Data Science Modelling with Excel
In the 21st century, we continue to see a rising trend in the applications of Data Science (DS) from the use of GrabPay to the example of Siri. The applications of Data Science encompass all walks of life and indirectly every aspect of the business - from how business acquires data to how data contributes and complements with the traditional intuitive decision-making approach. Data Science will not only continue to shape how industries operate in the near future but also revolutionise how firms harness Data to its full potential. Data Scientist has been hailed the "sexiest career in the 21st Century", however, not only are firms competing to hire good data scientists, but are also starting to see the need to groom their in-house Subject Matter Experts into a functional-hybrid kind.
We will focus on how Data Science is being used across a wide spectrum of industries. We will also explore various use cases and applications of Data Science and its potential for improving our daily lives. Participants will be able to leverage on Data Science to complement with their Domain Expertise to contribute within the Data Science Pipeline. Participants will be equipped with the Basics of Data Science Modelling and Algorithm with Excel.
This course is designed to introduce the general concepts of Data Science to anyone, regardless of their computing or math background.
At the end of this course, participants will be able to apply the basic Predictive Analytics, basic Machine Learning and basic Statistical Model based on a prescribed project.
Plan your learning path
This course can be taken as a module on its own or as part of the Graduate Certificate in Data Analytics (Non-Programming) stack and participants will earn 12 subject credits which can also be used towards completing the ModularMaster in Data Science.
Who Should Attend
- Learners especially those working in an industry or role dealing with data, who would benefit from data science modelling with the use of ready tools, applications, plugins and Microsoft Excel.
Prerequisites
- Some literacy and competency in basic mathematics and computers
- Participants are encouraged to complete Data Wrangling and Preparation with Excel and Data Validation and Statistical Analysis with Excel before enrolling for this course
Course Fees and Funding
SkillsFuture Course Fee Subsidy
(70%)
Fee after subsidy
$1,350.00
GST on Fee after SSG Course Fee Subsidy
$94.50
You pay
$1,444.50
Mid-career Enhanced Subsidy (MCES)
(90%)
Fee after subsidy
$450.00
GST on Fee after SSG Course Fee Subsidy
$94.50
You pay
$544.50
Enhanced Training Support for SMEs (ETSS)
(90%)
Fee after subsidy
$450
GST on Fee after SSG Course Fee Subsidy
$94.50
You pay
$544.50
Full Course Fee (without subsidies): $4,815 (inclusive of prevailing GST)
Instructor
Johnson Ang
Instructor, SUTD Academy
Johnson Ang has a Masters in IT Analytics from SMU and is certified and a member of CQF, ACTA and CEH. He previously work as a Data Scientist with MND and was an ex-affiliate University lecturer for Cardiff Metropolitan UK University and ex-lecturer with EASB in 2018 and was also Corporate and Open-Run trainer for 2 years.
His experience spans more than 10 years of working experience, of which 4 years of experience in the Banking Wide Operations and has been particularly involved in IT Analytics banking projects within the Risk Management and Financial Instrument sphere, and 4 years in the Real Estate, FMCG, Construction and Education Sector. He is familiar with typical banking software and acquainted with popular tools such as SAS, JMP, Oracle, VBA, Access, Python, R, Tableau, Weka, C and Java. He also has working knowledge in the area of Data Science, Analytics, Machine Learning and Deep Learning.
ModularMaster Certificate in Data Science
Course Structure and Pathways
Two Tracks
We have designed two tracks to cater to participants with different learning preferences.
Participants who prefer to make use of programming can go for the modules under the Programming Track while participants who prefer to make use of readily available tools can embark on modules under the Non-Programming Track.
For participants who do not possess a programming background, you may also take on a Bridging pathway towards the Programming Track.

Policies and Financing Options