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Analytics, Data Science and A I: Systems for Decision Support

Eleventh Edition

Chapter 4

Data Mining Process, Methods, and Algorithms

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
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Learning Objectives (1 of 2)
4.1 Define data mining as an enabling technology for business analytics
4.2 Understand the objectives and benefits of data mining
4.3 Become familiar with the wide range of applications of data mining
4.4 Learn the standardized data mining processes
4.5 Learn different methods and algorithms of data mining

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide 2 is list of textbook LO numbers and statements
2

Learning Objectives (2 of 2)
4.6 Build awareness of the existing data mining software tools
4.7 Understand the privacy issues, pitfalls, and myths of data mining

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide 2 is list of textbook LO numbers and statements
3

Opening Vignette (1 of 3)
Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime
Predictive analytics in law enforcement
Policing with less
New thinking on cold cases
The big picture starts small
Success brings credibility
Just for the facts
Safer streets for smarter cities

Opening Vignette (2 of 3)
Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime
Discussion Questions
Why do law enforcement agencies and departments like Miami-Dade Police Department embrace advanced analytics and data mining?
What are the top challenges for law enforcement agencies and departments like Miami-Dade Police Department? Can you think of other challenges (not mentioned in this case) that can benefit from data mining?

Opening Vignette (3 of 3)
Miami-Dade Police Department Is Using Predictive Analytics to Foresee and Fight Crime
Discussion Questions (continued)
What are the sources of data that law enforcement agencies and departments like Miami-Dade Police Department use for their predictive modeling and data mining projects?
What type of analytics do law enforcement agencies and departments like Miami-Dade Police Department use to fight crime?
What does “the big picture starts small” mean in this case? Explain.

Data Mining Concepts and Definitions Why Data Mining?
More intense competition at the global scale.
Recognition of the value in data sources.
Availability of quality data on customers, vendors, transactions, Web, etc.
Consolidation and integration of data repositories into data warehouses.
The exponential increase in data processing and storage capabilities; and decrease in cost.
Movement toward conversion of information resources into nonphysical form.

Definition of Data Mining
The nontrivial process of identifying valid, novel, potentially useful, and ultimately understandable patterns in data stored in structured databases. — Fayyad et al., (1996)
Keywords in this definition: Process, nontrivial, valid, novel, potentially useful, understandable.
Data mining: a misnomer?
Other names: knowledge extraction, pattern analysis, knowledge discovery, information harvesting, pattern searching, data dredging,…

Data Mining Is a Blend of Multiple Disciplines
Figure 4.1 Data Mining Is a Blend of Multiple Disciplines.

Application Case 4.1
Visa Is Enhancing the Customer Experience While Reducing Fraud with Predictive Analytics and Data Mining
Questions for Discussion:
What challenges were Visa and the rest of the credit card industry facing?
How did Visa improve customer service while also improving retention of fraud?
What is in-memory analytics, and why was it necessary?

Data Mining Characteristics & Objectives
Source of data for D M is often a consolidated data warehouse (not always!).
D M environment is usually a client-server or a Web-based information systems architecture.
Data is the most critical ingredient for D M which may include soft/unstructured data.
The miner is often an end user
Striking it rich requires creative thinking
Data mining tools’ capabilities and ease of use are essential (Web, parallel processing, etc.)

How Data Mining Works
D M extract patterns from data
Pattern? A mathematical (numeric and/or symbolic) relationship among data items
Types of patterns
Association
Prediction
Cluster (segmentation)
Sequential (or time series) relationships

Application Case 4.2
American Honda Uses Advanced Analytics to Improve Warranty Claims
Questions for Discussion:
How does American Honda use analytics to improve warranty claims?
In addition to warranty claims, for what other purposes does American Honda use advanced analytics methods?
Can you think of other uses of advanced analytics in the automotive industry? You can search the Web to find some answers to this question.

A Taxonomy for Data Mining
Figure 4.2 A Simple Taxonomy for Data Mining Tasks, Methods, and Algorithms.

Other Data Mining Patterns/Tasks
Time-series forecasting
Part of the sequence or link analysis?
Visualization
Another data mining task?
Covered in Chapter 3
Data Mining versus Statistics
Are they the same?
What is the relationship between the two?

