SITizens Learning Credits

Overview

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.

The 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.

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 and other classification methods
  • 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.
Clustering techniques
  • K-means and hierarchical clustering will be covered.
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 individuals with Engineering background and keen to learn more about how data analytics is used in decision making.


SITizens Learning Credits (SLC) - Eligible Course

This course is SITizens Learning Credits (SLC) eligible. Please refer to the user guide how to register for courses utilising your SLC.

Find out more about SITizens Learning Credits (SLC).


Prerequisites

  • Has basic statistics knowledge.


Certificate and Assessment

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

Schedule

 
Day 1 Schedule
Time Topic
9:00 - 10:40 Lecture 1: Multiple linear regression
10:40 - 11:00 Break
11:00 - 13:00 Lab 1: Multiple linear regression
13:00 - 14:00 Lunch
14:00 - 15:40 Lecture 2: Logistic regression
15:40 - 16:00 Break
16:00 -17:30 Lab 2: Logistic regression
17:30 - 18:00 Wrap up
   
Day 2 Schedule         
Time Topic
9:00 - 10:40 Lecture 3: Model selection
10:40 - 11:00 Break
11:00 - 13:00 Lab 3: Model Selection
13:00 - 14:00 Lunch
14:00 - 15:10 Lecture 4: Clustering
15:10 - 15:30 Break
15:30 -17:00 Lab 4: Clustering
17:00 - 18:00 Quiz
  

Fees

Category Full Fee After SF Funding
Singapore Citizen (Below 40) /
Singapore PR
$1,926.00 $577.80
Singapore Citizen (40 & above) $1,926.00 $217.80
Non-Singaporeans $1,926.00 Not Eligible

Note:

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

» Learn more about funding types available

Terms & Conditions:

SkillsFuture Course Funding

To be eligible for the 70% training grant awarded, 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 21 Feb 2022 (Mon) to
22 Feb 2022 (Tue)
Registration Closing on: 31 Jan 2022
Apply Now

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