Data Analytics Essentials (DAE)

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

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

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)

[Online] Data Analytics Essentials (DAE)

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