Data Analytics Essentials (DAE)


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  • 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 and technical skills on how data analytics can be used by organizations to enhance decision making and uncover hidden data insights.

Learn the core components of Data Analytics, Data Preprocessing and Cleaning, Data Mining, Data Warehousing and Visualization using Python Programming


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

Participants are expected to score a minimum of 70% to pass the examination

Module 1
Introduction to Data Analytics

Data Analytics is expected to revolutionize all industries and verticals. In this module, participants will engage in extensive discussion on what is data analytics, its key components and impact on businesses.

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

Learn how to craft a data strategy that meets your organization need. Participants will acquire essential knowledge on how to design a data strategy based on their organization’s specific needs. Participants will also have the opportunity to learn about the different techniques in data analytics and its application.

Topics Covered

  • Different Application of Analytics Method
  • Concepts of Text Analytics and Web Analytics
  • 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

Python Programming, a preferred programming language in the digital economy. It is also widely used by data scientist and data analysts. One of the key reason is due to its ease of application and its comprehensive libraries. In this module, participants will have the chance to develop a simple application using 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

Participants will be introduced to key modules and packages used for Data Analytics by going through a simple data analytics process. Participants can experience first-hand how to read data, perform simple exploratory data analytics, conduct a simple data pre-processing, and create a simple plot to analyse a data.

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

Data Mining and Data Pre-processing plays an important role in the success of all data analytics project. In this module, participants will dive deep into the various Data pre-processing and data-mining techniques.

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

Understanding the various different techniques and its application enhances a Data Analyst ability to better understand and capture hidden insights from vast volume of data. This module aimed to provide participants with the first-hand experience on how various techniques are being utilized.

Topics Covered

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

Module 7
Deep Dive into Data Visualization with Python

Acquire essential knowledge on the importance of data visualization and how different visual elements can be utilized to enhance and better understand trends, outliers, and patterns in data.

Topics Covered

  • Data Visualization and Data Exploration
  • Comparison Plots (Line Chart, Bar Chart, Radar Chart)
  • Relation Plots (Scatter Plot, Bubble Plot, Correlogram, Heatmap)
  • Composition Plots (Pie Chart, Stacked Bar Chart, Stacked Area Chart)
  • Distribution Plots (Histogram, Density Plot, Box Plot, Violin Plot)
  • Geo Plots (Dot Map, Choropleth Map, Connection Map)
  • Enhancement of Visualization

Module 8
Matplotlib and Seaborn Libraries in Python for Visualization

Matplotlib and Seaborn libraries are two of the most widely utilized libraries in Python for visualization. Learn the key components and parameters of Matplotlib and Seaborn to enhance your visualization.

Topics Covered

  • Deep Dive into Matplotlib in Python
  • Essential Matplotlib Components for Plotting
  • Basic Plots, Layouts, Images, and Mathematical Expressions using Matplotlib
  • Introduction to Seaborn in Python
  • Kernel Density Estimation, Bivariate Distribution, Pairwise Relationships
  • Multi-plots, Regression Plots, Squarify, and Geospatial Plotting using Seaborn

Module 9
Understanding Machine Learning

Automate your data analytics model building by leveraging on the prowess of Machine learning methods. In this module, participants will learn how to expand beyond the conventional data analytics process to leverage on Machine Learning methods to analyse and predict trends.

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, Validating)

Data Analytics Essentials (DAE) involves extensive hands-on exercises / practical, rigorous usage of real-time case studies, hands-on exercises and group discussion

Some Reasons Why Learners Choose CASUGOL

  • International Certification Body

  • Presence in 38 Countries

  • Developed by Industry Experts

  • More than 42,000 professionals passed through our education system

  • Flexible program design for all individuals

  • Learn from internationally renowned leading industry experts, academics, and researchers

  • Support for participants during and after training

  • Enhance competency of workforce and improve individual career prospect

  • Customization of programs for specific industry, organization, government agencies, statutory boards

  • Learn in a highly interactive, supportive and encouraging environment

  • Regular invitation to attend courses / workshops / seminars / events at complimentary rate

Certificate Verification

All certificates issued by CASUGOL are to individuals who have successfully completed CASUGOL Certification Programs / Executive Workshops and have fulfilled all requirements by demonstrating proficiency in applying the knowledge and skills acquired.

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