SITizens Learning Credits


Engineering decisions are increasingly being made based on interpretation of large amount of decisions. This two-day interactive workshop will provide participants with the basic knowledge and skills in data analytics.

This 2-day workshop will focus on classification techniques to predict categorical and continuous variables. Important issues in classification like under- and overfitting, evaluation of model performance and feature selection will be covered. Hands-on practical will be conducted.

To aid companies in transforming their capabilities through their human capital, and support Singapore’s drive towards becoming a Smart Nation, this course is mapped to the Singapore Economic Development Board’s Singapore Smart Industry Readiness Index:

  • Dimension 3 - Integrated Product Lifecycle
  • Dimension 10 - Shop Floor Intelligence
  • Dimension 11 - Enterprise Intelligence

Teaching Team

What You’ll Learn

Multiple Linear Regression
- Fitting of linear regression model to dependent variable and interpreting model performance using p-value, R-square, adjusted R-square and residuals.

Logistic Regression
- Fitting of logistic regression model to dependent categorical variable and interpreting model performance with the confusion matrix.

Issues in Classification
- Feature normalization, data pre-processing, under- and overfitting, feature selection, regularization and cross-validation

Practical machine learning
- Using freely available graphical programming software to perform classification and linear regression tasks.

Who Should Attend

  • For general audience with basic understanding of statistics and no background in programming who wants to learn more about data analytics and machine learning.
  • For people with Engineering background and want to learn more about how data analytics is used in decision making.


  • Basic Statistics
  • Course Journey: This course is part of a three courses series on data analytics. Upon completion of this course, students can sign up for Data Analytics for Engineer II.

SITizens Learning Credits (SLC) - Eligible Course

SIT Alumni: Before registering for courses, please activate your SITizens Learning Credits via the email sent by SIT Alumni Team, on behalf of SITLEARN Professional Development.

CLICK HERE TO APPLY(only for SIT Alumni)

Certificate and Assessment

A Certificate of Participation will be issued to participants who:
  • Attend 75% of the course
  • Undertake non-credit bearing assessment (during the course)


Day 1: 19 Nov

Lecture 1: Multiple linear regression 
Tea Break
Lab 1: Multiple linear regression
Lecture 2: Logistic regression
Tea Break
Lab 2: Logistic regression
Day 1 wrap up

Day 2: 26 Nov

Lecture 3: Model selection
Tea Break
Lab 3: Model selection
Lecture 4: Data Handling


Category Full Fee After SF Funding After SF Mid-Career
Enhanced Subsidy
Singapore Citizen (Below 40) / Singapore PR $1,712.00 $513.60 Not Eligible
Singapore Citizen (40 & above) $1,712.00 $513.60 $193.60
Non-Singaporeans $1,712.00 Not Eligible Not Eligible

  • All figures include GST. GST applies to individuals and Singapore-registered companies.
  • You can opt for either SF Series Funding or Mid-Career Enhanced Subsidy. Both cannot be combined.

»  Learn more about funding types available

Terms & Conditions:

SkillsFuture Funding

In order to be eligible for the 70% training grant awarded by SkillsFuture, applicants (and/or their sponsoring organisations where applicable) must:
  1.  Be a Singaporean Citizen or Singapore Permanent Resident
  2.  Not receive any other funding from government sources in respect of the actual grant disbursed for the programme

SkillsFuture Mid-Career Enhanced Subsidy

To be eligible for the 90% enhanced subsidy awarded, applicants (and/or their sponsoring organisations where applicable) must:
  1.  Be a Singaporean Citizen
  2.  Be at least 40 years old
  3.  Not receive any other funding from government sources in respect of the actual grant disbursed for the programme

SIT reserves the right to collect the balance of the programme fees (i.e. the potential grant amount) directly from the applicants (and/or their sponsoring organisations where applicable) should the above requirements not be fulfilled.

SIT reserves the right to make changes to published course information, including dates, times, venues, fees and instructors without prior notice.
SITizens Learning Credits

Key Info

Venue SIT@Dover, 10 Dover Drive S138683
Time 09:00 AM to 06:00 PM
Date 19 Nov 2020 (Thu) to
26 Nov 2020 (Thu)
Registration is Closed.

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