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Suitable for everyone. Learn in an Interactive, Supportive and Encouraging Environment. 80% Hands-On / 20% Theory

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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

Pre-Requisite

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

Examination

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