Nov 30, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Human resource  Source

[ NEWS BYTES]

>>
 Texas insurers to cover advanced breast cancer screenings – Texas Tribune Under  Health Analytics

>>
 Google Cloud adds firewall to App Engine | ZDNet – ZDNet Under  Cloud

>>
 East Africa: Liquid Telecom Releases IOT Report 2017 During #iotas2017 – AllAfrica.com Under  IOT

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[ FEATURED COURSE]

Applied Data Science: An Introduction

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As the world’s data grow exponentially, organizations across all sectors, including government and not-for-profit, need to understand, manage and use big, complex data sets—known as big data…. more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Strong business case could save your project
Like anything in corporate culture, the project is oftentimes about the business, not the technology. With data analysis, the same type of thinking goes. It’s not always about the technicality but about the business implications. Data science project success criteria should include project management success criteria as well. This will ensure smooth adoption, easy buy-ins, room for wins and co-operating stakeholders. So, a good data scientist should also possess some qualities of a good project manager.

[ DATA SCIENCE Q&A]

Q:What are feature vectors?
A: * n-dimensional vector of numerical features that represent some object
* term occurrences frequencies, pixels of an image etc.
* Feature space: vector space associated with these vectors

Source

[ VIDEO OF THE WEEK]

Data-As-A-Service (#DAAS) to enable compliance reporting

 Data-As-A-Service (#DAAS) to enable compliance reporting

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[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. – Geoffrey Moore

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

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[ FACT OF THE WEEK]

Retailers who leverage the full power of big data could increase their operating margins by as much as 60%.

Sourced from: Analytics.CLUB #WEB Newsletter

Simplifying Loyalty Driver Analysis

Customer Experience Management (CEM) programs use customer feedback data to help understand and improve the quality of the customer relationship. In their attempts to improve systemic problems, companies use these data to identify where customer experience improvement efforts will have the greatest return on investment (ROI). Facing a tidal wave of customer feedback data, how do companies make sense of the data deluge? They rely on Loyalty Driver Analysis, a business intelligence solution that distills the feedback data into meaningful information. This method provides most of the insight you need to direct your customer experience improvement efforts to business areas (e.g., product, service, account management, marketing) that matter most to your customers.

The Survey Data

Let’s say our customer experience management program collects customer feedback using a customer relationship survey that measures satisfaction with the customer experience and customer loyalty. Specifically, these measures are:

  1. Satisfaction with the customer experience for each of seven (7) business areas: Measures that assess the quality of the customer experience. I focus on these seven customer experience areas: 1) Ease of Doing Business, 2) Account Management, 3) Overall Product Quality, 4) Customer Service, 5) Technical Support, 6) Communications from the Company and 7) Future Product/Company Direction. Using a 0 (Extremely Dissatisfied) to 10 (Extremely Satisfied) scale, higher ratings indicate a better customer experience (higher satisfaction).
  2. Customer Loyalty: Measures that assess the likelihood of engaging in different types of loyalty behaviors. I use three measures of customer loyalty: 1) Advocacy Loyalty, 2) Purchasing Loyalty and 3) Retention Loyalty. Using a 0 (Not at all likely) to 10 (Extremely likely) scale, higher ratings indicate higher levels of customer loyalty.

Summarizing the Data

You need to understand only two things about each of the seven business areas: 1) How well you are performing in each area and 2) How important each area is in predicting customer loyalty:

  1. Performance:  The level of performance is summarized by a summary statistic. Different approaches provide basically the same results; pick one that senior executives are familiar with and use it. Some use the mean score (sum of all responses divided by the number of respondents). Others use the “top-box” approach which is simply the percent of respondents who gave you a rating of, say, 9 or 10 (on the 0-10 scale).  So, you will calculate seven (7) performance scores, one for each business area. Low scores reflect a poor customer experience while high scores reflect good customer experience.
  2. Impact:  The impact on customer loyalty can be calculated by simply correlating the ratings of the business area with the customer loyalty ratings. This correlation is referred to as the “derived importance” of a particular business area. So, if the survey has measures of seven (7) business areas, we will calculate seven (7) correlations. The correlation between the satisfaction scores of a business area and the loyalty index indicates the degree to which performance on the business area has an impact on customer loyalty behavior. Correlations can be calculated using Excel or any statistical software package. Higher correlations (max is 1.0) indicate a strong relationship between the business area and customer loyalty (e.g., business area is important to customers). Low correlations (near 0.o) indicate a weak relationship between the business area and customer loyalty (e.g., business area is not important to customers).
Figure 1. Loyalty Driver Matrix is a Business Intelligence Solution

Graphing the Results: The Loyalty Driver Matrix

So, we now have the two pieces of information for each business area: 1) Performance and 2) Impact. Using both the performance index and derived importance for a business area, we plot these two pieces of information for each business area.

