Data Analytics Essentials (DAE)

About IFS Academy – Casugol Partnership

IFS Academy and CASUGOL, Singapore has signed a MoU for delivering world class Certification Programs to students, faculty and corporates in the field of Computer & Information Technology. IFS Academy is Certified Training Partner (CTP) of Casugol in India. Based in Singapore, CASUGOL is an International Certification Provider offering a wide range of professional Certification Programs, and Executive Workshops in Digital Transformation, and Emerging Technologies, designed for all industries and verticals. IFS Academy will run these courses in India in association with CASUGOL.


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

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

  • Dynamic learning environment that providing participants with professional networking opportunity.

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

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

  • Online support for participants after the training.

Course Information

Training Schedule:

Time. Sessions Dates
9:30am to 5:30pm
(Singapore Timezone)
9 to 12 Nov, 14 to 17 Dec, 11 to 14 Jan
8:30am to 12:30pm
(Singapore Timezone)
(Every Tuesday)
30 Nov to 18 Jan
9:30am to 5:30pm
(Singapore Timezone)
(Every Saturday)
6 to 27 Nov, 4 to 25 Dec

Mode of Training: Instructor-Led Online

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

Course Contents:

Module 1 Introduction to Data Analytics

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

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

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

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

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

Different Data Mining Techniques

Data Classification

Clustering Analysis

Regression Analysis

Association Rules

Outliers Analysis

Sequential Patterns

Predictive Analytics

Module 7 Understanding Machine Learning

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

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)

Course Fees: USD 500

Admission / Registration Procedure:

Students are requested to complete their admission / registration formalities and pay the course fees by clicking on the Apply Now button given below.

Apply Now