Data Mining Applications (1 of 4)
Customer Relationship Management
Maximize return on marketing campaigns
Improve customer retention (churn analysis)
Maximize customer value (cross-, up-selling)
Identify and treat most valued customers
Banking & Other Financial
Automate the loan application process
Detecting fraudulent transactions
Maximize customer value (cross-, up-selling)
Optimizing cash reserves with forecasting

Data Mining Applications (2 of 4)
Retailing and Logistics
Optimize inventory levels at different locations
Improve the store layout and sales promotions
Optimize logistics by predicting seasonal effects
Minimize losses due to limited shelf life
Manufacturing and Maintenance
Predict/prevent machinery failures
Identify anomalies in production systems to optimize the use manufacturing capacity
Discover novel patterns to improve product quality

Data Mining Applications (3 of 4)
Brokerage and Securities Trading
Predict changes on certain bond prices
Forecast the direction of stock fluctuations
Assess the effect of events on market movements
Identify and prevent fraudulent activities in trading
Insurance
Forecast claim costs for better business planning
Determine optimal rate plans
Optimize marketing to specific customers
Identify and prevent fraudulent claim activities

Data Mining Applications (4 of 4)
Computer hardware and software
Science and engineering
Government and defense
Homeland security and law enforcement
Travel, entertainment, sports
Healthcare and medicine
Sports,… virtually everywhere…

Application Case 4.3
Predictive Analytic and Data Mining Help Stop Terrorist Funding
Questions for Discussion:
How can data mining be used to fight terrorism? Comment on what else can be done beyond what is covered in this short application case.
Do you think data mining, although essential for fighting terrorist cells, also jeopardizes individuals’ rights of privacy?

Data Mining Process
A manifestation of the best practices
A systematic way to conduct D M projects
Moving from Art to Science for D M project
Everybody has a different version
Most common standard processes:
C R I S P-D M (Cross-Industry Standard Process for Data Mining)
S E M M A (Sample, Explore, Modify, Model, and Assess)
K D D (Knowledge Discovery in Databases)

Data Mining Process: C R I S P-D M (1 of 2)
Cross Industry Standard Process for Data Mining
Proposed in 1990s by a European consortium
Composed of six consecutive steps
Step 1: Business Understanding
Step 2: Data Understanding
Step 3: Data Preparation

Step 4: Model Building
Step 5: Testing and Evaluation
Step 6: Deployment

Data Mining Process: C R I S P-D M (2 of 2)
Figure 4.3 The Six-Step C R I S P-D M Data Mining Process. 
The process is highly repetitive and experimental (D M: art versus science?)

Data Mining Process: S E M M A
Figure 4.5 S E M M A Data Mining Process.
Developed by S A S Institute

Data Mining Process: K D D
Figure 4.6 K D D (Knowledge Discovery in Databases) Process.

Which Data Mining Process is the Best?
Figure 4.7 Ranking of Data Mining Methodologies/Processes.
Source: Used with permission from KDnuggets.com.

Application Case 4.4
Data Mining Helps in Cancer Research
Questions for Discussion
How can data mining be used for ultimately curing illnesses like cancer?
What do you think are the promises and major challenges for data miners in contributing to medical and biological research endeavors?

Data Mining Methods: Classification
Most frequently used D M method
Part of the machine-learning family
Employ supervised learning
Learn from past data, classify new data
The output variable is categorical (nominal or ordinal) in nature
Classification versus regression?
Classification versus clustering?

Assessment Methods for Classification
Predictive accuracy
Hit rate
Speed
Model building versus predicting/usage speed
Robustness
Scalability
Interpretability
Transparency, explainability

Accuracy of Classification Models
In classification problems, the primary source for accuracy estimation is the confusion matrix

Estimation Methodologies for Classification: Single/Simple Split
Simple split (or holdout or test sample estimation)
Split the data into 2 mutually exclusive sets: training (~70%) and testing (30%)
For Neural Networks, the data is split into three sub-sets (training [~60%], validation [~20%], testing [~20%])

Estimation Methodologies for Classification: k-Fold Cross Validation
Data is split into k mutual subsets and k number training/testing experiments are conducted
Figure 4.10 A Graphical Depiction of k-Fold Cross-Validation.

Additional Estimation Methodologies for Classification
Leave-one-out
Similar to k-fold where k = number of samples
Bootstrapping
Random sampling with replacement
Jackknifing
Similar to leave-one-out
Area Under the R O C Curve (A U C)
R O C: receiver operating characteristics (a term borrowed from radar image processing)

Area Under the R O C Curve (A U C) (1 of 2)
Works with binary classification
Figure 4.11 A Sample R O C Curve.