The abscissa (x-axis) of the Loyalty Driver Matrix is the performance index (e.g., mean score, top box percentage) of the business areas. The ordinate (y-axis) of the Loyalty Driver Matrix is the impact (correlation) of the business area on customer loyalty.

The resulting matrix is referred to as a Loyalty Driver Matrix (see Figure 1). By plotting all seven datapoints, we can visually examine all business areas at one time, relative to each other.

Understanding the Loyalty Driver Matrix: Making Your Business Decisions

The Loyalty Driver Matrix is divided into quadrants using the average score for each of the axes. Each of the business areas will fall into one of the four quadrants. The business decisions you make about improving the customer experience will depend on the quadrant in which each business area falls:

  1. Key Drivers: Business areas that appear in the upper left quadrant are referred to as Key Drivers. Key drivers reflect business areas that have both a high impact on loyalty and have low performance ratings relative to the other business areas. These business areas reflect good areas for potential customer experience improvement efforts because we have ample room for improvement and we know business areas are linked to customer loyalty; when these business areas are improved, you will likely see improvements in customer loyalty (attract new customers, increase purchasing behavior and retain customers).
  2. Hidden Drivers: Business areas that appear in the upper right quadrant are referred to as Hidden Drivers. Hidden drivers reflect business areas that have a high impact on loyalty and have high performance ratings relative to other business areas. These business areas reflect the company’s strengths that keep the customer base loyal. Consider using these business areas in marketing and sales collateral in order to attract new customers, increase purchasing behaviors or retain customers.
  3. Visible Drivers: Business areas that appear in the lower right quadrant are referred to as Visible Drivers. Visible drivers reflect business areas that have a low impact on loyalty and have high performance ratings relative to other business areas. These business areas reflect the company’s strengths. These areas may not impact loyalty but they are areas in which you are performing well. Consider using these business areas in marketing and sales collateral in order to attract new customers.
  4. Weak Drivers: Business areas that appear in the lower left quadrant are referred to as Weak Drivers. Weak drivers reflect business areas that have a low impact on loyalty and have low performance ratings relative to other business areas. These business areas are lowest priorities for investment. They are of low priority because, despite the fact that performance is low in these areas, these areas do not have a substantial impact on whether or not customers will be loyalty toward your product/company.

Example

Figure 2. Loyalty Driver Matrix for Software Company

A software company wanted to understand the health of their customer relationship. Using a customer relationship survey, they collected feedback from nearly 400 of their customers. Applying driver analysis to this set of data resulted in the Loyalty Driver Matrix in Figure 2. The results of this driver analysis shows that Account Management is a key driver of customer loyalty; this business area is the top candidate for potential customer experience improvement efforts; it has a large impact on advocacy loyalty AND there is room for improvement.

While the Loyalty Driver Matrix helps steer you in the right direction with respect to making improvements, you must consider the cost of making improvements. Senior management needs to balance the insights from the feedback results with the cost (labor hours, financial resources) of making improvements happen. Maximizing ROI occurs when you are able to minimize the costs while maximizing customer loyalty. Senior executives of this software company implemented product training for their Account teams. This solution was inexpensive relative to the expected gains they would see in new customer customer growth (driven by advocacy loyalty). Additionally, the company touted the ease of doing business with them as well as the quality of their products, customer service and technical support in their marketing and sales collateral to attract new customers.

Although not presented here, the company also calculated two additional driver matrices based on the results using the other two loyalty indices (purchasing loyalty and retention loyalty). These three Loyalty Driver Matrices provided the foundation for making improvements that would impact different types of customer loyalty.

Summary

Loyalty Driver Analysis is a business intelligence solution that helps companies understand and improve the health of the customer relationship. The Loyalty Driver Matrix is based on two key pieces of information: 1) Performance of the business area and 2) Impact of that business area on customer loyalty. Using these two key pieces of information for each business area, senior executives are able to make better business decisions to improve customer loyalty and accelerate business growth.

