Duration
2 days
Overview
As the process of analyzing raw data to find trends and answer questions, the definition of data analytics captures its broad scope of the field. However, it includes many techniques with many different goals.
The data analytics process has some components that can help a variety of initiatives. By combining these components, a successful data analytics initiative will provide a clear picture of where you are, where you have been and where you should go.
Introduction
- Types of Data Analytics
- What is the Role of Data Analytics?
- Why Data Analytics is Important?
- How Data Analytics is Used in Everyday Life
- Challenges of Data Analytics and How to Fix Them
Chapter 2 – Data Analytics Lifecycle
- What are a Data Analytics Lifecyle?
- Phases of Data Analytics Lifecyle
- Data Discovery and Formation
- Data Preparation and Processing
- Design a Model
- Model Building
- Result Communication and Publication
- Measuring of Effectiveness
Chapter 3 – Data Analytics Tools
- Non-Programmatically
- Power BI
- Tableau
- Programmatically
- Python
- R
Chapter 4 – Data-Driven Decision Making (DDDM)
- What does Data-Driven Decision Making (DDDM) Mean?
- The Importance of Data-Driven Decision Making
- How to Create a Data-Driven Culture
- Challenges in a Data-Driven Project and How to Overcome Them
Chapter 5 – Data-Driven Organization (DDO)
- What is a Data-Driven Organization?
- Which Capabilities Do Organizations Need to Become a Data-Driven Organization?
- Becoming a Data-Driven Organization
Chapter 6 – AI and Data Analytics
- How is AI and Data Analytics Defined?
- The Connection Between AI and Data Analytics
- Why AI for Data Analytics?
- How AI is Used for Data Analytics?
Programme scope, delivery arrangements and suitability are confirmed during enquiry.