Jun 08, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Pacman  Source

[ AnalyticsWeek BYTES]

>> 100 Greatest Quotes On Leadership by v1shal

>> How Oracle Uses Big Data to Improve the Customer Experience by bobehayes

>> Improve the Patient Experience to Increase Patient Loyalty by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Internet of Things Can Drive Efficiency, Safety and Productivity – BizTech Magazine Under  Internet Of Things

>>
 Cloud Complexity Leads to Chaotic Security Environments … – Infosecurity Magazine Under  Cloud Security

>>
 Verisk Analytics, Inc. (VRSK) Director Christopher M. Foskett Sells 10500 Shares – Sports Perspectives Under  Risk Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

The Analytics Edge

image

This is an Archived Course
EdX keeps courses open for enrollment after they end to allow learners to explore content and continue learning. All features and materials may not be available, and course content will not be… more

[ FEATURED READ]

How to Create a Mind: The Secret of Human Thought Revealed

image

Ray Kurzweil is arguably today’s most influential—and often controversial—futurist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilization—reverse… 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 statistical power?
A: * sensitivity of a binary hypothesis test
* Probability that the test correctly rejects the null hypothesis H0H0 when the alternative is true H1H1
* Ability of a test to detect an effect, if the effect actually exists
* Power=P(reject H0|H1istrue)
* As power increases, chances of Type II error (false negative) decrease
* Used in the design of experiments, to calculate the minimum sample size required so that one can reasonably detects an effect. i.e: ‘how many times do I need to flip a coin to conclude it is biased?’
* Used to compare tests. Example: between a parametric and a non-parametric test of the same hypothesis

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

 @AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Everybody gets so much information all day long that they lose their common sense. – Gertrude Stein

[ PODCAST OF THE WEEK]

Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

 Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 01, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Data analyst  Source

[ AnalyticsWeek BYTES]

>> Big universe, big data, astronomical opportunity by analyticsweekpick

>> Google loses data as lightning strikes by analyticsweekpick

>> Rise of Data Capital by Paul Sonderegger by thebiganalytics

Wanna write? Click Here

[ NEWS BYTES]

>>
 Internet of Things botnet threatens to knock the entire country of Liberia offline – Boing Boing Under  Internet Of Things

>>
 Network Engineer – Truven Health Analytics | The Siasat Daily – The Siasat Daily Under  Health Analytics

>>
 Irdai asks insurers to appoint data security officer by April 30 – Economic Times Under  Data Security

More NEWS ? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

image

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

image

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]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

[ VIDEO OF THE WEEK]

Using Analytics to build A #BigData #Workforce

 Using Analytics to build A #BigData #Workforce

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Every person in the world having more than 215m high-resolution MRI scans a day.

Sourced from: Analytics.CLUB #WEB Newsletter

May 25, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Productivity  Source

[ FEATURED COURSE]

CS229 – Machine Learning

image

This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

image

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]

Data aids, not replace judgement
Data is a tool and means to help build a consensus to facilitate human decision-making but not replace it. Analysis converts data into information, information via context leads to insight. Insights lead to decision making which ultimately leads to outcomes that brings value. So, data is just the start, context and intuition plays a role.

[ DATA SCIENCE Q&A]

Q:Which kernels do you know? How to choose a kernel?
A: * Gaussian kernel
* Linear kernel
* Polynomial kernel
* Laplace kernel
* Esoteric kernels: string kernels, chi-square kernels
* If number of features is large (relative to number of observations): SVM with linear kernel ; e.g. text classification with lots of words, small training example
* If number of features is small, number of observations is intermediate: Gaussian kernel
* If number of features is small, number of observations is small: linear kernel

Source

[ VIDEO OF THE WEEK]

Rethinking classical approaches to analysis and predictive modeling

 Rethinking classical approaches to analysis and predictive modeling

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

With data collection, ‘the sooner the better’ is always the best answer. – Marissa Mayer

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Juan Gorricho, @disney

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

The largest AT&T database boasts titles including the largest volume of data in one unique database (312 terabytes) and the second largest number of rows in a unique database (1.9 trillion), which comprises AT&T’s extensive calling records.

Sourced from: Analytics.CLUB #WEB Newsletter

May 18, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Insights  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Challenges for Data Driven Organization by d3eksha

>> The Business of Data by anum

>> Four Ways Big Data Can Improve Customer Surveys by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Exxon Mobil: Lies, Damned Lies, And Statistics – Seeking Alpha – Seeking Alpha Under  Statistics

>>
 Free checklist shows how recruiters and HR teams can integrate online assessments with their ATS/HRIS – Onrec Under  Talent Analytics

>>
 Understanding Comcast’s “Internet of Things” Story in 11 Slides – Motley Fool Under  Internet Of Things

More NEWS ? Click Here

[ FEATURED COURSE]

Intro to Machine Learning

image

Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most stra… more

[ FEATURED READ]

