Data Analytics for IT Professionals

Overview

Overview

Duration 2 days
Course Time 9.00am - 5.00pm
Enquiry Click here to contact us

This course is being offered and delivered by School of Continuing and Lifelong Education (SCALE), NUS.

This course aims to provide participants with a foundation in:

  • Overview of data and data analytics
  • Data analytics process and project life cycle
  • Tools and technology used for data analytics and visualisation
  • Machine learning and data visualisation techniques


Key Takeaways

Key Takeaways

At the end of the course, participants will be able to:
  • Apply data analytics techniques using tools through hands-on sessions
  • Apply data visualisation techniques using tools through hands-on sessions
  • Gain insights from data using various machine learning techniques

Who Should Attend

Who Should Attend

This course is targeted at Data and IT Professionals.

Pre-requisites

Knowledge of data management and fundamental statistics.


ICT and SS Competency Framework

ICT and SS Competency Framework

As part of the ICTCF, this course falls under the Apps Management functional cluster and tagged to the following competencies:
  • Data Management & Analytics
The course is mapped to the following job roles:
  • Project Manager (Agile)
  • Digital Business Analyst
  • Solutions Architect
  • Digital Service Manager (DSM)

Course Structure

Course Structure

The course is conducted virtually and cover the following topics:

Day 1
  • Big Data and Data Analytics
  • Tools and Technology
  • Data Analytics Process
  • Introduction to Machine Learning – Supervised and Unsupervised Learning
  • Overview - All about Data
    • Data concepts
    • Statistics Primer 
  • Analytics Project Life Cycle (CRISP-DM)
    • What is going with the data in each data analytics phase?
    • Model Development, Deployment, Scoring and Refresh
  • Hands-on Exercise Lab using Orange
  • Supervised Learning Models
    • Data Cleaning Concepts/Techniques
    • Exploratory Data Analysis
    • Data Preparation 
    • Linear Regression 
    • Model Evaluation R2
  • Hands-on Exercise Lab using Orange
  • Logistic Regression (Predicting HDB Prices)
  • Decision Tree, Random Forest
  • Model Evaluation 
  • Confusion Matrix, ROC, AUC


Day 2

  • Visualisation
    • Visualisation Concepts and Principles
    • Selecting the Visualisations
  • Hands-on Exercise Lab using Tableau
    • Creating Visuals and Dashboards
  • Unsupervised Learning Models
  • Clustering Using K-means
    • Concepts and Method
    • Application
  • Hands-on Exercise Lab Using Orange
  • NLP - Text Analytics
    • Concepts and Method
    • Application
      • Text Pre-processing 
      • Visualisation 
      • Stop Words 
      • Clustering 
      • Text Classification 
  • Hands-on Exercise Lab Using Orange

Instructors

Instructors


Fees and Funding

Fees and Funding

Full Fee

Full Programme Fee

S$1,900.00

7% GST on Nett Programme Fee

S$133.00

Total Nett Programme Fee Payable, Incl. GST

S$2,033.00


NOTE: Funding is available for this course. Please visit the training provider’s website to find out about the updated course fee funding breakdown, eligibility, terms and conditions.

 
 

Upcoming Classes

Upcoming Classes

Class 1

Duration: 2 days

10 Nov 2022 to 11 Nov 2022 (Full Time)

When :
Nov:
10, 11

Time : 09:00am to 05:00pm
Registration:

How To Register

How To Register


Agency-sponsored

Step 1 Apply through your organisation's training request system.

Step 2 Your organisation's training request system (or relevant HR staff) confirms your organisation's approval for you to take the course.

Your organisation will send registration information to the academy.

Organisation HR L&D or equivalent staff can click here for details of the registration submission process.


Step 3 The Digital Academy will inform you whether you have been successful in enrolment.