Area Under the R O C Curve (A U C) (2 of 2)
Produces values from 0 to 1.0
Random chance is 0.5 and perfect classification is 1.0
Produces good a assessment for skewed class distributions too!

Classification Techniques
Decision tree analysis
Statistical analysis
Neural networks
Support vector machines
Case-based reasoning
Bayesian classifiers
Genetic algorithms
Rough sets

Decision Trees (1 of 2)
Employs a divide-and-conquer method
Recursively divides a training set until each division consists of examples from one class:
A general algorithm (steps) for building a decision tree
Create a root node and assign all of the training data to it.
Select the best splitting attribute.
Add a branch to the root node for each value of the split. Split the data into mutually exclusive subsets along the lines of the specific split.
Repeat the steps 2 and 3 for each and every leaf node until the stopping criteria is reached.

Decision Trees (2 of 2)
D T algorithms mainly differ on
Splitting criteria
Which variable, what value, etc.
Stopping criteria
When to stop building the tree
Pruning (generalization method)
Pre-pruning versus post-pruning
Most popular D T algorithms include
I D3, C4.5, C5; C A R T; C H A I D; M5

Ensemble Models for Predictive Analytics
Produces more robust and reliable prediction models
Figure 4.12 Graphical Illustration of a Heterogeneous Ensemble.

Application Case 4.5
Influence Health Uses Advanced Predictive Analytics to Focus on the Factors That Really Influence People’s Healthcare Decisions
Questions for Discussion:
What did Influence Health do?
What were the challenges, the proposed solutions, and the obtained results?
How can data mining help companies in the healthcare industry (in ways other than the ones mentioned in this case)?

Cluster Analysis for Data Mining (1 of 4)
Used for automatic identification of natural groupings of things
Part of the machine-learning family
Employ unsupervised learning
Learns the clusters of things from past data, then assigns new instances
There is not an output/target variable
In marketing, it is also known as segmentation

Cluster Analysis for Data Mining (2 of 4)
Clustering results may be used to
Identify natural groupings of customers
Identify rules for assigning new cases to classes for targeting/diagnostic purposes
Provide characterization, definition, labeling of populations
Decrease the size and complexity of problems for other data mining methods
Identify outliers in a specific domain (e.g., rare-event detection)

Cluster Analysis for Data Mining (3 of 4)
Analysis methods
Statistical methods (including both hierarchical and nonhierarchical), such as k-means, k-modes, and so on.
Neural networks (adaptive resonance theory [A R T], self-organizing map [S O M])
Fuzzy logic (e.g., fuzzy c-means algorithm)
Genetic algorithms
How many clusters?

Cluster Analysis for Data Mining (4 of 4)
k-Means Clustering Algorithm
k: pre-determined number of clusters
Algorithm (Step 0: determine value of k)
Step 1: Randomly generate k random points as initial cluster centers.
Step 2: Assign each point to the nearest cluster center.
Step 3: Re-compute the new cluster centers.
Repetition step: Repeat steps 3 and 4 until some convergence criterion is met (usually that the assignment of points to clusters becomes stable).

Cluster Analysis for Data Mining –
k-Means Clustering Algorithm
Figure 4.13 A Graphical Illustration of the Steps in the k-Means Algorithm.

Association Rule Mining (1 of 6)
A very popular D M method in business
Finds interesting relationships (affinities) between variables (items or events)
Part of machine learning family
Employs unsupervised learning
There is no output variable
Also known as market basket analysis
Often used as an example to describe D M to ordinary people, such as the famous “relationship between diapers and beers!”

Association Rule Mining (2 of 6)
Input: the simple point-of-sale transaction data
Output: Most frequent affinities among items
Example: according to the transaction data…
“Customer who bought a lap-top computer and a virus protection software, also bought extended service plan 70 percent of the time.”
How do you use such a pattern/knowledge?
Put the items next to each other
Promote the items as a package
Place items far apart from each other!

