Category: Uncategorized

[  COVER OF THE WEEK ]

Extrapolating  Source

[ LOCAL EVENTS & SESSIONS]

• Jul 09, 2017 #WEB Data Science for Professionals
• Jul 20, 2017 #WEB KNIME Introduction & Use Cases
• Jun 24, 2017 #WEB Free Live Data Analytics class using R, Tableau& SAS for Business

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Understanding Customer Buying Journey with Big Data by v1shal

>> The Differences Between a Business Analyst & a Data Analyst by anum

>> What Are the 3 Critical Keys to Healthcare Big Data Analytics? by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Traders are loading up on bets against China Evergrande Group … – Business Insider Under  Financial Analytics

>>
 Machine Learning Techniques for Predictive Maintenance – InfoQ.com Under  Machine Learning

>>
 Accepting What You Don’t Know Is Crucial to Detecting Risk – American Banker Under  Risk Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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]

Hypothesis Testing: A Visual Introduction To Statistical Significance

Statistical significance is a way of determining if an outcome occurred by random chance, or did something cause that outcome to be different than the expected baseline. Statistical significance calculations find their … 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 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]

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

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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]

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Nathaniel Lin (@analytics123), @NFPA

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[ FACT OF THE WEEK]

As recently as 2009 there were only a handful of big data projects and total industry revenues were under $100 million. By the end of 2012 more than 90 percent of the Fortune 500 will likely have at least some big data initiatives under way.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Data Accuracy  Source

[ AnalyticsWeek BYTES]

>> 5 Steps Required to Building a Best Practice Digital Analytics Function by analyticsweekpick

>> 100 Greatest Quotes On Leadership by v1shal

>> For the airline industry, big data is cleared for take-off by anum

Wanna write? Click Here

[ NEWS BYTES]

>>
 The Rise of Network Functions Virtualization – Virtualization Review Under  Virtualization

>>
 Data Science Up and Down the Ladder of Abstraction – InfoQ.com Under  Data Science

>>
 Wildly inaccurate election forecasts highlight Big Data challenges – ZDNet Under  Big Data Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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]

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

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]

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:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

Source

[ VIDEO OF THE WEEK]

 Surviving Internet of Things

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[ QUOTE OF THE WEEK]

In God we trust. All others must bring data. – W. Edwards Deming

[ PODCAST OF THE WEEK]

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

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[ 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

[  COVER OF THE WEEK ]

Fake data  Source

[ AnalyticsWeek BYTES]

>> IBM and Hadoop Challenge You to Use Big Data for Good by bobehayes

>> AtScale opens Hadoop’s big-data vaults to nonexpert business users by anum

>> Hacking the Data Science by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Machine Learning

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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 a decision tree?
A: 1. Take the entire data set as input
2. Search for a split that maximizes the ‘separation” of the classes. A split is any test that divides the data in two (e.g. if variable2>10)
3. Apply the split to the input data (divide step)
4. Re-apply steps 1 to 2 to the divided data
5. Stop when you meet some stopping criteria
6. (Optional) Clean up the tree when you went too far doing splits (called pruning)

Finding a split: methods vary, from greedy search (e.g. C4.5) to randomly selecting attributes and split points (random forests)

Purity measure: information gain, Gini coefficient, Chi Squared values

Stopping criteria: methods vary from minimum size, particular confidence in prediction, purity criteria threshold

Pruning: reduced error pruning, out of bag error pruning (ensemble methods)

Source

[ VIDEO OF THE WEEK]

 @AnalyticsWeek Panel Discussion: Big Data Analytics

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[ QUOTE OF THE WEEK]

In God we trust. All others must bring data. – W. Edwards Deming

[ PODCAST OF THE WEEK]

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

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[ FACT OF THE WEEK]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Ethics  Source

[ AnalyticsWeek BYTES]

>> Big Data has Big Implications for Customer Experience Management by bobehayes

>> Optimizing your customer relationship survey by bobehayes

>> Big Data Insights in Healthcare, Part II. A Perspective on Challenges to Adoption by froliol

Wanna write? Click Here

[ NEWS BYTES]

