JAWAHARLAL NEHRU TECHNOLOGICAL UNIVERSITY HYDERABAD

22BBA604: DATA VISUALIZATION

BBA (Data Analytics) VI Semester (R22)

Course Objectives:

To understand the purpose of data Visualization.
To introduce the methods of data handling and data models.
To introduce the methods of data manipulation for decision making.
To present the data in a compelling manner using the Tableau.
To understand the methods used to analyze data for strategic decision making. Learning outcome: Students will be able to
To design compelling data visualizations for insights for solving business problem.
Get familiarize with data handling methods and models.
To apply the data manipulation techniques for decision making.
To able to present the data in a compelling manner using the Tableau.
To able to conduct analysis on the data for strategic decision making.

UNIT - I: Effective Communication of Quantitative Information

Purpose, Scope, Communication Style, Quantitative relationships, differing roles of tables and graphs Mechanics of Sight, applying visual attributes to design, Gestalts principle, Fundamental variations of tables, Fundamental variations of graphs General Design principles for Communication Organizing, Highlighting, Integration, Table design, General graph design, Component level graph design, multi- Variable display.

UNIT - II: Introduction to Data Handling

Overview of Data Analysis, Introduction to Data visualization, Working with Logical and financial functions, Data Validation & data models, Power Map for visualizing data, Power BI-Business Intelligence, Dashboard designing.

UNIT - III: Introduction to Data Manipulation

Heat Map, Tree Map, Smart Chart, Column Chart, Line Chart , Pi, Bar, Area, Scatter Chart, Data Series, Axes, Chart Sheet, Trendline, Error Bars, Sparklines, Combination Chart, Gauge, Thermometer Chart , Gantt Chart, Pareto Chart etc., Frequency Distribution, Pivot Chart, Slicers, Tables: Structured References, Table Styles, What-If Analysis: Data Tables, Goal Seek, Sensitivity Analysis, Histogram, Descriptive, Statistics, Anova, F-Test, t-Test, Moving, Average, Exponential Smoothing, Correlation model, Regression model.

UNIT - IV: Getting Started with Tableau

Tableau-Tableau product suite, Working, Tableau Architecture, Tableau Repository, Connecting to Data & Introduction to data source concepts, Understanding the Tableau workspace, Dimensions and Measures, Data Types & Default Properties, Building basic views.

UNIT - V: Data Strategy

Understanding Product & Category, Competitive Analysis, Market Share understanding- Market potential Index, Seasonality Sales Trending, Consumer behavior Analytics-MIND AND MARKET FACTORS, Budget planning & Execution- MIMI, Regression & Correlation Analysis for Sales trending, Forecasting method with predictive investment modelling, Cohort Analysis, Google Analytics (GA).