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Describe how DSS/BI technologies and tools can aid in each phase of decision making.Please review attachments belowNote: Using APA in discussion posts is very similar to using APA in a paper. You need to cite your sources in your discussion post both in-text and in a references section.300-400 words
sharda_11e_full_accessible_ppt_03.pptx

sharda_11e_full_accessible_ppt_04.pptx

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Analytics, Data Science and AI:
Systems for Decision Support
Eleventh Edition
Chapter 3
Nature of Data, Statistical Modeling
and Visualization
Slide in this Presentation Contain Hyperlinks.
JAWS users should be able to get a list of links
by using INSERT+F77
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Learning Objectives (1 of 2)
3.1 Understand the nature of data as it relates to business
intelligence (BI) and analytics
3.2 Learn the methods used to make real-world data
analytics ready
3.3 Describe statistical modeling and its relationship to
business analytics
3.4 Learn about descriptive and inferential statistics
3.5 Define business reporting, and understand its historical
evolution
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Learning Objectives (2 of 2)
3.6 Understand the importance of data/information
visualization
3.7 Learn different types of visualization techniques
3.8 Appreciate the value that visual analytics brings to
business analytics
3.9 Know the capabilities and limitations of dashboards
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Opening Vignette
Attracts and Engages a New Generation of
Radio Consumers with Data-Driven Marketing
1. What does Sirius XM do? In what type of market does it
conduct its business?
2. What were the challenges? Comment on both
technology and data-related challenges.
3. What were the proposed solutions?
4. How did they implement the proposed solutions? Did
they face any implementation challenges?
5. What were the results and benefits? Were they worth the
effort/investment?
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The Nature of Data (1 of 2)
• Data: a collection of facts
– usually obtained as the result of experiences,
observations, or experiments
• Data may consist of numbers, words, images, …
• Data is the lowest level of abstraction (from which
information and knowledge are derived)
• Data is the source for information and knowledge
• Data quality and data integrity → critical to analytics
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The Nature of Data (2 of 2)
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Metrics for Analytics ready Data
• Data source reliability
• Data content accuracy
• Data accessibility
• Data security and data privacy
• Data richness
• Data consistency
• Data currency/data timeliness
• Data granularity
• Data validity and data relevancy
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A Simple Taxonomy of Data (1 of 2)
• Data (datum—singular form of data): facts
• Structured data
– Targeted for computers to process
– Numeric versus nominal
• Unstructured/textual data
– Targeted for humans to process/digest
• Semi-structured data?
– XML, HTML, Log files, etc.
• Data taxonomy…
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A Simple Taxonomy of Data (2 of 2)
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Application Case 3.1
Verizon Answers the Call for Innovation: The
Nation’s Largest Network Provider uses
Advanced Analytics to Bring the Future to its
Customers
Questions for Discussion:
1. What was the challenge Verizon was facing?
2. What was the data-driven solution proposed for
Verizon’s business units?
3. What were the results?
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The Art and Science of Data
Preprocessing (1 of 2)
• The real-world data is dirty, misaligned, overly complex,
and inaccurate
– Not ready for analytics!
• Readying the data for analytics is needed
– Data preprocessing
▪ Data consolidation
▪ Data cleaning
▪ Data transformation
▪ Data reduction
• Art – it develops and improves with experience
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The Art and Science of Data
Preprocessing (2 of 2)
• Data reduction
1. Variables
– Dimensional reduction
– Variable selection
2. Cases/samples
– Sampling
– Balancing / stratification
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Data Preprocessing Tasks and Methods
Table 3.1 A Summary of Data Preprocessing Tasks and Potential Methods.
Main Task
Subtasks
Popular Methods
Data consolidation
Access and collect the data
Select and filter the data
Integrate and unify the data
SQL queries, software agents, Web services. Domain expertise, SQL queries,
statistical tests. SQL queries, domain expertise, ontology-driven data mapping.
Data cleaning
Handle missing values in the
data
Fill in missing values (imputations) with most appropriate values (mean, median,
min/max, mode, etc.); recode the missing values with a constant such as “ML”;
remove the record of the missing value; do nothing.
Blank
Identify and reduce noise in
the data
Identify the outliers in data with simple statistical techniques (such as averages and
standard deviations) or with cluster analysis; once identified, either remove the
outliers or smooth them by using binning, regression, or simple averages.
Blank
Find and eliminate erroneous
data
Identify the erroneous values in data (other than outliers), such as odd values,
inconsistent class labels, odd distributions; once identified, use domain expertise to
correct the values or remove the records holding the erroneous values.
Data transformation
Normalize the data
Reduce the range of values in each numerically valued variable to a standard range
(e.g., 0 to 1 or −1 to +1) by using a variety of normalization or scaling techniques.
Blank
Discretize or aggregate the
data
If needed, convert the numeric variables into discrete representations using rangeor frequency-based binning techniques; for categorical variables, reduce the number
of values by applying proper concept hierarchies.
Blank
Construct new attributes
Derive new and more informative variables from the existing ones using a wide
range of mathematical functions (as simple as addition and multiplication or as
complex as a hybrid combination of log transformations).
Data reduction
Reduce number of attributes
Use principal component analysis, independent component analysis, chi-square
testing, correlation analysis, and decision tree induction.
Blank
Reduce number of records
Perform random sampling, stratified sampling, expert-knowledge-driven purposeful
sampling.
Blank
Balance skewed data
Oversample the less represented or undersample the more represented classes.
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Application Case 3.2 (1 of 4)
Improving Student Retention with Data-Driven
Analytics
Questions for Discussion:
1. What is student attrition, and why is it an important
problem in higher education?
2. What were the traditional methods to deal with the
attrition problem?
3. List and discuss the data-related challenges within
context of this case study.
4. What was the proposed solution? And, what were the
results?
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Application Case 3.2 (2 of 4)
Improving Student
Retention with Data-Driven
Analytics
• Student retention
– Freshmen class
• Why it is important?
• What are the common techniques
to deal with student attrition?
• Analytics versus theoretical
approaches to student retention
problem
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Application Case 3.2 (3 of 4)
Improving Student Retention with Data-Driven
Analytics
• Data imbalance problem
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Application Case 3.2 (4 of 4)
Improving Student Retention with Data-Driven Analytics
• Results…
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Statistical Modeling for Business
Analytics (1 of 2)
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Statistical Modeling for Business
Analytics (2 of 2)
• Statistics
– A collection of mathematical techniques to
characterize and interpret data
• Descriptive Statistics
– Describing the data (as it is)
• Inferential statistics
– Drawing inferences about the population based on a
sample data
• Descriptive statistics for descriptive analytics
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Descriptive Statistics Measures of
Centrality Tendency (1 of 2)
• Arithmetic mean
x1 + x2 + … + xn
x=
n