>>
 Red Hat launches OpenShift on Google Cloud – ZDNet Under  Cloud

>>
 Justifying the Hybrid Cloud: It’s All About the Application – IT Business Edge (blog) Under  Hybrid Cloud

>>
 MEF, PNDA Roll Out Initiative Focusing on LSO Analytics – SDxCentral Under  Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Machine Learning

6.867 is an introductory course on machine learning which gives an overview of many concepts, techniques, and algorithms in machine learning, beginning with topics such as classification and linear regression and ending … more

[ FEATURED READ]

On Intelligence

Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… 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:Examples of NoSQL architecture?
A: * Key-value: in a key-value NoSQL database, all of the data within consists of an indexed key and a value. Cassandra, DynamoDB
* Column-based: designed for storing data tables as sections of columns of data rather than as rows of data. HBase, SAP HANA
* Document Database: map a key to some document that contains structured information. The key is used to retrieve the document. MongoDB, CouchDB
* Graph Database: designed for data whose relations are well-represented as a graph and has elements which are interconnected, with an undetermined number of relations between them. Polyglot Neo4J

Source

[ VIDEO OF THE WEEK]

 Reimagining the role of data in government

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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]

 #BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

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[ FACT OF THE WEEK]

By 2020, we will have over 6.1 billion smartphone users globally (overtaking basic fixed phone subscriptions).

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

• Jun 14, 2017 #WEB Big Data & Analytics for Retail Summit – Day 1
• May 31, 2017 #WEB Empowering Yourself with Data Science & Case Study: Classifying Cars
• Jun 27, 2017 #WEB Sentiment Analysis Symposium – Day 1

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Your Agile Data Warehousing Architect: Excel by v1shal

>> The future of marketing automation depends on data analytics at scale by anum

>> Google loses data as lightning strikes by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Big Data Startup Tamr Wins Financial Investment From GE Ventures – CRN Under  Big Data

>>
 Securonix Unveils Big Data Security Analytics Platform With Unprecedented Threat Prediction, Detection and … – Broadway World Under  Big Data Security

>>
 Installing Ubuntu On Windows 10 — On vSphere – Virtualization Review Under  Virtualization

More NEWS ? Click Here

[ FEATURED COURSE]

Hadoop Starter Kit

Hadoop learning made easy and fun. Learn HDFS, MapReduce and introduction to Pig and Hive with FREE cluster access…. more

[ FEATURED READ]

Machine Learning With Random Forests And Decision Trees: A Visual Guide For Beginners

If you are looking for a book to help you understand how the machine learning algorithms “Random Forest” and “Decision Trees” work behind the scenes, then this is a good book for you. Those two algorithms are commonly u… more

[ TIPS & TRICKS OF THE WEEK]

Save yourself from zombie apocalypse from unscalable models
One living and breathing zombie in today’s analytical models is the pulsating absence of error bars. Not every model is scalable or holds ground with increasing data. Error bars that is tagged to almost every models should be duly calibrated. As business models rake in more data the error bars keep it sensible and in check. If error bars are not accounted for, we will make our models susceptible to failure leading us to halloween that we never wants to see.

[ DATA SCIENCE Q&A]

Q:What is: collaborative filtering, n-grams, cosine distance?
A: Collaborative filtering:
– Technique used by some recommender systems
– Filtering for information or patterns using techniques involving collaboration of multiple agents: viewpoints, data sources.
1. A user expresses his/her preferences by rating items (movies, CDs.)
2. The system matches this user’s ratings against other users’ and finds people with most similar tastes
3. With similar users, the system recommends items that the similar users have rated highly but not yet being rated by this user

n-grams:
– Contiguous sequence of n items from a given sequence of text or speech
– ‘Andrew is a talented data scientist”
– Bi-gram: ‘Andrew is”, ‘is a”, ‘a talented”.
– Tri-grams: ‘Andrew is a”, ‘is a talented”, ‘a talented data”.
– An n-gram model models sequences using statistical properties of n-grams; see: Shannon Game
– More concisely, n-gram model: P(Xi|Xi?(n?1)…Xi?1): Markov model
– N-gram model: each word depends only on the n?1 last words

