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DVLAB

 

VNR VIGNANA JYOTHI INSTITUTE OF ENGINEERING AND TECHNOLOGY

(22SD5DS201) DATA VISUALIZATION

TEACHING SCHEME

 

 

 

EVALUATION SCHEME

L

T/P

C

 

 

 

D-D

PE

LR

CP

SEE

TOTAL

0

2

1

 

 

 

10

10

10

10

60

100

 

COURSE OBJECTIVES:

·         To install and run the R studio for data analysis.

·         To understand principles and techniques for data visualization.

·         To visualize the data that can improve comprehension, communication, and decision making.

·         To implement various tools and methods for easy interpretation of data.

COURSE OUTCOMES:      After completion of the course, the student should be able to

CO-1: Understand the importance of data visualization in analytics.
CO-2: Understand the principles of data visualization.

CO-3: To apply the principles of data visualization on toy datasets using R.

CO-4: To analyze data towards decision making using visualization.

CO-5: Identify appropriate/suitable visualization for particular requirements imposed by the data type and analytics algorithms

COURSE ARTICULATION MATRIX:

CO

PROGRAM OUTCOMES (PO)

PROGRAM SPECIFIC OUTCOMES (PSO)

 

PO-1

PO-2

PO-3

PO-4

PO-5

PO-6

PO-7

PO-8

PO-9

  PO=10

PO-11

PO-12

   PSO-1

  PSO-2

PSO-3

PSO-4

CO-1

2

2

2

3

3

-

-

-

-

-

-

2

3

-2

-

3

CO-2

1

1

2

2

3

-

-

-

-

-

-

1

2

2

-

2

CO-3

2

2

1

1

3

-

-

-

-

-

1

1

3

2

2

3

CO-4

2

2

1

1

2

-

-

-

-

-

1

1

2

3

3

2

CO-5

1

1

2

2

1

-

-

-

-

-

2

1

1

2

2

2

 

Exercise 1:  Basics

Introduction to basic components of R programming, overview of visualization, data types, basics of plotting graphs, different types of graphs in analytics

Exercise 2: Importance of visualizations

Principles of communicating data, Principles of encoding data to make visualizations, Importance of color in visualizations

Exercise 3:  Plots using basic R

Exercise 3.1:  Plots with one categorical, continuous variable

Functions is R for plotting, plots with one categorical variable, plots with one continuous variable, plots with one categorical and one continuous variable

Exercise 3.2:  Plots with 2 continuous variables

Plots with two continuous variables, controlling various aesthetics of the graph.

Exercise 4: ggplot2 in R

Group manipulation and data reshaping in R, understanding the philosophy of ggplot2, bar plot, pie chart, histogram, boxplot, scatter plotand regression plots

Exercise 5: ggplot2 in R

Controlling aesthetics like colour, size, legend and facets.

 Exercise 6: Data Visualization

Importing the data, Dimensions and measures, color code for various types of variables

Exercise 7: Working with sheets

Understanding the worksheet, row and column shelves, showme card, filter and pages shelf

 Exercise 8: Calculations    

Working with different measures, creating new calculated fields, Quick table calculations, parameters and groups

Exercise 9: Graphs for Analytics

Calculate Proportions and percentages, Comparing current to historical and Actual to Target

Exercise 10: Normal Distribution Variation

Calculate Mean and Median – Normal DistributionVariation and Uncertainty

Exercise 11: Reporting and Visualizing variation

Reporting and Visualizing variation, Control Charts Multiple Quantities – Scatter Plots, Stacked bars, Regressions and trend lines

Exercise 12: Depicting changes over time

Depicting changes over time, Line Chart, Dual Axis Line Chart, scatterplot

Exercise 13:Reporting

Introduction to dashboard, use of filters in dashboard, Imbedding pictures, Insert live webpage, story

TEXT BOOKS:       

1. Microsoft Power BI, Marco Russo.

2. R for everyone, Jarad P Lander

3. Statistics : An Introduction using R Michael J Crawley

 

REFERENCES:      

1. Think Python, Allen Downey, Green Tea Press

2. Core Python Programming, W.Chun, Pearson

3. Introduction to Python, Kenneth A. Lambert, Cengage.

 

ONLINE RESOURCES:

1.     https://www.tableau.com/learn

2.     https://tableauacademy.substack.com/p/tableau-training-and-learning-2021

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