Jun 27, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The Big Data Challenge: Generating Actionable Insight by analyticsweekpick

>> @EdwardBoudrot / @Optum on #DesignThinking in Data Driven Products by v1shal

>> Apache Spark for Big Analytics by thomaswdinsmore

Wanna write? Click Here

[ FEATURED COURSE]

Probability & Statistics

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This course introduces students to the basic concepts and logic of statistical reasoning and gives the students introductory-level practical ability to choose, generate, and properly interpret appropriate descriptive and… more

[ FEATURED READ]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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Python for Data Analysis is concerned with the nuts and bolts of manipulating, processing, cleaning, and crunching data in Python. It is also a practical, modern introduction to scientific computing in Python, tailored f… 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:How do you test whether a new credit risk scoring model works?
A: * Test on a holdout set
* Kolmogorov-Smirnov test

Kolmogorov-Smirnov test:
– Non-parametric test
– Compare a sample with a reference probability distribution or compare two samples
– Quantifies a distance between the empirical distribution function of the sample and the cumulative distribution function of the reference distribution
– Or between the empirical distribution functions of two samples
– Null hypothesis (two-samples test): samples are drawn from the same distribution
– Can be modified as a goodness of fit test
– In our case: cumulative percentages of good, cumulative percentages of bad

Source

[ VIDEO OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

 @JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe 

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[ FACT OF THE WEEK]

We are seeing a massive growth in video and photo data, where every minute up to 300 hours of video are uploaded to YouTube alone.

Sourced from: Analytics.CLUB #WEB Newsletter

Santa 2.0, What Santa could do with technology

Santa 2.0, What Santa could do with technology
Santa 2.0, What Santa could do with technology

Small 24-hour window to deliver all the present, how complicated the task is for Santa. Beyond the imagination of any logistics company today. So, doing this huge task in short amount of time, what should Santa do? Certainly, leveraging technology as catalyst goes a long way in making sure Santa’s day is not as crazy as it sounds. 1.8 million Kids under 14 years of age to serve, 24hours to spare. Delivering Gifts, Picture Stops with kids are some of many things Santa is tasked to do.

In the infographics below, financesonline put together some state of the art technologies to help Santa be a better, faster and optimal Santa. Santanet, Naughtiness Detector, Global Gift Network, Power boosters to name a few. Hyperloops, Amazon drones, Bounce Image explorer and Optical camouflage technologies are also used as well. So how much it all costs? A whooping $226b. This is price tags for helping Santa do his job better.

Let me stop talking and take you to this amazing infographics:

Cyber Santa Claus: Amazon Drones, Supercomputers and Other Cutting-edge Technologies Santa Can Use To Deliver Christmas Gifts
Designed by www.financesonline.com | Author: Julia Trello | Visit our Google+

Source: Santa 2.0, What Santa could do with technology by v1shal

House holding Analytics for Insurance

In a recent survey done by the West Monroe Partners, 2/3rds of insurance carriers indicated that they weren’t sure about the quality or accuracy of their data. Insurance as a business is driven by sales agents. Which means that nearly 70% or more of insurance business comes from sales agents. Most of the times, the end consumers relationship and day to day dealings are with the sales agent, who then interfaces with the carrier. Renewals, cross sell and retention efforts are driven by the sales agent.

 

While this works well for the carrier in terms of reach and scale, it does put them at one big disadvantage – lack of complete visibility into their customers profiles. The carrier is largely dependent on the agent to provide accurate and up-to-date information. This would include demographic data, policy relevant data, addresses etc… This opacity into customer data has many challenges:

 

Incorrect or Stagnant Data

Often when customers change addresses, or if there is a notable change in their life stage such as getting married or starting a family or getting a promotion etc…, these updates do not find their way back to the carrier.

Operational Inefficiencies / Contactability

While the sales agents are critical to the business of the carrier, the quality of customer information is paramount. Incorrect contact details such as incorrect email ids or WRONG contact numbers hamper a carrier’s efforts in cross sell, retention and even risk assessment.

Individual and Households

Most insurers look at policy holders in isolation. It may be quite possible that more than one individuals have different or multiple policies from the same insurer. As such the premium impact of the household is larger than that of the individuals. Insurers cannot optimize for cross sell or upsell keeping in mind the overall portfolio of the household.

Customer Feedback

When the policy holders relationship is with the sales agent, the carrier may not be able to get timely feedback from their consumers. In such cases, the carrier is never really sure of the end consumer experience (CX) it delivers.