Association Rule Mining (3 of 6)
A representative applications of association rule mining include
In business: cross-marketing, cross-selling, store design, catalog design, e-commerce site design, optimization of online advertising, product pricing, and sales/promotion configuration
In medicine: relationships between symptoms and illnesses; diagnosis and patient characteristics and treatments (to be used in medical D S S); and genes and their functions (to be used in genomics projects)
…

Association Rule Mining (4 of 6)
Are all association rules interesting and useful?
A Generic Rule: X  Y [S%, C%]
X, Y: products and/or services
X: Left-hand-side (L H S)
Y: Right-hand-side (R H S)
S: Support: how often X and Y go together
C: Confidence: how often Y go together with the X
Example: {Laptop Computer, Antivirus Software}  {Extended Service Plan} [30%, 70%]

Association Rule Mining (5 of 6)
Several algorithms are developed for discovering (identifying) association rules
Apriori
Eclat
F P-Growth
+ Derivatives and hybrids of the three
The algorithms help identify the frequent item sets, which are, then converted to association rules

Association Rule Mining (6 of 6)
Apriori Algorithm
Finds subsets that are common to at least a minimum number of the itemsets
Uses a bottom-up approach
frequent subsets are extended one item at a time (the size of frequent subsets increases from one-item subsets to two-item subsets, then three-item subsets, and so on), and
groups of candidates at each level are tested against the data for minimum support. (see the figure)  —

Association Rule Mining Apriori Algorithm
Figure 4.14 A Graphical Illustration of the Steps in the k-Means Algorithm.

Data Mining Software Tools
Figure 4.15 Popular Data Mining Software Tools (Poll Results).
Commercial
I B M S P S S Modeler (formerly Clementine)
S A S Enterprise Miner
Statistica – Dell/Statsoft
… many more
Free and/or Open Source
K N I M E
RapidMiner
Weka
R, …
Source: Used with permission from KDnuggets.com.

Application Case 4.6 (1 of 4)
Data Mining Goes to Hollywood: Predicting Financial Success of Movies
Goal: Predicting financial success of Hollywood movies before the start of their production process
How: Use of advanced predictive analytics methods.
Results: promising.

Application Case 4.6 (2 of 4)
Data Mining Goes to Hollywood: Predicting Financial Success of Movies
A Typical Classification Problem
Table 4.3 Movie Classification based on Receipts
Class No. 1 2 3 4 5 6 7 8 9
Range (in millions of dollars) >1 (Flop) >1 <610 >10 <20 >20 <640 >40 <665 >65 <6100 >100 <6150 >150 <6200 >200 (Blockbuster)

Table 4.4 Summary of Independent Variables
Independent Variable Number of Values Possible Values
M P A A Rating 5 G, P G, P G-13, R, N R
Competition 3 High, medium, low
Star value 3 High, medium, low
Genre 10 Sci-Fi, Historic Epic Drama, Modern Drama, Politically Related, Thriller, Horror, Comedy, Cartoon, Action, Documentary
Special effects 3 High, medium, low
Sequel 2 Yes, no
Number of screens 1 A positive integer between 1 and 3,876

Application Case 4.6 (3 of 4)
Data Mining Goes to Hollywood: Predicting Financial Success of Movies
FIGURE 4.16 Process Flow Screenshot for the Box-Office Prediction System.
The D M Process Map in I B M S P S S Modeler
Source: Reprint Courtesy of International Business Machines Corporation, © International Business Machines Corporation.

Application Case 4.6 (4 of 4)
Data Mining Goes to Hollywood: Predicting Financial Success of Movies

Data Mining Myths
Table 4.6 Data Mining Myths.
Myth Reality
Data mining provides instant, crystal-ball-like predictions. Data mining is a multistep process that requires deliberate, proactive design and use.
Data mining is not yet viable for mainstream business applications. The current state of the art is ready for almost any business type and/or size.
Data mining requires a separate, dedicated database. Because of the advances in database technology, a dedicated database is not required.
Only those with advanced degrees can do data mining. Newer Web-based tools enable managers of all educational levels to do data mining.
Data mining is only for large firms that have lots of customer data. If the data accurately reflect the business or its customers, any company can use data mining.

Data Mining Mistakes
Selecting the wrong problem for data mining
Ignoring what your sponsor thinks data mining is and what it really can/cannot do
Beginning without the end in mind.
Not leaving insufficient time for data acquisition, selection and preparation
Looking only at aggregated results and not at individual records/predictions
… 10 more mistakes… in your book

Application Case 4.7
Predicting Customer Buying Patterns – The Target Story
Questions for Discussion:
What do you think about data mining and its implication for privacy? What is the threshold between discovery of knowledge and infringement of privacy?
Did Target go too far? Did it do anything illegal? What do you think Target should have done? What do you think Target should do next (quit these types of practices)?