Originally Posted at: Simplifying Loyalty Driver Analysis by bobehayes

Big Data is a Big Deal (Infographic)

90% of the world’s digital data has been generated over the past two years. This data is now being used to solve issues across the globe. We take a look at big data usage throughout the United States, China, Indonesia, France, Philippines, Norway, Spain, and Brazil:

Created by Mushroom Networks

Source: Big Data is a Big Deal (Infographic)

Nov 23, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Complex data  Source

[ AnalyticsWeek BYTES]

>> Nov 23, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> December 5, 2016 Health and Biotech analytics news roundup by pstein

>> What Felix Baumgartner Space Jump Could Teach Entrepreneurs by v1shal

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[ NEWS BYTES]

>>
 Hardware Trends 2017: Complete Slides, And Some Analysis – Forbes Under  IOT

>>
 Qualcomm Launches 48-core Centriq for $1995: Arm Servers for … – AnandTech Under  Cloud

>>
 VMware working with Amazon on Hybrid Cloud Product – Financialbuzz.com Under  Hybrid Cloud

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[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:What is random forest? Why is it good?
A: Random forest? (Intuition):
– Underlying principle: several weak learners combined provide a strong learner
– Builds several decision trees on bootstrapped training samples of data
– On each tree, each time a split is considered, a random sample of m predictors is chosen as split candidates, out of all p predictors
– Rule of thumb: at each split m=?p
– Predictions: at the majority rule

Why is it good?
– Very good performance (decorrelates the features)
– Can model non-linear class boundaries
– Generalization error for free: no cross-validation needed, gives an unbiased estimate of the generalization error as the trees is built
– Generates variable importance

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @ScottZoldi, @FICO

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. – Hal Varian

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Joe DeCosmo, @Enova

Subscribe 

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[ FACT OF THE WEEK]

In the developed economies of Europe, government administrators could save more than €100 billion ($149 billion) in operational efficiency improvements alone by using big data, not including using big data to reduce fraud and errors and boost the collection of tax revenues.

Sourced from: Analytics.CLUB #WEB Newsletter

IBM Invests to Help Open-Source Big Data Software — and Itself

The IBM “endorsement effect” has often shaped the computer industry over the years. In 1981, when IBM entered the personal computer business, the company decisively pushed an upstart technology into the mainstream.

In 2000, the open-source operating system Linux was viewed askance in many corporations as an oddball creation and even legally risky to use, since the open-source ethos prefers sharing ideas rather than owning them. But IBM endorsed Linux and poured money and people into accelerating the adoption of the open-source operating system.

On Monday, IBM is to announce a broadly similar move in big data software. The company is placing a large investment — contributing software developers, technology and education programs — behind an open-source project for real-time data analysis, called Apache Spark.

The commitment, according to Robert Picciano, senior vice president for IBM’s data analytics business, will amount to “hundreds of millions of dollars” a year.

Photo courtesy of Pingdom via Flickr
Photo courtesy of Pingdom via Flickr

In the big data software market, much of the attention and investment so far has been focused on Apache Hadoop and the companies distributing that open-source software, including Cloudera, Hortonworks and MapR. Hadoop, put simply, is the software that makes it possible to handle and analyze vast volumes of all kinds of data. The technology came out of the pure Internet companies like Google and Yahoo, and is increasingly being used by mainstream companies, which want to do similar big data analysis in their businesses.

But if Hadoop opens the door to probing vast volumes of data, Spark promises speed. Real-time processing is essential for many applications, from analyzing sensor data streaming from machines to sales transactions on online marketplaces. The Spark technology was developed at the Algorithms, Machines and People Lab at the University of California, Berkeley. A group from the Berkeley lab founded a company two years ago, Databricks, which offers Spark software as a cloud service.

Spark, Mr. Picciano said, is crucial technology that will make it possible to “really deliver on the promise of big data.” That promise, he said, is to quickly gain insights from data to save time and costs, and to spot opportunities in fields like sales and new product development.

IBM said it will put more than 3,500 of its developers and researchers to work on Spark-related projects. It will contribute machine-learning technology to the open-source project, and embed Spark in IBM’s data analysis and commerce software. IBM will also offer Spark as a service on its programming platform for cloud software development, Bluemix. The company will open a Spark technology center in San Francisco to pursue Spark-based innovations.