x=
n
i =1
xi
n
• Median
– The number in the middle
• Mode
– The most frequent observation
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Descriptive Statistics Measures of
Dispersion (1 of 2)
• Dispersion
– Degree of variation in a given variable
• Range
– Max – Min
• Variance
Standard Deviation
 i=1 ( xi − x )2
n
s2 =
n −1
2
(
x

x
)
 i=1 i
n
s=
n −1
• Mean Absolute Deviation (MAD)
– Average absolute deviation from the mean
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Descriptive Statistics Measures of
Dispersion (2 of 2)
• Quartiles
• Box-and-Whiskers Plot
– a.k.a. box-plot
– Versatile / informative
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Descriptive Statistics Measures of
Centrality Tendency (2 of 2)
• Histogram – frequency chart
• Skewness
– Measure of asymmetry

skewness = s =
n
3
(
x

x
)
i
i =1
(n − 1) s 3
• Kurtosis
– Peak/tall/skinny nature of the distribution

kurtosis = K =
n
4
(
x

x
)
i
i =1
ns
4
−3
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Relationship Between Dispersion
and Shape Properties
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Technology Insights 3.1 –
Descriptive Statistics in Excel
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Technology Insights 3.1 – Descriptive
Statistics in Excel Creating box-plot
in Microsoft Excel
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Application Case 3.3
Town of Cary Uses Analytics to Analyze Data
from Sensors, Assess Demand, and Detect
Problems
Questions for Discussion:
1. What were the challenges the Town of Cary was facing?
2. What was the proposed solution?
3. What were the results?
4. What other problems and data analytics solutions do you
foresee for towns like Cary?
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Regression Modeling for Inferential
Statistics
• Regression
– A part of inferential statistics
– The most widely known and used analytics technique
in statistics
– Used to characterize relationship between explanatory
(input) and response (output) variable
• It can be used for
– Hypothesis testing (explanation)
– Forecasting (prediction)
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Regression Modeling (1 of 3)
• Correlation versus Regression
– What is the difference (or relationship)?
• Simple Regression versus Multiple Regression
– Base on number of input variables
• How do we develop linear regression models?
– Scatter plots (visualization—for simple regression)
– Ordinary least squares method
▪ A line that minimizes squared of the errors
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Regression Modeling (2 of 3)
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Regression Modeling (3 of 3)
• x: input, y: output
• Simple Linear Regression
y =  0 + 1 x
• Multiple Linear Regression
y =  0 + 1 x1 +  2 x2 +  3 x3 + … +  n xn
• The meaning of Beta ( ) coefficients
– Sign (+ or −) and magnitude
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Process of Developing a
Regression Model
How do we know if the model is good
enough?
– R2 (R-Square)
– p Values
– Error measures (for prediction
problems)
▪ MSE, MAD, RMSE
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Regression Modeling Assumptions
• Linearity
• Independence
• Normality (Normal Distribution)
• Constant Variance
• Multicollinearity
• What happens if the assumptions do NOT hold?
– What do we do then?
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Logistic Regression Modeling (1 of 2)
• A very popular statistics-based classification algorithm
• Employs supervised learning
• Developed in 1940s
• The difference between Linear Regression and Logistic
Regression
– In Logistic Regression Output/Target variable is a
binomial (binary classification) variable (as supposed
to numeric variable)
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Logistic Regression Modeling (2 of 2)
f ( y) =
1
1 + e − ( 0 + 1 x )
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Application Case 3.4 (1 of 4)
Predicting NCA A Bowl Game Outcomes
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Application
Case 3.4 (2 of 4)
Predicting NCA A
Bowl Game
Outcomes
• The analytics
process to develop
prediction models
(both regression and
classification type)
for NCA A Bowl
Game outcomes
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Application Case 3.4 (3 of 4)
Predicting NCA A Bowl Game Outcomes
Prediction Results
1. Classification
2. Regression
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Application Case 3.4 (4 of 4)
Predicting NCA A Bowl Game Outcomes
Questions for Discussion:
1. What are the foreseeable challenges in predicting
sporting event outcomes (e.g., college bowl games)?
2. How did the researchers formulate/design the prediction
problem (i.e., what were the inputs and output, and what
was the representation of a single sample—row of data)?
3. How successful were the prediction results? What else
can they do to improve the accuracy?
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Time Series Forecasting
• Is it different than Simple Linear Regression? How?