Issues:
– when facing infrequent n-grams
– solution: smooth the probability distributions by assigning non-zero probabilities to unseen words or n-grams
– Methods: Good-Turing, Backoff, Kneser-Kney smoothing

Cosine distance:
– How similar are two documents?
– Perfect similarity/agreement: 1
– No agreement : 0 (orthogonality)
– Measures the orientation, not magnitude

Given two vectors A and B representing word frequencies:
cosine-similarity(A,B)=?A,B?/||A||?||B||

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

Torture the data, and it will confess to anything. – Ronald Coase

[ PODCAST OF THE WEEK]

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

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[ FACT OF THE WEEK]

The data volumes are exploding, more data has been created in the past two years than in the entire previous history of the human race.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Productivity  Source

[ LOCAL EVENTS & SESSIONS]

• Jun 21, 2017 #WEB Anyone Can Be a Data Scientist with Case Study: Classifying Cars
• May 27, 2017 #WEB [Online Global] Distributed Tensorflow AI + GPU + Model Optimizing Serving (20%)
• Jun 23, 2017 #WEB June 2017 INFORMATION SESSION | Azure Machine Learning Training*

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Solving the Healthcare Crisis with Mobile Big Data Analytics by jelaniharper

>> November 7, 2016 Health and Biotech analytics news roundup by pstein

>> Big Data Advances in Customer Experience Management by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Data science is creating a tidal wave of opportunity for women to get into executive leadership – Recode Under  Data Science

>>
 Net Politics China’s Big Data Push Runs Into Orwell and Red Tape … – Council on Foreign Relations (blog) Under  Big Data

>>
 7 Questions On How To Use AI Technology — Without A Data Scientist – MediaPost Communications Under  Data Scientist

More NEWS ? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

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

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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:Do we always need the intercept term in a regression model?
A: * It guarantees that the residuals have a zero mean
* It guarantees the least squares slopes estimates are unbiased
* the regression line floats up and down, by adjusting the constant, to a point where the mean of the residuals is zero

Source

[ VIDEO OF THE WEEK]

 Reimagining the role of data in government

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

[ PODCAST OF THE WEEK]

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

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[ FACT OF THE WEEK]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Big Data knows everything  Source

[ AnalyticsWeek BYTES]

>> What Is Happening With Women Entrepreneurs? [Infographics] by d3eksha

>> Wrapping my head around Big-data problem by v1shal

>> Using Analytics to build A Big Data Workforce by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Assessing the Google Cloud security strategy – TechTarget Under  Cloud Security

>>
 Big Data: Synthetic voices in one minute – The Boston Globe – The Boston Globe Under  Big Data

>>
 U. offers new graduate degree in business analytics – Deseret News Under  Business Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

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]

Research Design: Qualitative, Quantitative, and Mixed Methods Approaches, 4th Edition

The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… 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:What is better: good data or good models? And how do you define ‘good”? Is there a universal good model? Are there any models that are definitely not so good?
A: * Good data is definitely more important than good models
* If quality of the data wasn’t of importance, organizations wouldn’t spend so much time cleaning and preprocessing it!
* Even for scientific purpose: good data (reflected by the design of experiments) is very important

How do you define good?
– good data: data relevant regarding the project/task to be handled
– good model: model relevant regarding the project/task
– good model: a model that generalizes on external data sets

Is there a universal good model?
– No, otherwise there wouldn’t be the overfitting problem!
– Algorithm can be universal but not the model
– Model built on a specific data set in a specific organization could be ineffective in other data set of the same organization
– Models have to be updated on a somewhat regular basis

Are there any models that are definitely not so good?
– ‘all models are wrong but some are useful” George E.P. Box
– It depends on what you want: predictive models or explanatory power
– If both are bad: bad model

Source

[ VIDEO OF THE WEEK]

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

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[ 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]

 Using Analytics to build A #BigData #Workforce

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[ FACT OF THE WEEK]

And one of my favourite facts: At the moment less than 0.5% of all data is ever analysed and used, just imagine the potential here.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Data interpretation  Source

[ LOCAL EVENTS & SESSIONS]

• May 10, 2017 #WEB TDWI Chicago Conference – Day 4
• May 19, 2017 #WEB 2017 Machine Learning Conference, Seattle
• May 06, 2017 #WEB (ONLINE Presentation) – Learn Data Science & Data Analytics from scratch

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Improving the Value of Customer Experience Analytics by bobehayes

>> For the Bold, Bullied & Beautiful by v1shal

>> Big Data Provides Big Insights for U.S. Hospitals by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Streaming analytics market forecast for 2016 made available by top research firm – WhaTech Under  Streaming Analytics

>>
 Research details developments in the financial analytics global market analysis and forecast to 2021 – WhaTech Under  Financial Analytics

>>
 6 Questions Every CEO Should Ask About Their Data Security – Information Management Under  Data Security

More NEWS ? Click Here

[ FEATURED COURSE]

Data Mining

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

Mathematical superstar and inventor of fractal geometry, Benoit Mandelbrot, has spent the past forty years studying the underlying mathematics of space and natural patterns. What many of his followers don’t realize is th… 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 the Central Limit Theorem? Explain it. Why is it important?
A: The CLT states that the arithmetic mean of a sufficiently large number of iterates of independent random variables will be approximately normally distributed regardless of the underlying distribution. i.e: the sampling distribution of the sample mean is normally distributed.
– Used in hypothesis testing
– Used for confidence intervals
– Random variables must be iid: independent and identically distributed
– Finite variance

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

 #GlobalBusiness at the speed of The #BigAnalytics

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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

[  COVER OF THE WEEK ]

Data Accuracy  Source

[ LOCAL EVENTS & SESSIONS]

• May 19, 2017 #WEB 2017 Machine Learning Conference, Seattle
• May 31, 2017 #WEB Webinar – Advanced Power BI Visualizations by Marco Russo
• May 19, 2017 #WEB May 2017 INFORMATION SESSION | Azure Machine Learning Training*

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Intel bets on real-time analytics with high-end processor refresh by anum

>> In a Word: The Customer Sentiment Index by bobehayes

>> Why Your Company Should Use Data Science to Make Better Decisions by anum

Wanna write? Click Here

[ NEWS BYTES]

>>
 Court grants stay in FTC data security complaint against LabMD – Health Data Management Under  Data Security

>>
 WWE Royal Rumble 1990 Match Time and Statistics – Cageside Seats (blog) Under  Statistics

>>
 Social Listening in 2017: The Next Frontier in Social Media – MarketingProfs.com (subscription) Under  Sentiment Analysis

More NEWS ? Click Here

[ FEATURED COURSE]

Probability & Statistics

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]

The Industries of the Future

The New York Times bestseller, from leading innovation expert Alec Ross, a “fascinating vision” (Forbes) of what’s next for the world and how to navigate the changes the future will bring…. 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 JOB Q&A]

Q:Explain the difference between “long” and “wide” format data. Why would you use one or the other?
A: * Long: one column containing the values and another column listing the context of the value Fam_id year fam_inc

* Wide: each different variable in a separate column
Fam_id fam_inc96 fam_inc97 fam_inc98

Long Vs Wide:
– Data manipulations are much easier when data is in the wide format: summarize, filter
– Program requirements

Source

[ VIDEO OF THE WEEK]

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

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 Dr. Nipa Basu, @DnBUS

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[ FACT OF THE WEEK]

Every person in the US tweeting three tweets per minute for 26,976 years.

Sourced from: Analytics.CLUB #WEB Newsletter

 

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 ] Weak data  Source [ LOCAL EVENTS & SESSIONS] Apr 19, 2017 #WEB Apache Spark 101 – Learn in a fun way Apr 15, 2017 #WEB Machine Learning w/ H2O Apr 19, 2017 #WEB Beyond Bitcoin: What to do with Blockchain? 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 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 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 iTunes XbyTAO Facebook Twitter Youtube Analytic.Club LinkedIn Newsletter [ ENGAGE WITH TAO] #GetTAO Coach   Join @xTAOai   [ VIDEO OF THE WEEK]  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 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]   [ PROGRAMS]         *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.