This document provides an introduction to data visualization and its applications. It discusses different types of data sources, how data is processed through steps like preprocessing, wrangling and preparation. It also covers topics like the difference between data analytics and business analytics, common roles in data analysis, tools used like SQL and Power BI, example datasets, and a case study on analyzing global store data. The document concludes with an overview of the Power BI architecture.

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Introduction to Data Visualization and its Applications
B.Bhuvaneswaran
28 September 2022

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Data?
 Industries
 E-Commerce
 Retailers
 Social Media
 Food (Online/Offline)
 Email/Photos/Videos
 Calling

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Data

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Structured Data vs Unstructured Data

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Structured Data vs Unstructured Data

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Data Processing
 Data Preprocessing is a technique that is used to convert the raw
data into a clean data set.
 In other words, whenever the data is gathered from different
sources it is collected in raw format which is not feasible for the
analysis.

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Data Wrangling
 Data Wrangling is a technique that is executed at the time of
making an interactive model.
 In other words, it is used to convert the raw data into a format
that is convenient for the consumption of data.

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Data Preparation
 Data Cleaning
 Data Integration
 Data Transformation
 Data Reduction

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Data Wrangling
 Discovering
 Structuring
 Cleaning
 Validating
 Publishing

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Dashboard and Reporting

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Data Analytics vs Business Analytics
 Business Analytics is specific to business-related problems like
cost, profit, etc.
 Data Analytics answers questions like the influence of geography,
seasonal factors, and customer preferences on the business.
 Business analytics is the analysis of company data with statistical
concepts to get solutions and insights.
 Data Analytics combines data with algorithm building like adding
Visualization Insights and technology to draw the answers to a
range of questions.

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Process of Data Analysis

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Roles

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Roles
 Data Analyst
 Business Data Analyst
 Data Analytics Specialist
 Data Visualization Specialist
 Product Data Analyst
 Marketing Analyst
 Healthcare Data Analyst
 Financial Analyst
 Digital Marketing Analyst
 HR Analyst
 Customer Insights Analyst
 Web Analyst
 CRM Data Analyst
 Analytics Manager
 Business Intelligence Analyst

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Tools
 SQL
 Tableau Public
 Power BI Desktop

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Datasets
 Kaggle
 Google Public Datasets
 healthdata.gov
 WHO - Covid Data

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Case Study-Global Store Data Analysis
 Importing the dataset
 Data Manipulating and Joining
 Data Visualization, Geo Maps and Complex Visuals
 Decision making and indepth analysis

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Power BI Architecture
 Data Integration
 Data Transforming
 Report & Publish
 Creating and Dashboard

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Power BI Architecture
 Power BI Architecture
 Power BI Desktop - Visualization
 Power BI Editor - Structure and Exploration of the data
 Power BI Service - Sharing the dashboards online, within a team

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Power BI Architecture

21. Queries…?

22. Thank You…!