Data Analytics Essentials (DAE) with Python

Upcoming Batch
9:30am to 5:30pm
Singapore Timezone

4 Sessions / 32-Hours

8 to 11 Jun . 13 to 16 Jul
10 to 13 Aug . 14 to 17 Sep
12 to 15 Oct . 9 to 12 Nov
14 to 17 Dec

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Weekday (Every Tuesday)
8:30am to 12:30pm
Singapore Timezone

8 Sessions / 32-Hours

15 Jun to 3 Aug . 10 Aug to 28 Sep
5 Oct to 23 Nov . 30 Nov to 18 Jan

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Every Saturday
9:30am to 5:30pm
Singapore Timezone

4 Sessions / 32-Hours

12 Jun to 3 Jul . 10 Jul to 31 Jul
7 Aug to 28 Aug . 4 Sep to 25 Sep
2 Oct to 23 Oct . 6 Nov to 27 Nov
4 Dec to 25 Dec

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Course Information
  • Duration: 4 Day / 32 Hours

  • Certification: Participants will receive a Certificate of Competency upon successfully completing the course and passing the examination

  • Who Should Attend: Aspiring Data Scientist, Data Analyst, HR Analyst, and Anyone interested in pursuing a career in the areas of Business Analytics / Data Analytics

Course Objective

Acquire the essential knowledge on how to use data analytics to make better business or organisational decisions.

Learn the different components of Data Analytics, Data Mining, Data Warehousing and Visualization using Python


No pre-requisite. Data Analytics Essentials (DAE) is suitable for anyone who is interested in Data Analytics and does not have any prior technological experience


Participants are required to attempt an examination upon completion of course. This exam tests a candidate’s knowledge and skills related to Data Analytics and Python Programming based on the syllabus covered

Module 1 Introduction to Data Analytics

Topics Covered
  • Data Analytics Overview
  • Concepts of Data Analytics
  • Importance and Advantages of Data Analytics
  • Developing / Application of Data Analytics Strategies
  • Data Analytics Maturity Model
  • Understanding Descriptive, Predictive and Prescriptive Analytics

Module 2 Different Types of Analytics and Application

Topics Covered
  • Different Application of analytics method
  • Concepts of Text Analytics and Web Analytics
  • Different Application of Analytics Methods
  • Data / information architecture
  • ETL Architecture
  • What is Data Warehouse
  • Business intelligence vs Data Analytics
  • Application of Analytics in an Organisation

Module 3 Deep Dive into Python Programming

Topics Covered
  • Introduction to Python Programming
  • Setting up Python IDE and Programming Environment
  • Understanding Structure of Python Programming
  • Python Variables: Integer, Floats, Strings
  • Using of List vs. Dictionary
  • Operators and Loops: If-Else, For, While, Break, Continue
  • Types of Functions in Python
  • Introduction to Built-In Functions in Python
  • Introduction to Classes in Python
  • What is Object-Oriented Programming (OOP)

Module 4 Working with Key Modules / Packages

Topics Covered
  • Understanding Modules in Python
  • Working with NumPy Module
  • Using Python Pandas Module
  • Data Pre-processing, Data Cleaning, and Data Engineering
  • Introduction to MatPlotLib in Python
  • Data Visualization using Python Programming
Module 5 Data Mining Processes for Data Analytics

Topics Covered
  • Fundamentals of Data Mining
  • Objectives of Data Mining
  • Key aspects of Data Mining
  • Concepts of Knowledge Discovery in Databases (KDD)
  • Models in Data Mining
  • Data Mining Model vs Statistical Model
  • Data Mining Processes

Module 6 Data Mining Techniques

Topics Covered
  • Different Data Mining Techniques
  • Data Classification
  • Clustering Analysis
  • Regression Analysis
  • Association Rules
  • Outliers Analysis
  • Sequential Patterns
  • Predictive Analytics

Module 7 Understanding Machine Learning

Topics Covered
  • Statistical learning vs Machine learning
  • Iteration and evaluation
  • Supervised, Unsupervised and Reinforcement Learning
  • Different Phases of Predictive Modelling (Data Pre-processing, Sampling, Model Building, Validation)

Module 8 Data Visualization with Python

Topics Covered
  • Introduction Exploratory Data Analysis
  • Descriptive statistics, Frequency Tables and summarization
  • Univariate Analysis (Distribution of data, Graphical Analysis)
  • Bivariate Analysis (Cross Tabs, Distributions & Relationships, Graphical Analysis)

What Past Participants Say
The trainer work through the codes line by line and explains what the codes do and to get us to do the hands-on practice. Learning was well paced and progressive. I’ve achieved my objectives of growing from zero knowledge on python to being able to get an understanding and using the codes to run data-analytics.

Dr. Siti Zubaidah

The trainer display professionalism, patience and subject expertise and guides us through the course despite our lack of technical experience. Sessions are kept interactive giving us many opportunities to engage with trainer in a personal manner.

Dr. Ting Kok Guan


Customization of Programs for specific industry, organisation, government agencies, statutory boards.

Flexible programmes designed to cater to the individual needs of participants, whether for professional upskilling, or for general interest.

Benefit from contribution from leading Industry Experts, Academics, and Researchers from across the world.

Opportunities for employers to develop their workforce and for individuals to enhance their career.

Dynamic learning environment that providing participants with professional networking opportunity.

Online support for participants after the training.

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