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Predictive Analytics with Orange

Course Information

  • Sessions 1 day
  • Duration 7.5 hrs
  • Level Beginner
  • Assessment NA

Venue

12 Woodlands Square #07-85/86/87 Woods Square Tower 1, Singapore 737715. 5 mins walk from Woodlands (NS9) MRT station.

The venue is disabled-friendly.

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Certification

  • Certificate of Completion from Tertiary Infotech - Upon meeting at least 75% attendance and passing the assessment(s), participants will receive a Certificate of Completion from Tertiary Infotech.

Additional Information

Duration

2 months (Full Time)

Assessment

3 hours online assessment after each module

Class (No of teacher : student): 1:20

Intake

  • 3 Nov 2025 to 29 Sep 2026
  • 4 May 2026 to 26 June 2026
  • 2 Jan 2026 to 2 Mar 2026
  • 2 Mar 2026 to 27 Apr 2026

Enrolment Requirement

  • Age: 21 years old and above
  • Language Proficiency: At least C6 for GCE "O" Level English
  • Academic: At least C6 for GCE "O" Level in any 3 subjects

Graduation Requirement

  • Attendance: 75%
  • Assessment: Passed

Predictive Analytics with Orange

Course Code: C545

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What's This Course About

Discover the realm of predictive analytics with our Predictive Analytics with Orange training at Tertiary Courses. Orange, renowned for its user-friendly interface and comprehensive modules, has emerged as a leading tool in the analytics community. This course ensures participants unravel the extensive capabilities of Orange, enabling them to anticipate trends, patterns, and behaviors from their data.

Begin with a comprehensive overview of Orange and its landscape. Trainees will delve deep into classification, predictive modeling, and regression analysis techniques that power data-driven decisions. Progressing further, participants will explore advanced modules like clustering and image analytics, ultimately culminating in the powerful realm of dimension reduction. Join us and supercharge your predictive analytics skills, turning data insights into actionable strategies.

WSQ Funding

Full Fee $350.00 Before GST
GST $31.50 9% of fee
Baseline Nett $206.50 SG/PR age 21+ · 50% funded
MCES / SME Nett $136.50 SG age 40+ · 70% funded
Funding and Grant Applications

No funding is available for this course.

For WSQ funding, please checkout the details at WSQ - Data Mining and Machine Learning Fundamentals for Beginners

Course Fee

$350.00 (GST-exclusive)
$381.50 (GST-inclusive)

Course Date

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Additional Note

Please bring your own laptop for hands-on training. If you don't have laptop, we can provide spare laptop for training use.

Post-Course Support

  • We provide free consultation related to the subject matter after the course.
  • Please email your queries to enquiry@tertiaryinfotech.com and we will forward your queries to the subject matter experts.

Cancellation & Reschedule Policy

  • You can register your interest without upfront payment. There is no penalty for withdrawal of the course before the class commences.
  • We reserve the right to cancel or re-schedule the course due to unforeseen circumstances. If the course is cancelled, we will refund 100% for any paid amount.
  • Note the venue of the training is subject to changes due to availability of the classroom.

Course Details

Course Details

What You'll Learn

Topic 1 Overview of Predictive Analytics and Orange

Data Mining Process

Introduction to Machine Learning

Supervised vs UnSupervised Learnings

Overview of Orange

Topic 2: Data Preparation

Load Data to Orange

Interactive Visualization

Filter Data

Merge and Concat Data

Preprocess Data

Feature Statistics

Save Data

Topic 3: Regression

What is Regression

Linear Regression

Model Evaluation Metrics for Regression

Regularization

Topic 4: Classification

What is Classification

Classification Algorithms

K-Fold Cross Validation

Model Evaluation Metrics for Classification

Confusion Matrix

ROC Analysis for Binary Classification

Topic 5: Clustering

What is Clustering

K-Means Clustering

Silhouette Analysis

Hierarchical Clustering

Topic 6: Dimension Reduction

What is Dimension Reduction

Principal Component Analysis (PCA)

Feature Ranking

t-SNE and MDS

Topic 7: Association Analysis

What is Association Analysis

Apriori Algorithm

Association Analysis with Orange

Course Info

Promotion Code

Your will get 10% discount voucher for 2nd course onwards if you write us a Google review.

Minimum Entry Requirement

Knowledge and Skills

  • Able to operate using computer functions
  • Minimum 3 GCE ‘O’ Levels Passes including English or WPL Level 5 (Average of Reading, Listening, Speaking & Writing Scores)

Attitude

  • Positive Learning Attitude
  • Enthusiastic Learner

Experience

  • Minimum of 1 year of working experience.

Target Age Group: 21-65 years old

Minimum Software/Hardware Requirement

Software:

You can download and install the following software:

Hardware: Windows and Mac Laptops

Job Roles

Job Roles

  • Data Scientist
  • Machine Learning Engineer
  • Data Analyst
  • Business Intelligence Analyst
  • Research Scientist
  • Quantitative Researcher
  • Bioinformatics Scientist
  • Data Mining Specialist
  • Customer Insights Analyst
  • Marketing Analytics Specialist
  • Predictive Analytics Specialist
  • Healthcare Data Analyst
  • Financial Modeler
  • E-commerce Data Specialist
  • User Behavior Analyst

Trainers

Trainers

Dwight Nuwan Fonseka

Dwight Nuwan Fonseka is Head of Data Science at Plano Pte. Ltd. and an ACLP-certified trainer with deep expertise in data analytics, machine learning, and AI applications. He has extensive hands-on experience developing predictive models, RShiny dashboards, and deep learning solutions using R, Python, TensorFlow, and Keras. With a strong professional background in healthcare, finance, and customer analytics, Dwight brings an applied perspective to teaching AI, focusing on both the opportunities and risks of emerging technologies.

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