perspectives:

how data science can be applied to business problems to create value,
what are the implications of increasing analytics applications to the businesses

Who Should Attend

The course is for business leaders, senior executives and entrepreneurs responsible for leveraging the creation of value through application of data science and analytics toolbox for their business decision making.

Programme Outline

Learning Objectives and Structure

By the end of this module, participants would be able to:

Develop skillsets for formulating and driving digital strategy and digital transformation
Understand how change driven by digital transformation may affect your value chain and value generation
Draw the “big picture” on different technological innovation drivers and business model disruptors
Learn the key elements for creating and capturing value through digital strategies, data monetisation, and digital platforms business

Programme Structure: Participants will go through 4 days of training. Class will reconvene on the 5th day for a presentation as part of the course assessment.

Day 1

Data strategy
Data as an economic good
Big data good
Data sharing
Data governance and data quality management
Data and analytics culture
Field definitions of analytics and machine learning
The three types of learning
Typical case scenarios in analytics
Models, accuracy, and generalisation
Case examples
Course summary

Day 2

Data Science Toolbox and applications: Descriptive and diagnostics
Group work: Application of diagnostic analytics in a case
Data Science Toolbox and applications: Predictive analytics pt 1.
Data Science Toolbox and applications: Predictive analytics pt 2: Model selection and value of prediction
Group work: Predictive analytics minicase
Data Science Toolbox and applications: Prescriptive analytics
Group discussion: From predictive to prescriptive analytics: identification of opportunities at your company
Effective decision making with data science
Group work: Analytics canvas application for your company case
Summary: Creating value with data science for business decisions

Day 3

Overview of the data science process
Applications of data science and decision making
Exercise: Discussion of case studies
Characteristics and types of data
Collecting, storing and representing data
Data pre-processing and wrangling
Exercise: Data collection and handling
Big data and analytics
Understanding trends from data
Identifying outliers
Exercise: Hands-on lab

Day 4

Best practices in data visualisation
Types of graphs and charts
Tools for visualisation
Exercise: Visualising and presenting results
Supervised learning: Classification and regression
Unsupervised learning: Clustering
Experimentation and evaluation process
Exercise: Hands-on lab on classification
Data science in different businesses
Social, ethical and legal considerations
Limitations of data science
Exercise: Application of data science for decision making

Day 5

Project Presentation

Assessment

Multiple methods of assessment are used to provide an opportunity for the participant to demonstrate their learning results with a variety of learning styles.

These include:

Pre-assignment due before in-class session
Class contribution
In-class assignments (group work and presentations during the module)
Take-home assignment due after in-class session

Subject Credits

Upon completion and satisfying the requirements of passing this course, learners will be awarded 12 subject credits.

Course Fees and Funding

Full course fee inclusive of prevailing GST	You pay S$6,758.00
SkillsFuture Course Fee subsidy (70%) For Singapore Citizens < 40 years old For Permanent Residents	You pay S$2,027.40
Mid-Career Enhanced Subsidy (90%) For Singapore Citizens ≥ 40 years old	You pay S$787.40
Enhanced Training Support for SMEs (90%) For SME - Sponsored employees	You pay S$787.40

The above module fee payable is inclusive of 9% GST.
This course is not eligible for the $4,000 credit under SkillsFuture Credit (Mid-Career) top-up scheme.

Instructors

Kwan Hui Lim
Fellow, SUTD Academy
Assistant Professor at SUTD

Kwan Hui LIM is an Assistant Professor at the Information Systems Technology and Design Pillar, Singapore University of Technology and Design, and leads the Social and Urban Analytics Lab. Previously, he was a Research Fellow at the School of Computing and Information Systems, University of Melbourne, Research Engineer at the Living Analytics Research Centre, Singapore Management University, Research Intern at IBM Research – Australia, and various visiting appointments. He received his PhD from the University of Melbourne, and MSc (Research) and BCompSci (1st Class Honours) from the University of Western Australia.

He is a recipient of the 2016 Google PhD Fellowship in Machine Learning. His research interests are in Data Mining, Machine Learning, Artificial Intelligence, Social Network Analysis, and Social Computing.

Lauri Saarinen
Assistant Professor
Aalto University School of Science

Lauri Saarinen serves as Assistant Professor of Operations Management, Aalto University School of Science, Finland. He is an expert in data analytics-based operations management, process improvement and planning of operations. He focuses in research and teaching on value creating analytical applications to companies’ operational data and has specialized on questions related to value of local production, international supply chains and process improvement. Lauri engages actively with industry with a goal to understand the edge of developing operations management and process improvement in practice. Before joining the academia, Lauri worked in supply chain management, inventory and production planning optimization. He has continued this work and is currently involved in developing tools to make analytics and optimization more accessible for companies to use in their decision making.