 

Household Analytics

House-holding is the process of identifying connected individuals or households in an insurance portfolio. This is important from the point of view of an insurance company because it gives them a chance to run campaigns involving households rather than individual customers. It also gives them a chance to arrest cascading lapses in a household when they have a view of the households of risky customers.

 

Given a portfolio of policies, the process of house-holding involves 2 steps:

  1. Identifying individual customers (or persons) in the portfolio
  2. Identifying relations between the individuals identified in step 1 in order to be able to identify households.

 

There is a variety of information available about relationships between customers in the policy portfolio. This includes:

 

  1. The ‘proposer – insurer’ pair in every policy. (The fact that person A has bought a policy insuring person B is, barring group insurance, indicative of a relation between person A and person B.
  2. Common addresses for different customers – indicating that those individuals are staying at the same place.
  3. The nominee information and corresponding relationship information stored by insurance companies.

 

Potentially all of this information can be used to establish households, but before doing this we need to bear one important fact in mind. There are 2 kinds of errors that can occur in house-holding: False Negatives and False Positives.

We have detailed these in our white paper on Householding and it can be downloaded from here.

 

Advantages of Household Analytics

Householding is an important activity that can help unearth critical linkages between policy holders. A few immediate benefits that householding can provide are:

Contactability

By understanding which household a person belongs to, it may be possible to reach out to their alternate contact / email addresses. This is critical when there are important policy related updates that need to be communicated.

Cross Sell / Up Sell

The skill to identify and position a product that may be relevant to a household or an individual based on their portfolio can improve cross sell or upsell efficiencies. The customer will also appreciate a timely and relevant value proposition.

Risk Assessment

For an insurer, risk assessment at a household level in addition to the individual level can help write better premiums.

 

For more details on how householding analytics can help you, reach out to us below.

 

Contact

Source: House holding Analytics for Insurance by analyticsweek

Jun 20, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Accuracy check  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Customer Loyalty Resource for Customer Experience Professionals by bobehayes

>> How Similar Are UX Metrics in Moderated vs. Unmoderated Studies? by analyticsweek

>> Hacking journalism: Data science in the newsroom by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

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

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more

[ TIPS & TRICKS OF THE WEEK]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:How do you take millions of users with 100’s transactions each, amongst 10k’s of products and group the users together in meaningful segments?
A: 1. Some exploratory data analysis (get a first insight)

* Transactions by date
* Count of customers Vs number of items bought
* Total items Vs total basket per customer
* Total items Vs total basket per area

2.Create new features (per customer):

Counts:

* Total baskets (unique days)
* Total items
* Total spent
* Unique product id

Distributions:

* Items per basket
* Spent per basket
* Product id per basket
* Duration between visits
* Product preferences: proportion of items per product cat per basket

3. Too many features, dimension-reduction? PCA?

4. Clustering:

* PCA

5. Interpreting model fit
* View the clustering by principal component axis pairs PC1 Vs PC2, PC2 Vs PC1.
* Interpret each principal component regarding the linear combination it’s obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)

Source

[ VIDEO OF THE WEEK]

Unraveling the Mystery of #BigData

 Unraveling the Mystery of #BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

I’m sure, the highest capacity of storage device, will not enough to record all our stories; because, everytime with you is very valuable da

[ PODCAST OF THE WEEK]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

 #BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

Subscribe 

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[ FACT OF THE WEEK]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter

Massive Data Visualization Brings NYC’s Busiest Street To Life

What does big data tell us about the character of an iconic street? Created by data viz maestros Moritz Stefaner and Lev Manovich, On Broadway is an unprecedented visualization of social media data, which pulls in millions of updates from Instagram, Twitter, Foursquare, Google Street View, and more as a way of exploring the data that stacks up, almost like vertebrae, to translate the spine of Manhattan into a digital entity.

On Broadway was inspired by Every Building on Sunset Strip, a 1966 book by Edward Ruscha that unfolds to a 25-foot-long panorama of a 1.5-mile section of Sunset Boulevard. On Broadway takes that concept into the 21st century. By loading up the app in your web browser, you can explore the 13-mile section of Broadway that runs through the heart of Manhattan in various ways, from the average colors in the facades of Harlem to the average number of tweets per day in the Financial District.

According to project lead Lev Manovich, On Broadway is the latest in a series of experiments to leverage computers, the web, and massive data to represent our cities in new ways. “We wanted to avoid standard techniques like numbers, graphs, or maps,” he tells me, instead opting for “a visually rich image-centric interface with no maps and where numbers only play a secondary role.” The site represents Broadway as a vertical stack of image and data layers, as colorful and densely packed as one of the thousands of buildings in New York itself.

