Singapore University of Social Sciences

Generating and Understanding Insights from Machine Learning Analytics using R

Generating and Understanding Insights from Machine Learning Analytics using R (CET253)

Applications Open: 16 September 2020

Applications Close: 01 December 2020

Next Available Intake: 15 December 2020

Course Types: Short Course

Language: English

Duration: 2 days

Fees: $1400

Area of Interest: Others

Schemes: Lifelong Learning Credit (L2C)

Funding: SkillsFuture

School/Department: Centre for Continuing & Professional Education


Next Available Intakes: 15 & 16 December 2020, 11 & 12 January 2021, 09 & 10 February 2021, 12 & 13 July 2021, 23 & 24 August 2021
Level: Intermediate
Duration: 2 days
Venue: Singapore University of Social Sciences
Minimum number to run: 25 participants (To be updated by SUSS)


Synopsis

Today, the word's data is growing at an astonishing rate. It is said that the world's most valuable resource is no longer "oil", but "data". More and more, we see forward-looking corporates harvesting and analysing large complex datasets to improve competitiveness. Every now and then, we come across narratives of how disruptive technology can be, and have heard mantras that we need to continually acquire new skillsets such as analytics and data science to stay relevant in this increasingly digitalised world. Yet, how much do we really know about these subjects?

This course endeavours to equip learners with the essentials of R programming, a powerful statistical and machine learning tool at the heart of data science and data analytics. Through this course, learners will acquire the skillsets to:

  • manipulate, clean and enrich raw data into more usable formats;
  • discover hidden patterns in the enriched datasets;
  • connect the dots, interpret and derive actionable insights to facilitate decision making.

Who Should Attend

  • Existing undergraduates looking to acquire analytics skillsets prior to entering the workforce.
  • Existing professionals looking to acquire analytics skillsets to future-proof their careers.
  • Forward-thinking professionals looking to quickly pick up the essentials of machine learning algorithms so as to transform and make an impact to their organisations.
  • Curious individuals (including nerds) who wishes to seek a technical understanding of data analytics using machine learning algorithms.

Topics

  • Essentials of R Programming, Data Wrangling, and Data Visualization
  • Multiple Linear Regression modelling and deriving insights for decision making
  • Logistic Regression modelling and deriving insights for decision making
  • Decision Trees (Classification Trees) modelling and deriving insights for decision making
  • Cluster Analysis modelling and deriving insights for decision making

Learning Outcomes

A. Knowledge and Understanding (Theory Component)

At the end of this course, participants should be able to:

  • Explain key machine learning concepts.
  • Apply suitable machine learning models to address different business problems.
  • Analyse results derived from machine learning models.

B. Key Skills (Practical Component)

At the end of this course, participants should be able to:

  • Generate enriched datasets from raw data.
  • Construct machine learning models to analyse business data.
  • Predict future trends using past data.

Schedule

TimeAgenda
Day 1
09:00 - 09:30Course overview
09:30 - 10:30R programming - programming basics
10:30 - 10:45Break
10:45 - 12:00R programming – data wrangling
12:00 - 13:00Lunch
13:00 - 15:30R programming – data visualization
15:30 - 15:45Break
15:45 - 18:00Multiple Linear Regression modelling and deriving insights for decision making
Day 2
09:00 - 09:15Course overview
09:15 - 10:30Logistic Regression modelling and deriving insights for decision making
10:30 - 10:45Break
10:45 - 12:00Decision Trees (Classification Trees) modelling and deriving insights for decision making
12:00 - 13:00Lunch
13:00 - 15:30Cluster Analysis modelling and deriving insights for decision making
15:30 - 15:45Break
15:45 - 17:00Cluster Analysis – Part 2
17:00 - 18:00Assessment


Assessments

  • Written Quiz

Requirements

  • Attendees ideally should have some knowledge of analytics and R, but learners without such knowledge are welcome as well, so long as they have GCE ‘O' Levels as minimal entry requirement.
  • Attendees have to bring along their own (personal) laptop, with at least 8 GB RAM. Attendees are discouraged from bringing corporate laptops to the course as there may be restrictions in installing R software and its packages.

Trainer's Profile

Tai Hock LinMr Tai Hock Lin is an experienced trainer in data science and data analytics using R programming. He graduated with a Master Degree in Applied Economics from the National University of Singapore, with a distinguished CAP score of 4.6.

Hock Lin has over 20 years of working experience in many establishments, including universities, startups, and government bodies. He specialises in analysing corporate growth potential and have supported many startups, SMEs, and MNCs to tap on government grants to jumpstart their digitalisation initiatives. Additionally, he has also coached, account managed, and invested promising startups while they were still being incubated in one of Singapore's renowned incubation ecosystems, including BLK 71. Prior to devoting full time into lecturing, he was with the Singapore civil service working on economic research and other policy initiatives. In these roles, Hock Lin has acquired unique perspectives of the real-world challenges that corporates and individuals faced, as well as the limitations and trade-offs of government policies. As a trainer, he delivers analytic courses that focuses on business insights and macroeconomic trends analysis, and incorporates real-world perspectives as case studies. In turn this helps learners better understand and apply acquired learnings into practice.

Aside from analytics courses in SUSS, Hock Lin also taught "Wiley Certified Data Analytics", as well as "Fundamentals of Applied Quantitative Data Analytics using R", and many more courses in other institutions. Hock Lin has acquired WSQ Advanced Certificate in Training and Assessment (ACTA) that specialises in conducting courses for continuing professional development

Application Procedures

Please submit the following documents to cet@suss.edu.sg:

  1. Coloured copy (back and front) of NRIC for Singaporeans and PRs, or "Employment"/"S" Pass for foreign applicant
  2. Application form

Course Fee

International ParticipantsS'poreans (aged below 40) and PRsSkillsFuture Mid-Career Enhanced Subsidy1
(S'poreans aged 40 and above)
Enhanced Training Support for SMEs2
Full Course fee (A)$1400$1400$1400$1400
SSG grant (70%) (B)-($980)($980)($980)
Nett course fee (A) - (B) = (C)$1400$420$420$420
7% GST on nett course fee (D)$98$29.40$29.40$29.40
Total nett course fee payable, including GST (C) + (D) = (E)$1498$449.40$449.40$449.40
Less additional funding if eligible under various schemes (F)--($280)($280)
Total nett course fee payable, including GST, after additional funding from the various funding schemes (E) - (F) = (G)$1498$449.40$169.40$169.40

1 Mid-Career Enhanced Subsidy: Singaporeans aged 40 and above may enjoy subsidies up to 90% of the course fees.
2 Enhanced Training Support for SMEs: SME-sponsored employees (Singaporean Citizens and PRs) aged 21 and above may enjoy subsidies up to 90% of the course fees.

  • Participants are required to achieve at least 75% attendance and/or sit and pass any prescribed examinations/assessments or submit any course/project work (if any) under the course requirement.
  • Participants are required to complete all surveys and feedbacks related to the course.
  • The course fees are reviewed annually and may be revised. The University reserves the right to adjust the course fees without prior notice.
  • Singapore University of Social Sciences reserves the right to amend and/or revise the above schedule without prior notice

For clarification, please contact the Centre for Continuing and Professional Education (CCPE) via the following:

Telephone: +65 6248 0263
Email: CET@suss.edu.sg
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