Policies and Financing Options

SSG Funding Terms and Conditions

Use of Personal Details

In consideration of the subsidy provided by SkillsFuture Singapore Agency (“SSG”) through the SUTD Academy for the Course,

I consent to:

The collection, use and disclosure to relevant third parties of my personal data by the SUTD Academy including but not limited to personal particulars, attendance records, assessment/performance records, for the following purposes:

Reporting of national statistics and conducting of holistic continuing education training research and analysis;
Facilitate the conduct of the relevant surveys and audits in relation to the Course;
General administration of the Course including but not limited to processing of the subsidy provided by SSG;
Publicity and marketing of the Course or other Courses to be provided by SSG or SUTD Academy; and
SSG or its Appointed Auditors or Nominated Representatives to directly contact Course Participant to obtain information deemed necessary for the purposes of conducting effectiveness survey or audits in relation to the Course.

I agree to:

Attend and complete all lectures, class exercises, workshops and assessments;
Complete the Course feedback at the end of the Course;
Complete the post Course survey sent about 3 to 6 months after class attendance; and
Sign up for a personal email account.

SUTD Privacy Statement

For more information on SUTD's privacy statement, please visit https://sutd.edu.sg/Privacy-Statement.

SUTD Terms and Conditions

Methods of Payment

Learn more about the available payment modes.

Cancellation & Refund Policy

If a written notification is sent to sutd_academy@sutd.edu.sg within 24 hours after course registration deadline there will be no cancellation charges. A full refund will be made.
No refund is provided if written notification is more than 24 hours after course registration deadline. SUTD Academy reserves the rights to collect the full fee amount from the participant.

Replacement Policy

Companies may replace participants who have signed up for the course by giving a 3-working day notice before the course commencement date to sutd_academy@sutd.edu.sg. Terms and conditions apply.

Registration Policy

Course may be cancelled due to insufficient participants. SUTD Academy will not be responsible or liable in any way for any claims, damages, losses, expenses, costs or liabilities whatsoever (including, without limitation, any direct or indirect damages for loss of profits, business interruption or loss of information) resulting or arising directly or indirectly from any course cancellation.
Course enrolment is based on a first-come, first-served basis.
SUTD Academy reserves the right to change or cancel any course or instructor due to unforeseen circumstances.

Types of Funding

Funding under Mid-Career Enhanced Subsidy ("MCES")

MCES is an enhanced Subsidy to encourage mid-career individuals to upskill and reskill, thereby helping them to remain competitive and resilient in the job market. With this, all Singaporeans aged 40 and above will receive higher subsidies of up to 90% course fee subsidy for SSG-funded certifiable courses.
Individuals/employers are not required to submit an application for the MCES. Those pursuing SSG-funded programmes will be charged the appropriate subsidised fees by SUTD Academy if they are eligible MCES. Individuals/employers will only need to pay the nett fee (full course fee after SSG's grant).

For more info, please visit SkillsFuture website at https://www.skillsfuture.gov.sg/enhancedsubsidy

Funding under Enhanced Training Support for SMEs ("ETSS")

ETSS is an enhanced funding to enable SMEs to send their employees for training.
SMEs will enjoy subsidies of up to 90% of the course fees when they sponsor their employees for SSG-funded certifiable courses.
In addition to higher course fee funding, SMEs can also claim absentee payroll funding of 80% of basic hourly salary at a higher cap of $7.50 per hour. SMEs may apply for the absentee payroll via the SkillsConnect system.
To qualify, SMEs must meet all of the following criteria:
- Organisation must be registered or incorporated in Singapore
- Employment size of not more than 200 or with annual sales turnover of not more than $100 million
- Trainees must be hired in accordance with the Employment Act and fully sponsored by their employers for the course
- Trainees must be Singapore Citizens or Singapore Permanent Residents

For more info, please visit SSG website at https://www.ssg.gov.sg/programmes-and-initiatives/funding/enhanced-training-support-for-smes1.html

Funding under Union Training Assistance Programme ("UTAP")

UTAP is a training benefit for NTUC members to defray their cost of training. This benefit is to encourage more union members to go for skills upgrading.

NTUC members enjoy 50% unfunded course fee support for up to $250 each year when you sign up for courses supported under UTAP (Union Training Assistance Programme).

For more info, please visit https://e2i.com.sg/individuals/ntuc-education-and-training-fund/.

Funding under Post-Secondary Education Account ("PSEA")

The Post-Secondary Education Account (PSEA) is part of the Post-Secondary Education Scheme to help pay for the post-secondary education of Singaporeans.

This is part of the Government’s efforts to encourage every Singaporean to complete their post-secondary education. It also underscores the Government’s commitment to support families in investing in the future education of their children and to prepare them for the economy of the future. PSEA is not a bank account.

It is administered by the Ministry of Education (MOE) and is opened automatically for all eligible Singaporeans.

Account holders can use their PSEA funds to pay for their own or their siblings’ approved fees and charges for approved programs conducted by approved institutions.

However, you will have to check your eligibility and balance by contacting MOE first.

Contact MOE at (65) 6260 0777

E-mail to MOE at contact@moe.edu.sg

Click here for MOE website.