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Business Reporting Definitions and
Concepts
• Report = Information → Decision
• Report?
– Any communication artifact prepared to convey
specific information
• A report can fulfill many functions
– To ensure proper departmental functioning
– To provide information
– To provide the results of an analysis
– To persuade others to act
– To create an organizational memory…
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What is a Business Report?
• A written document that contains information regarding
business matters.
• Purpose: to improve managerial decisions
• Source: data from inside and outside the organization (via
the use of ETL)
• Format: text + tables + graphs/charts
• Distribution: in-print, email, portal/intranet
Data acquisition → Information generation → Decision
making → Process management
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Business Reporting
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Types of Business Reports
• Metric Management Reports
– Help manage business performance through metrics
(SL As for externals; KPIs for internals)
– Can be used as part of Six Sigma and/or TQM
• Dashboard-Type Reports
– Graphical presentation of several performance
indicators in a single page using dials/gauges
• Balanced Scorecard–Type Reports
– Include financial, customer, business process, and
learning & growth indicators
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Application Case 3.5
Flood of Paper Ends at F E MA
Questions for Discussion:
1. What is FEMA, and what does it do?
2. What are the main challenges that FEMA faces?
3. How did FEMA improve its inefficient reporting practices?
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Data Visualization
“The use of visual representations to explore, make sense
of, and communicate data.”
• Data visualization vs. Information visualization
• Information = aggregation, summarization, and
contextualization of data
• Related to information graphics, scientific visualization,
and statistical graphics
• Often includes charts, graphs, illustrations, …
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A Brief History of Data Visualization
• Data visualization can date back to the second century
AD
• Most developments have occurred in the last two and a
half centuries
• Until recently it was not recognized as a discipline
• Today’s most popular visual forms date back a few
centuries
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The First Pie Chart Created by William Playfair in 1801
William Playfair is widely credited as the inventor of the
modern chart, having created the first line and pie charts.
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Decimation of Napoleon’s Army
During the 1812 Russian Campaign
By Charles Joseph Minard
• Arguably the most popular multi-dimensional chart
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Application Case 3.6
Macfarlan Smith Improves Operational
Performance Insight with Tableau Online
Questions for Discussion:
1. What were the data and reporting related challenges
Macfarlan Smith facing?
2. What was the solution and the obtained results and/or
benefits?
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Which Chart or Graph Should You Use?
Figure 3.21 A Taxonomy of Charts and Graphs.
Source: Adapted from Abela, A. (2008). Advanced Presentations by Design: Creating
Communication That Drives Action. New York: Wiley.
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An Example Gapminder Chart:
Wealth and Health of Nations
Figure 3.22 A Gapminder Chart That Shows the Wealth and Health of Nations.
Source: gapminder.org.
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The Emergence of Data Visualization
And Visual Analytics (1 of 2)
Figure 3.23 Magic Quadrant for Business Intelligence and Analytics Platforms.

Magic Quadrant for Business
Intelligence and Analytics
Platforms (Source: Gartner.com)

Many data visualization
companies are in the 4th quadrant

There is a move towards
visualization
Source: Used with permission from Gartner Inc.
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The Emergence of Data Visualization
And Visual Analytics (2 of 2)
• Emergence of new companies
– Tableau, Spotfire, QlikView, …
• Increased focus by the big players
– MicroStrategy improved Visual Insight
– SAP launched Visual Intelligence
– SAS launched Visual Analytics
– Microsoft bolstered PowerPivot with Power View
– IBM launched Cognos Insight
– Oracle acquired Endeca
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Visual Analytics
• A recently coined term
– Information visualization + predictive analytics
• Information visualization
– Descriptive, backward focused
– “what happened” “what is happening”
• Predictive analytics
– Predictive, future focused
– “what will happen” “why will it happen”
• There is a strong move toward visual analytics
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Visual Analytics by SAS Institute
(1 of 2)
Figure 3.25 An Overview …
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