What’s most impressive about On Broadway is the fact that its interface holds up at all, given the massive data sets being thrown at it. To create On Broadway, the team pulled in 660,000 Instagram photos taken over a five-month period in 2014, as well as more than eight million Foursquare check-ins from 2009 to 2014, 22 million taxi pick-ups and drop-offs in 2013, countless Google Street View images and Twitter posts, and economic data from the U.S. Census Bureau. You explore each section of Broadway by clicking on a box representing its neighborhood at the bottom of the page, and On Broadway will even call out stats about individual buildings as you browse, like Monk’s fromSeinfeld.

With so much data in play, there’s no one big takeaway from On Broadway. You might learn, for example, that Columbia University is comparatively underrepresented on Twitter, or that taxi pickups in Midtown outnumber those in Harlem by almost 30 to 1, or that the average facade colors are tones of brown or blue, but ultimately, Broadway is as vibrant and complicated as the city it streaks through. What you get from it is going to depend on what you’re looking for.

On Broadway is currently on display as an interactive touch-screen exhibition at the New York Public Library until January 3, 2016, but you can also explore it online here.

Originally posted via “Massive Data Visualization Brings NYC’s Busiest Street To Life”

 

Source: Massive Data Visualization Brings NYC’s Busiest Street To Life

Jun 13, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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SQL Database  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Hacking journalism: Data science in the newsroom by analyticsweekpick

>> Data Scientists Never Stop Learning: Q&A Spotlight with Isabelle Nuage of Talend by analyticsweekpick

>> May 23, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

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Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more

[ FEATURED READ]

Superintelligence: Paths, Dangers, Strategies

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The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … more

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q:Is it better to design robust or accurate algorithms?
A: A. The ultimate goal is to design systems with good generalization capacity, that is, systems that correctly identify patterns in data instances not seen before
B. The generalization performance of a learning system strongly depends on the complexity of the model assumed
C. If the model is too simple, the system can only capture the actual data regularities in a rough manner. In this case, the system poor generalization properties and is said to suffer from underfitting
D. By contrast, when the model is too complex, the system can identify accidental patterns in the training data that need not be present in the test set. These spurious patterns can be the result of random fluctuations or of measurement errors during the data collection process. In this case, the generalization capacity of the learning system is also poor. The learning system is said to be affected by overfitting
E. Spurious patterns, which are only present by accident in the data, tend to have complex forms. This is the idea behind the principle of Occam’s razor for avoiding overfitting: simpler models are preferred if more complex models do not significantly improve the quality of the description for the observations
Quick response: Occam’s Razor. It depends on the learning task. Choose the right balance
F. Ensemble learning can help balancing bias/variance (several weak learners together = strong learner)
Source

[ VIDEO OF THE WEEK]

Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

 Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

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[ QUOTE OF THE WEEK]

Data that is loved tends to survive. – Kurt Bollacker, Data Scientist, Freebase/Infochimps

[ PODCAST OF THE WEEK]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

 Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Distributed computing (performing computing tasks using a network of computers in the cloud) is very real. Google GOOGL -0.53% uses it every day to involve about 1,000 computers in answering a single search query, which takes no more than 0.2 seconds to complete.

Sourced from: Analytics.CLUB #WEB Newsletter

Creative Destruction and Risk Taking

“Creative Destruction” is an aspect of the creative process, which is one of the systems at work when a company is inventing, innovating or researching new ideas or creations. Destruction inhabits a different sphere and most often is where over-funded and under-used knowledge gained through the creative process languishes. New ideas or inventions can remain stagnant and unused because a company might not realize what the knowledge can achieve or be applied to. In extreme cases, they might purposefully delay utilizing the insight so they don’t cannibalize their own prior work.

Rajshree Adarwal, a professor at the University of Maryland, wrote about Creative Construction and Knowledge spillover. He points to Xerox as a concrete example of a company that grew too big for its own good: “evidence shows that many large, established companies find it difficult to take advantage of all the opportunities emanating from their investment in scientific knowledge.” Xerox was originally the Haloid Photographic Company was the only business to create a product based on Chester Carlson’s invention, a process for printing images using electromagnetism, a drum, and dry toner. It’d take the newly named Xerox Corporation 20 years, from 1938 to 1959, to refine the process enough to release the first commercial plain paper copier: the Xerox 914. Decades of research and investments paid off when they completely captured the copier market (they had just created).

