SIT Learn
SIT Learn

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.

This 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 using KNIME GUI.

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 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 Find No Results

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

Speakers / Trainers

What You’ll Learn

Basic Statistical methods

  • Calculation of mean, median and correlation.

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

Other classification techniques

  • SVM and nearest neighbours algorithm will be introduced.

Hands-on machine learning using KNIME

Learn how to use KNIME to solve classification problems.

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.
 

Prerequisites

  • 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 on 31 Jan 2019.

CLICK HERE TO APPLY (only for SIT Alumni)
 

Certificate and Assessment

  • 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.
 

Prerequisites

  • Basic Statistics
  • Find No Results

Schedule

Day 1: 18 Oct

Time Activity
8:30 am Welcome and Registration
9:00 am Introduction to data analytics
10:00 am Simple linear regression/Multiple linear regression
10:30 am Introduction to KNIME and simple filtering and grouping functions.
11:20 am Break
11:40 am More hands-on practice. Finding mean, median, correlation, scatterplot and scatter matrix.
12:30 pm Lunch
1:30 pm Linear Regression and hands-on practice
2:30 pm Break
2:50 pm Underfitting and overfitting issues
3:30 pm Feature transformation
4:00 pm Feature selection and cross validation
5:15 pm Day 1 wrap up
 

Day 2: 25 Oct

Time Activity
8:30 am Welcome and summary
9:30 am Logistic regression model
10:10 am Break
10:30 am Tools to evaluate logistic regression model performance and hands-on.
11:00 Regularization
12:00 pm Lunch
1:00 pm Hands-on to implement regularization
2:00 pm Data preprocessing
3:05 pm Break
3:20 pm Other classification methods
4.00 pm Data analytics on Engineering examples
4:40 pm Day 2 wrap up
4:50 pm MCQ

Fees

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

Note:
  • 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.

Key Info

Venue SIT@Dover, 10 Dover Drive S138683
Time 08:30 AM to 05:30 PM
Date 18 Oct 2019 (Fri)
25 Oct 2019 (Fri)

Apply Now

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