DSAI

Data Science for Power Users

Enquiry
Programme Code D119
Domain
Data Science & AI
General Digital Literary
Level
Intermediate
Learning Partner(s)
Nanyang Polytechnic
Duration
14 Days
Format In-person
Rating
Competencies
Development & Deployment Data Visualisation & Communication Data Integration Visual Analytics Principles Statistical Techniques Scripting Machine Learning Exploration Analysis Data Storytelling Data Science Data Quality Data Collection Charts & Dashboards
Job Roles
Public Service Officer (non-ICT&SS)

Overview

Predictive analytics generates future insights with a significant degree of precision based on historical data. Through this programme, you will demonstrate your competencies in developing a predictive analytics project in teams by applying data privacy and ethical principles in the collection, use and disposal of data. With the data collected, you will demonstrate how to impute data format, transform and reshape the business data before applying relevant predictive modelling techniques to predict the desired business outcomes. You will then perform data exploratory analysis to discover patterns and trends and develop data stories for an effective narrative and visual representation of their predictive analytics project.

Key Takeaways

At the end of this programme, you will be able to:

  • collect data from multiple sources using appropriate collection tools and techniques that comply with data and privacy ethics
  • perform data pre-processing techniques to impute data format, transform, reshape and protect the data in accordance with the business requirements and data protection principles
  • develop interactive and effective visualisations with a global perspective to address international and cultural differences, diverse needs and expectations of the key stakeholders using visualisation tools
  • perform data exploratory analysis to identify underlying data patterns, trends and analytical insights using visualisation tools
  • apply relevant predictive modelling techniques to predict the desired business outcomes to meet the service expectation of the key stakeholders
  • work collaboratively in a team to develop dashboards using data storytelling approach for an effective narrative and visual representation of their predictive analytics project

Who Should Attend

  • Please refer to the job roles section.
  • ICT&SS Professionals with knowledge of databases and software development.

Prerequisites

Basic data analytics.

Programme Structure

This programme will cover the following topics:

  • Introduction to Data Visualisation
  • Preparing Data for Analysis
  • Visualising Data
  • Create data-driven story
  • Advanced Interactive Report
  • Introduction to Python
  • Descriptive Statistics
  • Data Analysis using Python
  • Data Preparation Techniques
  • Introduction to Machine Learning Techniques
  • Machine Learning
  • Feature Engineering
  • Model Comparison & Evaluation
  • Natural Language Processing
  • Predictive Modelling Project

Fees

 

Full Fee

Full programme fee

S$2400

8% GST on nett programme fee

S$192

Total nett programme fee payable, including GST S$2592
With effect from 1 Jan 2023 till 31 Dec 2023


 
NOTE
Funding is available for this programme. Please email to Aloysius or Angeline to find out about the updated programme fee funding breakdown and eligibility.

Upcoming Classes

Class 1
16 Aug 2023 to 15 Nov 2023 (Full Time)
Duration: 14 days
When: Aug - 16, 23, 30Sep - 06, 13, 20, 27Oct - 04, 11, 18, 25Nov - 01, 08, 15
Time : 9:00 AM to 5:00 PM

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 programme.

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 GovTech Digital Academy will inform you whether you have been successful in enrolment.