And IBM plans to partner with academic and private education organizations including UC Berkeley’s AMPLab, DataCamp, Galvanize and Big Data University to teach Spark to as many as 1 million data engineers and data scientists.

Ion Stoica, the chief executive of Databricks, who is a Berkeley computer scientist on leave from the university, called the IBM move “a great validation for Spark.” He had talked to IBM people in recent months and knew they planned to back Spark, but, he added, “the magnitude is impressive.”

With its Spark initiative, analysts said, IBM wants to lend a hand to an open-source project, woo developers and strengthen its position in the fast-evolving market for big data software.

By aligning itself with a popular open-source project, IBM, they said, hopes to attract more software engineers to use its big data software tools, too. “It’s first and foremost a play for the minds — and hearts — of developers,” said Dan Vesset, an analyst at IDC.

IBM is investing in its own future as much as it is contributing to Spark. IBM needs a technology ecosystem, where it is a player and has influence, even if it does not immediately profit from it. IBM mainly makes its living selling applications, often tailored to individual companies, which address challenges in their business like marketing, customer service, supply-chain management and developing new products and services.

“IBM makes its money higher up, building solutions for customers,” said Mike Gualtieri, a analyst for Forrester Research. “That’s ultimately why this makes sense for IBM.”

To read the original article on The New York Times, click here.

Source: IBM Invests to Help Open-Source Big Data Software — and Itself by analyticsweekpick

The Value of Enterprise Feedback Management Vendors

In an excellent post, Bob Thompson reviews the VoC space in his blog on Voice of the Customer (Voc) Command Centers, including a discussion of 1) the six feedback dimensions, 2) how the VoC command center needs to include technology to a) capture feedback, b) analyze feedback and c) manage top priorities to resolution, and 3) the consolidation of the Enterprise Feedback Management (EFM) industry, including mention of the Verint acquisition of Vovici.

Enterprise Feedback Management (EFM) is the process of collecting, managing, analyzing and disseminating different sources (e.g., customers, employees, partners) of feedback.  EFM vendors help companies facilitate their customer experience management (CEM) efforts, hoping to improve the customer experience and increase customer loyalty.  The value that the Verint-Vovici solution provides their customers is stated in their press release:

“As the market’s most comprehensive VoC Analytics platform available, the Verint-Vovici solution will enable organizations to implement a single-vendor solution for collecting, analyzing and acting on customer insights.”

Advice to Verint-Vovici: VoC Programs are about People, Processes

A VoC program involves more than technology that helps companies capture, analyze and manage feedback. A VoC program contains many components, each impacting the program’s effectiveness. To improve customer loyalty, companies must consider how they structure their VoC program across these components. A VoC program has six major areas or components: Strategy, Governance, Business Process Integration, Method, Reporting, and Applied Research. Figure 1 below represents the components of customer feedback programs.

Components of a Customer Feedback Program
Figure 1. Elements of a Customer Feedback Program

The success of VoC programs depends on proper adoption of certain business practices in each of these six areas. While each of the six areas has VoC best practice standards, the major success drivers are related to strategy/governance, business process integration, and applied research. Companies who adopt the following practices experience higher levels of customer loyalty compared to companies who do not adopt these practices:

  • Customer feedback included in the company’s strategic vision, mission and goals.
  • Customer feedback results used in executives’ objectives and incentive compensation.
  • Customer feedback results included in the company/executive dashboards.
  • Customer feedback program integrated into business processes and technology (e.g., CRM system).
  • All areas of the customer feedback program (e.g., process and goals) communicated regularly to the entire company.
  • Customer feedback results shared throughout the company.
  • Statistical relationships established between customer feedback data and operational metrics (e.g., turnaround time, hold time).
  • Applied research using customer feedback data regularly conducted.
  • Statistical relationships established between customer feedback data and other constituency metrics (e.g., employee satisfaction or partner satisfaction metrics).

Reanalyzing that same data (note: the following has not yet been published), I had asked respondents about their use of third-party survey vendors and their satisfaction with these vendors. Surprisingly, I found that companies who used third-party survey vendors did not have more loyal customers (Mean = 68th percentile in industry on customer loyalty) than companies who did not use third-party vendors (Mean = 65th percentile). Furthermore, of those companies who used third-party vendors, only 60% of the companies were satisfied (20% very satisfied) with them. The use of EFM vendors does not guarantee improvements in the customer experience and customer loyalty.