The Master Algorithm: How the Quest for the Ultimate Learning Machine Will Remake Our World

image

In the world’s top research labs and universities, the race is on to invent the ultimate learning algorithm: one capable of discovering any knowledge from data, and doing anything we want, before we even ask. In The Mast… more

[ TIPS & TRICKS OF THE WEEK]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:How do you assess the statistical significance of an insight?
A: * is this insight just observed by chance or is it a real insight?
Statistical significance can be accessed using hypothesis testing:
– Stating a null hypothesis which is usually the opposite of what we wish to test (classifiers A and B perform equivalently, Treatment A is equal of treatment B)
– Then, we choose a suitable statistical test and statistics used to reject the null hypothesis
– Also, we choose a critical region for the statistics to lie in that is extreme enough for the null hypothesis to be rejected (p-value)
– We calculate the observed test statistics from the data and check whether it lies in the critical region

Common tests:
– One sample Z test
– Two-sample Z test
– One sample t-test
– paired t-test
– Two sample pooled equal variances t-test
– Two sample unpooled unequal variances t-test and unequal sample sizes (Welch’s t-test)
– Chi-squared test for variances
– Chi-squared test for goodness of fit
– Anova (for instance: are the two regression models equals? F-test)
– Regression F-test (i.e: is at least one of the predictor useful in predicting the response?)

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Scott Zoldi, @fico

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Scott Zoldi, @fico

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

May 11, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Data interpretation  Source

[ NEWS BYTES]

>>
 Certificate Course in Business Analytics – Mathrubhumi English Under  Business Analytics

>>
 Connecting Unemployed Youth with Organizations That Need Talent – Harvard Business Review Under  Talent Analytics

>>
 How big data delivers data driven stories – ITProPortal Under  Big Data

More NEWS ? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

image

Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

Big Data: A Revolution That Will Transform How We Live, Work, and Think

image

“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:How frequently an algorithm must be updated?
A: You want to update an algorithm when:
– You want the model to evolve as data streams through infrastructure
– The underlying data source is changing
– Example: a retail store model that remains accurate as the business grows
– Dealing with non-stationarity

Some options:
– Incremental algorithms: the model is updated every time it sees a new training example
Note: simple, you always have an up-to-date model but you can’t incorporate data to different degrees.
Sometimes mandatory: when data must be discarded once seen (privacy)
– Periodic re-training in “batch” mode: simply buffer the relevant data and update the model every-so-often
Note: more decisions and more complex implementations

How frequently?
– Is the sacrifice worth it?
– Data horizon: how quickly do you need the most recent training example to be part of your model?
– Data obsolescence: how long does it take before data is irrelevant to the model? Are some older instances
more relevant than the newer ones?
Economics: generally, newer instances are more relevant than older ones. However, data from the same month, quarter or year of the last year can be more relevant than the same periods of the current year. In a recession period: data from previous recessions can be more relevant than newer data from different economic cycles.

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

 @AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The goal is to turn data into information, and information into insight. – Carly Fiorina

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

This year, over 1.4 billion smart phones will be shipped – all packed with sensors capable of collecting all kinds of data, not to mention the data the users create themselves.

Sourced from: Analytics.CLUB #WEB Newsletter

Apr 27, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Ethics  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Do Alexa and other such devices mean the end of privacy? – The Boston Globe Under  Internet Of Things

>>
 Box and Skyhigh Networks come together for greater cloud security … – Cloud Tech Under  Cloud Security

>>
 Three ways Salesforce Einstein can enhance workplace analytics – TechTarget Under  Sales Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

image

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

image

Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE JOB Q&A]

Q:Explain what a local optimum is and why it is important in a specific context,
such as K-means clustering. What are specific ways of determining if you have a local optimum problem? What can be done to avoid local optima?

A: * A solution that is optimal in within a neighboring set of candidate solutions
* In contrast with global optimum: the optimal solution among all others

* K-means clustering context:
It’s proven that the objective cost function will always decrease until a local optimum is reached.
Results will depend on the initial random cluster assignment

* Determining if you have a local optimum problem:
Tendency of premature convergence
Different initialization induces different optima

* Avoid local optima in a K-means context: repeat K-means and take the solution that has the lowest cost

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

 @AnalyticsWeek Panel Discussion: Finance and Insurance Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Numbers have an important story to tell. They rely on you to give them a voice. – Stephen Few

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Scott Zoldi, @fico

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Scott Zoldi, @fico

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

In that same survey, by a small but noticeable margin, executives at small companies (fewer than 1,000 employees) are nearly 10 percent more likely to view data as a strategic differentiator than their counterparts at large enterprises.

Sourced from: Analytics.CLUB #WEB Newsletter

Apr 20, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

 

Issue #15    Web Version
Contact Us: info@analyticsweek.com

[  ANNOUNCEMENT ]

I hope this note finds you well. Please excuse the brief interruption in our newsletter. Over past few weeks, we have been doing some A/B testing and mounting our Newsletter on our AI led coach TAO.ai. This newsletter and future versions would be using capability of TAO. As with any AI, it needs some training, so kindly excuse/report the rough edges.