End of Chapter 4
Questions / Comments

Copyright
This work is protected by United States copyright laws and is provided solely for the use of instructors in teaching their courses and assessing student learning. Dissemination or sale of any part of this work (including on the World Wide Web) will destroy the integrity of the work and is not permitted. The work and materials from it should never be made available to students except by instructors using the accompanying text in their classes. All recipients of this work are expected to abide by these restrictions and to honor the intended pedagogical purposes and the needs of other instructors who rely on these materials.

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
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Analytics, Data Science and A I: Systems for Decision Support

Eleventh Edition

Chapter 3

Nature of Data, Statistical Modeling and Visualization

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide in this Presentation Contain Hyperlinks. JAWS users should be able to get a list of links by using INSERT+F77

Learning Objectives (1 of 2)
3.1 Understand the nature of data as it relates to business intelligence (B I) 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

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide 2 is list of textbook LO numbers and statements
2

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

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
Slide 2 is list of textbook LO numbers and statements
3

Opening Vignette
Attracts and Engages a New Generation of Radio Consumers with Data-Driven Marketing
What does Sirius X M do? In what type of market does it conduct its business?
What were the challenges? Comment on both technology and data-related challenges.
What were the proposed solutions?
How did they implement the proposed solutions? Did they face any implementation challenges?
What were the results and benefits? Were they worth the effort/investment?

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

The Nature of Data (2 of 2)

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

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?
X M L, H T M L, Log files, etc.
Data taxonomy…

A Simple Taxonomy of Data (2 of 2)

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:
What was the challenge Verizon was facing?
What was the data-driven solution proposed for Verizon’s business units?
What were the results?

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

The Art and Science of Data Preprocessing (2 of 2)
Data reduction
Variables
Dimensional reduction
Variable selection
2. Cases/samples
Sampling
Balancing / stratification

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 range- or 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.

Application Case 3.2 (1 of 4)
Improving Student Retention with Data-Driven Analytics
Questions for Discussion:
What is student attrition, and why is it an important problem in higher education?
What were the traditional methods to deal with the attrition problem?
List and discuss the data-related challenges within context of this case study.
What was the proposed solution? And, what were the results?

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

Application Case 3.2 (3 of 4)
Improving Student Retention with Data-Driven Analytics
Data imbalance problem

Application Case 3.2 (4 of 4)
Improving Student Retention with Data-Driven Analytics
Results…

Statistical Modeling for Business Analytics (1 of 2)

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

Descriptive Statistics Measures of Centrality Tendency (1 of 2)
Arithmetic mean

Median
The number in the middle
Mode
The most frequent observation

Descriptive Statistics Measures of Dispersion (1 of 2)
Dispersion
Degree of variation in a given variable
Range
Max – Min
Variance Standard Deviation

Mean Absolute Deviation (M A D)
Average absolute deviation from the mean

Descriptive Statistics Measures of Dispersion (2 of 2)
Quartiles
Box-and-Whiskers Plot
a.k.a. box-plot
Versatile / informative

Descriptive Statistics Measures of Centrality Tendency (2 of 2)
Histogram – frequency chart
Skewness
Measure of asymmetry

Kurtosis
Peak/tall/skinny nature of the distribution

Relationship Between Dispersion and Shape Properties

Technology Insights 3.1 – Descriptive Statistics in Excel

Technology Insights 3.1 – Descriptive Statistics in Excel Creating box-plot in Microsoft Excel

Application Case 3.3
Town of Cary Uses Analytics to Analyze Data from Sensors, Assess Demand, and Detect Problems
Questions for Discussion:
What were the challenges the Town of Cary was facing?
What was the proposed solution?
What were the results?
What other problems and data analytics solutions do you foresee for towns like Cary?

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)

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

Regression Modeling (2 of 3)

Regression Modeling (3 of 3)
x: input, y: output
Simple Linear Regression

Multiple Linear Regression

The meaning of Beta ( ) coefficients
Sign (+ or −) and magnitude

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)
M S E, M A D, R M S E

Regression Modeling Assumptions
Linearity
Independence
Normality (Normal Distribution)
Constant Variance
Multicollinearity
What happens if the assumptions do NOT hold?
What do we do then?

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)

Logistic Regression Modeling (2 of 2)

Application Case 3.4 (1 of 4)
Predicting N C A A Bowl Game Outcomes

Application Case 3.4 (2 of 4)
Predicting N C A A Bowl Game Outcomes
The analytics process to develop prediction models (both regression and classification type) for N C A A Bowl Game outcomes

Application Case 3.4 (3 of 4)
Predicting N C A A Bowl Game Outcomes
Prediction Results
Classification
Regression

Application Case 3.4 (4 of 4)
Predicting N C A A Bowl Game Outcomes
Questions for Discussion:
What are the foreseeable challenges in predicting sporting event outcomes (e.g., college bowl games)?
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)?
How successful were the prediction results? What else can they do to improve the accuracy?