While technology will continue to play a role in improving VoC programs by capturing, aggregating and disseminating customer feedback, it appears that the success of a VoC program is more about people and processes and less about technology. The Verint-Vovici solution (for that matter, all EFM solutions), to be successful, need to cognizant of all components of their customer’s VoC program and must consider how their technology will improve the people and processes (building a customer-centric culture).

Assessing your VoC Program

The way your VoC program is structured impacts its success. If you are a VoC professional who manages customer feedback programs for your company, you can take the Customer Feedback Program Diagnostic (CFPD) to determine if your VoC program adopts best practices. This brief assessment process can help your company:

  1. identify your customer feedback program’s strengths and weaknesses
  2. understand how to improve your customer feedback program
  3. facilitate your customer experience improvement efforts
  4. increase customer loyalty
  5. accelerate business growth

Upon completion of this 10-minutes assessment, you will receive immediate feedback on your company’s VoC program. Additionally, all respondents will receive a free summary report of the research findings.  Take the CFPD now.

Source: The Value of Enterprise Feedback Management Vendors

Nov 16, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Statistics  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Put big data to work with Cortana Analytics by analyticsweekpick

>> Respondents Needed for a Study about the Use of Net Scores and Mean Scores in Customer Experience Management by bobehayes

>> Understanding Customer Buying Journey with Big Data by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 What will you regulate? Former White House chief data scientist questions Tesla’s Elon Musk on his AI fear – Economic Times Under  Data Scientist

>>
 How Big Data Analytics are Empowering Customer’s Acquisition in Native Advertising – Customer Think Under  Big Data Analytics

>>
 Maine businesses respond to global cyber attack – WCSH-TV Under  Big Data Security

More NEWS ? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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A black swan is an event, positive or negative, that is deemed improbable yet causes massive consequences. In this groundbreaking and prophetic book, Taleb shows in a playful way that Black Swan events explain almost eve… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:What is an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset
A: Outliers:
– An observation point that is distant from other observations
– Can occur by chance in any distribution
– Often, they indicate measurement error or a heavy-tailed distribution
– Measurement error: discard them or use robust statistics
– Heavy-tailed distribution: high skewness, can’t use tools assuming a normal distribution
– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean
– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:
– No rigid mathematical method
– Subjective exercise: be careful
– Boxplots
– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:
– Depends on the cause
– Retention: when the underlying model is confidently known
– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cook’s distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesn’t perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:
– Mahalanobi’s distance
– Used to calculate the distance between two random vectors
– Difference with Euclidean distance: accounts for correlations
– Discard them

Source

[ VIDEO OF THE WEEK]

@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

 @JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

 Jeff Palmucci @TripAdvisor discusses managing a #MachineLearning #AI Team

Subscribe 

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[ FACT OF THE WEEK]

More than 5 billion people are calling, texting, tweeting and browsing on mobile phones worldwide.

Sourced from: Analytics.CLUB #WEB Newsletter

8 ways IBM Watson Analytics is transforming business

intro_title_hp-100640472-orig
8 ways IBM Watson Analytics is transforming business

IBM says Watson represents a new era of computing — a step forward to cognitive computing, where apps and systems interact with humans via natural language and help us augment our own understanding of the world with big data insights.

The Watson Analytics offering is intended to provide the benefits of advanced analytics without the complexity. The data discovery service, available via the cloud, guides data exploration, automates predictive analytics and enables dashboard and infographic creation.

Here are eight examples of organizations using Watson Analytics to transform their operations.

Build Predictive Model – Paschall Truck Lines (PTL)

Analyze injury report to improve safety - Mears Group

Identify new business opportunity - Minter Ellison

Save costs and optimize travel time - Caliber Patient Care

Identify Customer Behavior Trends - Kristalytics

Gain insights on concession stand performance – Legends

Teach students how to leverage social media sentiments - Iowa State University

Unvial insights and build advanced visualization - University of Memphis

 

Read the complete article at http://www.cio.com/article/3026691/analytics/8-ways-ibm-watson-analytics-is-transforming-business.html

Source: 8 ways IBM Watson Analytics is transforming business by analyticsweekpick

Big Data Analytics, Supercomputing Seed Growth in Plant Research

Over the millennia our ability to utilize plants in many different ways has allowed us to flourish as a species. Most importantly, they turn our waste carbon dioxide into oxygen.