– Team TAO/AnalyticsCLUB

[  COVER OF THE WEEK ]

image
Weak data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Collaborative Analytics: Analytics for your BigData by v1shal

>> Colleges are using big data to identify when students are likely to flame out by analyticsweekpick

>> Rise of Data Capital by Paul Sonderegger by thebiganalytics

Wanna write? Click Here

[ NEWS BYTES]

>>
 Strategy Analytics: Android accounts for 88% of smartphones shipped in Q3 2016 – GSMArena.com Under  Analytics

>>
 Did you know we’re sedentary but less obese than average? So says Miami statistics website – Miami Herald Under  Statistics

>>
 MHS grad sinks Steel Roots in cyber security – News – North of … – Wicked Local North of Boston Under  cyber security

More NEWS ? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

image

This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

image

People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… more

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE JOB Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

[ ENGAGE WITH CLUB]

 ASK Club      FIND Project   

Get HIRED  #GetTAO Coach

 

[ FOLLOW & SIGNUP]

TAO

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XbyTAO

Facebook

Twitter

Youtube

Analytic.Club

LinkedIn

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[ ENGAGE WITH TAO]

#GetTAO Coach

  Join @xTAOai  

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with David Rose, @DittoLabs

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or 44 trillion gigabytes.

[ TAO DEMO]

AnalyticsClub Demo Video

 

[ PROGRAMS]

Invite top local professionals to your office

 

↓

 

Data Analytics Hiring Drive

 

 
*This Newsletter is hand-curated and autogenerated using #TEAMTAO & TAO, excuse some initial blemishes. As with any AI, it may get worse before it will get relevant, excuse us with your patience & feedback.
Let us know how we could improve the experience using: feedbackform

Copyright © 2016 AnalyticsWeek LLC.

Apr 13, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

 

Issue #15    Web Version
Contact Us: info@analyticsweek.com

[  ANNOUNCEMENT ]

I hope this note finds you well. Please excuse the brief interruption in our newsletter. Over past few weeks, we have been doing some A/B testing and mounting our Newsletter on our AI led coach TAO.ai. This newsletter and future versions would be using capability of TAO. As with any AI, it needs some training, so kindly excuse/report the rough edges.

– Team TAO/AnalyticsCLUB

[  COVER OF THE WEEK ]

image
Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The What and Where of Big Data: A Data Definition Framework by bobehayes

>> The Cost Of Too Much Data by v1shal

>> Unraveling the Mystery of Big Data by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 How a Data Scientist’s Job ‘Play in Front’ than other BI and Analytic Roles – CIOReview Under  Data Scientist

>>
 AI, Machine Learning to Reach $47 Billion by 2020 – Infosecurity Magazine Under  Machine Learning

>>
 Software to “Encode the Mindset” of Lawyers – Lawfuel (blog) Under  Prescriptive Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

image

Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

image

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]

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 JOB Q&A]

Q:What is cross-validation? How to do it right?
A: It’s a model validation technique for assessing how the results of a statistical analysis will generalize to an independent data set. Mainly used in settings where the goal is prediction and one wants to estimate how accurately a model will perform in practice. The goal of cross-validation is to define a data set to test the model in the training phase (i.e. validation data set) in order to limit problems like overfitting, and get an insight on how the model will generalize to an independent data set.

Examples: leave-one-out cross validation, K-fold cross validation

How to do it right?

the training and validation data sets have to be drawn from the same population
predicting stock prices: trained for a certain 5-year period, it’s unrealistic to treat the subsequent 5-year a draw from the same population
common mistake: for instance the step of choosing the kernel parameters of a SVM should be cross-validated as well
Bias-variance trade-off for k-fold cross validation:

Leave-one-out cross-validation: gives approximately unbiased estimates of the test error since each training set contains almost the entire data set (n?1n?1 observations).

But: we average the outputs of n fitted models, each of which is trained on an almost identical set of observations hence the outputs are highly correlated. Since the variance of a mean of quantities increases when correlation of these quantities increase, the test error estimate from a LOOCV has higher variance than the one obtained with k-fold cross validation

Typically, we choose k=5 or k=10, as these values have been shown empirically to yield test error estimates that suffer neither from excessively high bias nor high variance.
Source

[ ENGAGE WITH CLUB]

 ASK Club      FIND Project   

Get HIRED  #GetTAO Coach

 

[ FOLLOW & SIGNUP]

TAO

iTunes

XbyTAO

Facebook

Twitter

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Analytic.Club

LinkedIn

Newsletter

[ ENGAGE WITH TAO]

#GetTAO Coach

  Join @xTAOai  

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

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[ QUOTE OF THE WEEK]

Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

140,000 to 190,000. Too few people with deep analytical skills to fill the demand of Big Data jobs in the U.S. by 2018.

[ TAO DEMO]

AnalyticsClub Demo Video

 

[ PROGRAMS]

Invite top local professionals to your office

 

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Data Analytics Hiring Drive

 

 
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