Time Series Forecasting
Is it different than Simple Linear Regression? How?

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…

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 E T L)
Format: text + tables + graphs/charts
Distribution: in-print, email, portal/intranet
Data acquisition  Information generation  Decision making  Process management

Business Reporting

Types of Business Reports
Metric Management Reports
Help manage business performance through metrics (S L A s for externals; K P I s for internals)
Can be used as part of Six Sigma and/or T Q M
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

Application Case 3.5
Flood of Paper Ends at F E M A
Questions for Discussion:
What is F E M A, and what does it do?
What are the main challenges that F E M A faces?
How did F E M A improve its inefficient reporting practices?

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, …

A Brief History of Data Visualization
Data visualization can date back to the second century A D
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

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.

Decimation of Napoleon’s Army During the 1812 Russian Campaign
By Charles Joseph Minard
Arguably the most popular multi-dimensional chart

Application Case 3.6
Macfarlan Smith Improves Operational Performance Insight with Tableau Online
Questions for Discussion:
What were the data and reporting related challenges Macfarlan Smith facing?
What was the solution and the obtained results and/or benefits?

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.

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.

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.

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
S A P launched Visual Intelligence
S A S launched Visual Analytics
Microsoft bolstered PowerPivot with Power View
I B M launched Cognos Insight
Oracle acquired Endeca

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

Visual Analytics by S A S Institute (1 of 2)
Figure 3.25 An Overview of S A S Visual Analytics Architecture.
S A S Visual Analytics Architecture
Big data + In memory + Massively parallel processing + ..
Source: Copyright © S A S Institute, Inc. Used with permission.

Visual Analytics by S A S Institute (2 of 2)
At teradatauniversitynetwork.com, you can learn more about S A S V A, experiment with the tool
Figure 3.26 A Screenshot from S A S Visual Analytics.
Source: Copyright © S A S Institute, Inc. Used with permission.

Technology Insight 3.3 – Telling Great Stories with Data and Visualization
Figure 3.24 A Storyline Visualization in Tableau Software.
Source: Used with permission from Tableau Software, Inc.

Performance Dashboards (1 of 4)
Performance dashboards are commonly used in B P M software suites and B I platforms
Dashboards provide visual displays of important information that is consolidated and arranged on a single screen so that information can be digested at a single glance and easily drilled in and further explored

Performance Dashboards (2 of 4)
Figure 3.27 A Sample Executive Dashboard.
Source: A Sample Executive Dashboard from Dundas Data Visualization, Inc., www.dundas.com, reprinted with permission.

Application Case 3.7
Dallas Cowboys Score Big with Tableau and Teknion
Questions for Discussion:
How did the Dallas Cowboys use information visualization?
What were the challenge, the proposed solution, and the obtained results?

Performance Dashboards (3 of 4)
Dashboard design
The fundamental challenge of dashboard design is to display all the required information on a single screen, clearly and without distraction, in a manner that can be assimilated quickly
Three layer of information
Monitoring
Analysis
Management

Performance Dashboards (4 of 4)
What to look for in a dashboard
Use of visual components to highlight data and exceptions that require action.
Transparent to the user, meaning that they require minimal training and are extremely easy to use
Combine data from a variety of systems into a single, summarized, unified view of the business
Enable drill-down or drill-through to underlying data sources or reports
Present a dynamic, real-world view with timely data
Require little coding to implement, deploy, and maintain

Best Practices in Dashboard Design
Benchmark K P I s with Industry Standards
Wrap the Metrics with Contextual Metadata
Validate the Design by a Usability Specialist
Prioritize and Rank Alerts and Exceptions
Enrich Dashboard with Business-User Comments
Present Information in Three Different Levels
Pick the Right Visual Constructs
Provide for Guided Analytics

Application Case 3.8
Visual analytics helps energy supplier Make better connections
Questions for Discussion:
Why do you think energy supply companies are among the prime users of information visualization tools?
How did Electrabel use information visualization for the single version of the truth?
What were their challenges, the proposed solution, and the obtained results?

End of Chapter 3
Questions / Comments

Copyright © 2020, 2015, 2011 Pearson Education, Inc. All Rights Reserved
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