But we have also used plants to provide shelter, to publish and transmit information on paper and as a food source. In fact, developing new ways to utilize plants has even led to population explosions throughout time, such as when we first developed granaries to store grain thousands of years ago. In these modern times of climate change, global warming, ever-increasing populations and fossil fuels, plants have never been more important.

We need to ensure that there is enough plant biomass available to satisfy the world’s needs as well as ensuring there is enough to preserve habitats, act as a carbon sink and supply us with enough oxygen. To do this we must find ways to increase biomass yields, increase land available for plant production, reduce the risk to crop yields from pests and disease, limit wasted biomass and optimize plant properties to better suit specific applications such as increasing nutritional composition for human health.

IBM is acutely aware of the importance of plants and is developing and utilizing a number of approaches in agriculture. Precision agriculture is one approach combining real-time data such as weather and soil quality with predictive analytics to allow the best decisions to be made when planting, fertilizing and harvesting crops. Another approach, in collaboration with Mars, has used bioinformatics to sequence the genome of cocoa.

Photo courtesy of Building a Smarter Planet
Photo courtesy of Building a Smarter Planet

Leveraging the technologies developed from other life sciences studies and developing specialized algorithms, IBM was able to identify genes that produced healthier and better tasting cocoa plants. Both of these approaches utilize IBM’s expertise in Big Data and Analytics.

Here in Australia I am part of a team at the IBM Research Collaboratory for Life Sciences – Melbourne working in collaboration with researchers in the ARC Centre of Excellence in Plant Cell Walls. We are using computational biology to examine the structure of the wall that surrounds all plant cells. We are investigating how the plant cell wall is produced, its structure and organisation and how it gets deconstructed.

In work just published in the journal Plant Physiology we used molecular dynamics techniques and high performance computing to model the major component of plant cell walls: cellulose. Our results strongly suggest that the fibres of cellulose are much smaller than previously believed. We are now investigating how these cellulose fibres interact with each other and other wall components to form the plant cell wall. Through these studies we intend to produce more accurate models of the plant cell wall and make predictions about how changes will affect the plant’s physical properties.

The possible application areas are vast. In the area of food security, we could optimize the properties of plant cell walls to make plants that are more drought/salt tolerant or more resistant to disease pathogens. In the area of human health, we hope to increase the nutritional composition of plant cell walls. In the paper and textiles industry, we could increase the physical strength of the plant cell wall making plants better for pulping or fibre production. In the area of biofuels, our studies should help to limit the effect of recalcitrance leading to more efficient ethanol extraction.

Supercomputers, such as the IBM Blue Gene/Q that we used at the University of Melbourne’s Victorian Life Sciences Computation Initiative (VLSCI), are essential in these type of projects where we examine the dynamics of biological systems at the nanoscale. Such work requires simulation of the motion of each atom and to do this we must calculate how all these atoms interact.

This must be done for many millions of time steps – a process that would take years on a standard desktop computer but days on a supercomputer. Supercomputing accelerates science and that is what the University of Melbourne and IBM Research set out to do with the formation of the VLSCI and the Collaboratory. Our work with the ARC Centre of Excellence in Plant Cell Walls is an excellent example of the success of these two organisations and the importance of plant research to the world.

Over the past few decades the major focus of life sciences research has been on animals and humans. In many ways, today we are at a similar position with plant research. We predict this research to grow dramatically in the coming years and it’s exciting to be at the forefront of the field.

To read the original article on Building a Smarter Planet, click here.

Originally Posted at: Big Data Analytics, Supercomputing Seed Growth in Plant Research by analyticsweekpick

Nov 09, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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>> Genomics England exploits big data analytics to personalise cancer treatment by analyticsweekpick

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CPSC 540 Machine Learning

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Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more

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Rise of the Robots: Technology and the Threat of a Jobless Future

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What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

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Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

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Q:Is mean imputation of missing data acceptable practice? Why or why not?
A: * Bad practice in general
* If just estimating means: mean imputation preserves the mean of the observed data
* Leads to an underestimate of the standard deviation
* Distorts relationships between variables by “pulling” estimates of the correlation toward zero

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Information is the oil of the 21st century, and analytics is the combustion engine. – Peter Sondergaard

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In 2015, a staggering 1 trillion photos will be taken and billions of them will be shared online. By 2017, nearly 80% of photos will be taken on smart phones.

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