Data Science Modelling with Excel

Part of the ModularMaster in Data Science programme

In the 21^st 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 21^st 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

Participants should preferably have passed mathematics at least ‘O’ Level or equivalent.
Participants should preferably have basic knowledge of statistic.
Participants should be conversant with basic IT skills such as software installation, file management and web navigation.
Participants should have basic familiarity with Microsoft Excel: using built-in functions to perform some calculations.
Participants are encouraged to complete Data Wrangling and Preparation with Excel and Data Validation and Statistical Analysis with Excel before enrolling in this course.
Participants are required to bring their laptops.

Programme Outline

Learning Objectives and Structure

By the end of this module, participants will be able to

Appreciate the use of basic model by junior data scientist in a general setting
Adopt a statistical approach to solving ambiguous issues
Apply computational thinking to solve business problem
Leverage on Basic Supervised and Unsupervised Learning Model appropriately for Business use case
Conduct statistical test on Business use case
Experiment with various machine learning model specific to business context
Evaluate various parameters based on the consequence of the predicted model
Decipher and deconstruct convoluted patterns into meaningful insights

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

Basics of Statistics
Application of Statistics in Real World
Introduction to Quantitative Intuition for Statistics
Steps in Hypothesis Testing
Z Test
T Test

Day 2

Overview of Data Science
Data Science Pipeline
What is Machine Learning Model?
Understand what is required prior to using machine learning model
Understand how data scientist trains machine learning model
Data Preparation and Data Validation
Train-Test Split and Cross Validation
An introduction to Supervised and Unsupervised Learning

Day 3

Data Preparation for Linear Regression
Simple Linear Regression
Multiple Linear Regression
Evaluating Linear Regression Models Performance
An extension of regression on Correlation, Covariance and Multi-collinearity Issues
Remedies for Multi-Collinearity
Data Preparation for Logistic Regression
Logistic Regression Model
Evaluating Logistic Regression Models Performance

Day 4

Data Preparation for Clustering
K-Means Clustering
Dimensionality Reduction
Basics of Principal Component Analysis
Interpreting Principal Component Analysis Results

Day 5

Project Presentation

Assessment

Participants will be assessed via group based project presentation on the 5th session of the course. There will also be formative assessment and case studies to assess a participant's understanding and competency.

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$4,815.00
SkillsFuture Course Fee subsidy (70%) For Singapore Citizens < 40 years old For Permanent Residents	You pay S$1,444.50
Mid-Career Enhanced Subsidy (90%) For Singapore Citizens ≥ 40 years old	You pay S$544.50
Enhanced Training Support for SMEs (90%) For SME - Sponsored employees	You pay S$544.50

The above module fee payable is inclusive of GST. 1% GST increase in 2023 is absorbed by SUTD.

Instructor

Assoc Prof Duan Lingjie
Singapore University of Technology and Design

Lingjie Duan is an Associate Professor (Tenured) in the Engineering Systems and Design Pillar at Singapore University of Technology and Design. He received Ph.D. degree in Information Engineering from The Chinese University of Hong Kong in 2012. During 2011, he was a visiting scholar in the Department of Electrical Engineering and Computer Sciences at University of California at Berkeley.

Lingjie Duan has been actively working and contributing to the interdisciplinary research field combining computer networks and game theory. He has used optimization and game theory extensively as both modeling languages and solution tools to study the cooperative or competitive interplay among various parties in communications and networking. He received 2016 SUTD Excellence in Research Award, and in 2015 he received the 10th IEEE ComSoc Asia-Pacific Outstanding Young Researcher Award. He was also the Finalist of Hong Kong Young Scientist Award 2014 under Engineering Science track. He has many highly-cited top engineering and business publications, and his works on network economics attract ever-increasing attention from academia and industry.

Thia Wei Soon
Instructor, SUTD Academy

Wei Soon has more than ten years of experience working in the manufacturing and IT sectors. He worked as a data scientist using data analytics and machine learning to deliver actionable insights and drive strategic marketing initiatives. In recent years, as a technology consultant, he successfully helped clients to streamline enterprise operations and achieved cost saving through the adoption of robotic process automation.

Wei Soon has a Master of IT in Business Artificial Intelligence from Singapore Management of University and a B.Eng in Mechanical Engineering from Nanyang Technological University. He is proficient with tools such as Tableau, Jupyter, RStudio, MS Visual Studio, Automation Anywhere, UiPath, and programming languages such as Python, R, C#, HTML5, and JavaScript.

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/.