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Data Science Modelling with Excel

Part of the ModularMaster in Data Science programme

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.

Course Details

Course Dates:
New intake coming soon.

Register your interest
 

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

Programme Outline

Learning Objectives and Structure

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

  1. Appreciate the use of basic model by junior data scientist in a general setting
  2. Adopt a statistical approach to solving ambiguous issues
  3. Apply computational thinking to solve business problem
  4. Leverage on Basic Supervised and Unsupervised Learning Model appropriately for Business use case
  5. Conduct statistical test on Business use case
  6. Experiment with various machine learning model specific to business context
  7. Evaluate various parameters based on the consequence of the predicted model
  8. 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
  • Quantitative Foundation for Machine 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

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

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:

  1. Reporting of national statistics and conducting of holistic continuing education training research and analysis;

  2. Facilitate the conduct of the relevant surveys and audits in relation to the Course;

  3. General administration of the Course including but not limited to processing of the subsidy provided by SSG;

  4. Publicity and marketing of the Course or other Courses to be provided by SSG or SUTD Academy; and

  5. 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:

  1. Attend and complete all lectures, class exercises, workshops and assessments;

  2. Complete the Course feedback at the end of the Course;

  3. Complete the post Course survey sent about 3 to 6 months after class attendance; and

  4. 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

  1. 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. 

  2. 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

  1. 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.

  2. Course enrolment is based on a first-come, first-served basis.

  3. 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")

  1. 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.

  2. 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 http://www.skillsfuture.sg/enhancedsubsidy

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

  1. ETSS is an enhanced funding to enable SMEs to send their employees for training.

  2. SMEs will enjoy subsidies of up to 90% of the course fees when they sponsor their employees for SSG-funded certifiable courses.

  3. 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.

  4. To qualify, SMEs must meet all of the following criteria:
    - Organisation must be registered or incorporated in Singapore
    - At least 30% local shareholding by Singapore Citizens or Singapore Permanent Residents
    - Employment size of not more than 200 (at group level) or with annual sales turnover (at group level) 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 http://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/.

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