It’d only take ten more years for a researcher at Xerox to develop the laser printer, in 1969, which Xerox would withhold from developing a commercial version to keep from impacting their growing copier business. They wouldn’t release a laser printer until 1976, two years after their FTC agreement to license their patent portfolio (for maximum revenue of 1/2% of profits).

The laser printer was withheld on purpose; Xerox recognized the usefulness of the design. What about places where they didn’t recognize the utility or application? For that, there is one device that is easy to single out: a personal computer that designed with a single user in mind, the Alto computer. It was developed in 1974, and fifty units were donated to Stanford University. A majority of the computers were kept at the PARC, because their primary purpose was to help researchers, well, research.

What did the Alto personal computer bring to the table that previous minicomputers didn’t? A GUI based interface, rather than strict command-line. A three-button mouse for pointing. An early predecessor to the Ethernet, where the computer’s networked features had packet features, decentralized processing (in the case of a computer could act as a printer server with no user interface). All of these groundbreaking features were essentially written off as “unmarketable” by Xerox, whose eyes were firmly fixed on the copier market share it had grown to dominate and was in the process of losing.

By the mid-1970s, through a combination of xerography patents expiring and a FTC antitrust filing that forced Xerox to grant licensing options to its retained patent portfolio, Xerox’s market share plummeted from 90% to a dismal 13%. The concept of launching a personal, personal computer wasn’t not only in the books but not in the imagination.

Apple was shown PARC’s Alto computer, while they were designing the Lisa computer at the time, and it helped ‘galvanize’ the idea that the graphic user interface was the way to go.

James Turner of O’Reilly News interviewed one of the engineers that worked on the Macintosh, Andy Hertzfeld. While discussing the myth or allegations that Apple (and Microsoft) stole ideas from Xerox, they related that Xerox’s upper management just didn’t care about anything that didn’t involve copiers or paper. Turner notes, “I actually worked for Xerox AI Systems in 1986 and it was kind of frustrating because they really had the mentality there that if you couldn’t sell paper and toner for [them] they weren’t interested.” Others recall nicknames those upper executives were called behind closed doors, ‘toner-heads.’

What about a company that did take advantage of their market share, innovation, and research? Xerox’s ideas certainly weren’t lost to time – they were disseminated through what Professor Adarwal calls “knowledge spillover,” where knowledge gained at one location spreads as employees move between companies or industries. We have Ethernet, ip4 and ip6 protocols, networked personal computers, mice.

AT&T took advantage of its research and innovation and its secure footing as an ‘allowed’ monopoly under US antitrust law in the early 1900s to pave the way from a national telephone network to the first commercial satellite launched into space.

Alexander Bell didn’t only create the telephone, but a lesser known “photophone.” Years before the first radio transmissions were broadcast, Bell communicated over 700 feet using light encoded with data to send an audio signal. He was issued the master patent for his photophone in 1880. This same technology would grow and develop, by AT&T in the next century, to become fiber optic communication. Without fiber optics, transpacific and transatlantic internet and telephone lines wouldn’t be possible.

It’s important to not settle for ‘good enough’ or grow too narrow-minded in any industry or field. The quickest way for any industrial leader to get cut down in their prime is deciding the place they’re at is perfect and they don’t need to innovate or diversify their holdings. Too many companies are so driven by data-driven metrics that they lose sight of the peripheral, the hard-to-quantify information, the ‘quality of life’ invention that doesn’t have a place yet, but could if more ‘artistic’ liberties were taken with management decisions, rather than ‘by-the-numbers’ which promises steady growth over risk-taking innovation. Fortune favors the bold, and to see the company that took that risk and plunge by investing in Chester’s xerography invention suddenly turn their heads at their innovative computer designs is a narrative no executive should want to retread.

 

Originally Posted at: Creative Destruction and Risk Taking

Jun 06, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Statistically Significant  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #Podcast #FutureOfData by admin

>> Every step you take: Who owns our mobile health data? by analyticsweekpick

>> October 17, 2016 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

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Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more

[ FEATURED READ]

Thinking, Fast and Slow

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Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… 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 is the maximal margin classifier? How this margin can be achieved?
A: * When the data can be perfectly separated using a hyperplane, there actually exists an infinite number of these hyperplanes
* Intuition: a hyperplane can usually be shifted a tiny bit up, or down, or rotated, without coming into contact with any of the observations
* Large margin classifier: choosing the hyperplance that is farthest from the training observations
* This margin can be achieved using support vectors

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

 @AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

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[ QUOTE OF THE WEEK]

If we have data, let’s look at data. If all we have are opinions, let’s go with mine. – Jim Barksdale

[ PODCAST OF THE WEEK]

Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

 Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

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[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter