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

[  COVER OF THE WEEK ]

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Human resource  Source

[ AnalyticsWeek BYTES]

>> Automating Data Modeling for the Internet of Things: Accelerating Transformation and Data Preparation by jelaniharper

>> The End of Transformation: Expediting Data Preparation and Analytics with Edge Computing by jelaniharper

>> The Total Customer Experience: How Oracle Builds their Business Around the Customer by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 Mercy Builds a Healthy Data Framework – CIO Insight Under  Analytics

>>
 One Click Retail Publishes Amazon Holiday Strategy Report for Brand Manufacturers – Broadway World Under  Sales Analytics

>>
 HPE, Hedvig announce hybrid cloud storage partnership – Network World Under  Hybrid Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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Analytics with the Chadwick tools, dplyr, and ggplot…. more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… 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 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]

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

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

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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 with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

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[ FACT OF THE WEEK]

Three-quarters of decision-makers (76 per cent) surveyed anticipate significant impacts in the domain of storage systems as a result of the “Big Data” phenomenon.

Sourced from: Analytics.CLUB #WEB Newsletter

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

[  COVER OF THE WEEK ]

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Pacman  Source

[ AnalyticsWeek BYTES]

>> July 24, 2017 Health and Biotech analytics news roundup by pstein

>> 8 Ways Big Data and Analytics Will Change Sports by analyticsweekpick

>> What “Gangnam Style” could teach about branding: 5 Lessons by d3eksha

Wanna write? Click Here

[ NEWS BYTES]

>>
 Is your business too complacent about cyber security? – Information Age Under  cyber security

>>
 Trilliant buys payer analytics play – Nashville Post Under  Health Analytics

>>
 Gran Tierra Appoints New Director – Markets Insider Under  Risk Analytics

More NEWS ? 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]

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]

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: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]

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]

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

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

 Understanding Data Analytics in Information Security with @JayJarome, @BitSight

Subscribe 

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[ 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

Nov 02, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Pacman  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Media firms are excelling at social: Reach grows by 236% by analyticsweekpick

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

>> The Evolution of Master Data Management: Connecting the Big Data Dots by jelaniharper

Wanna write? Click Here

[ NEWS BYTES]

>>
 Free analytics tool lets developers troubleshoot app performance – BetaNews Under  Analytics

>>
 Goldman Sachs Leads $38M Investment in Cloud Storage Firm … – Xconomy Under  Cloud

>>
 Zodiac Aerospace flies in Birst to support local data needs – Diginomica Under  Business Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Intro to Machine Learning

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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]

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]

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

[ DATA SCIENCE Q&A]

Q: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]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

You can have data without information, but you cannot have information without data. – Daniel Keys Moran

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

Subscribe 

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[ 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

Sep 21, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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SQL Database  Source

[ AnalyticsWeek BYTES]

>> B.O.B.’s Top 10 Blog Posts of 2011 by bobehayes

>> Deriving “Inherently Intelligent” Information from Artificial Intelligence by jelaniharper

>> February 6, 2017 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ NEWS BYTES]

>>
 Which Analysts Are Watching Marchex, Inc. (NASDAQ:MCHX … – Fiscal Standard Under  Sales Analytics

>>
 TIBCO Named a Leader in Streaming Analytics by Top Independent Research Firm – CSO Australia Under  Streaming Analytics

>>
 Why you should embrace sales analytics tools – TechTarget Under  Sales Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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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]

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 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]

Using Topological Data Analysis on your BigData

 Using Topological Data Analysis on your BigData

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]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

Subscribe 

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[ FACT OF THE WEEK]

Decoding the human genome originally took 10 years to process; now it can be achieved in one week.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 14, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Human resource  Source

[ FEATURED COURSE]

The Analytics Edge

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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]

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

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“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]

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: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]

Using Topological Data Analysis on your BigData

 Using Topological Data Analysis on your BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 07, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Ethics  Source

[ AnalyticsWeek BYTES]

>> How big data is driving smarter cyber security tools by analyticsweekpick

>> How CFOs Can Harness Analytics by analyticsweekpick

>> Predicting the Future and Shaping Strategy with HR Analytics by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Using data science to improve partner win rates – Computer Dealer News Under  Data Science

>>
 Microsoft’s Online Data Science Program Enrolls in School – eWeek Under  Data Scientist

>>
 Hootsuite buys Snapchat analytics platform from Naritiv – ZDNet Under  Social Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Machine Learning

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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]

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

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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]

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

[ DATA SCIENCE Q&A]

Q:Do you think 50 small decision trees are better than a large one? Why?
A: * Yes!
* More robust model (ensemble of weak learners that come and make a strong learner)
* Better to improve a model by taking many small steps than fewer large steps
* If one tree is erroneous, it can be auto-corrected by the following
* Less prone to overfitting

Source

[ VIDEO OF THE WEEK]

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

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

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 @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

Subscribe 

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[ FACT OF THE WEEK]

40% projected growth in global data generated per year vs. 5% growth in global IT spending.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 31, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data security  Source

[ AnalyticsWeek BYTES]

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

>> Data Science Skills and the Improbable Unicorn by bobehayes

>> When shouldn’t you rely on data analytics – The danger of trusting polls by checcaaird

Wanna write? Click Here

[ NEWS BYTES]

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

>>
 Pulling Real ROI from a Sea of Big Data Analytics – insideBIGDATA Under  Big Data Analytics

>>
 Four things you should know about machine learning – GovInsider Under  Machine Learning

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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

[ FEATURED READ]

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

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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]

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: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]

Rethinking classical approaches to analysis and predictive modeling

 Rethinking classical approaches to analysis and predictive modeling

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]

Using Analytics to build A #BigData #Workforce

 Using Analytics to build A #BigData #Workforce

Subscribe 

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[ FACT OF THE WEEK]

According to estimates, the volume of business data worldwide, across all companies, doubles every 1.2 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 24, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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SQL Database  Source

[ NEWS BYTES]

>>
 Cloud Kings: ‘Geostorm’ Film Imagines Human-Controlled Weather – Space.com Under  Cloud

>>
 Greig: Farmer trust key to big data’s future – Manitoba Co-operator Under  Big Data

>>
 How to conduct an IoT pen test | Network World – Network World Under  IOT

More NEWS ? Click Here

[ FEATURED COURSE]

Learning from data: Machine learning course

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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]

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

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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]

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: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]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

#DataScience Approach to Reducing #Employee #Attrition

 #DataScience Approach to Reducing #Employee #Attrition

Subscribe 

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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

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

[  COVER OF THE WEEK ]

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Weak data  Source

[ AnalyticsWeek BYTES]

>> Betting the Enterprise on Data with Cloud-Based Disaster Recovery and Backups by jelaniharper

>> Movie Recommendations? How Does Netflix Do It? A 9 Step Coding & Intuitive Guide Into Collaborative Filtering by nbhaskar

>> Big Data: Career Opportunities Abound in Tech’s Hottest Field by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 The secret to cloud security: elasticity | Healthcare IT News – Healthcare IT News Under  Cloud Security

>>
 Salesforce Introduces Einstein Account-Based Marketing, Built on the World’s #1 CRM Platform – MarTech Series (press release) (blog) Under  Sales Analytics

>>
 Cloud Control: Data Security Hazards and How to Avoid Them – The Legal Intelligencer Under  Data Security

More NEWS ? Click Here

[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… 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: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]

Understanding How Fitness Tracker Works via @STEAMTribe #STEM #STEAM

 Understanding How Fitness Tracker Works via @STEAMTribe #STEM #STEAM

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Getting information off the Internet is like taking a drink from a firehose. – Mitchell Kapor

[ 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]

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

Aug 10, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Pacman  Source

[ AnalyticsWeek BYTES]

>> The New Analytics Professional: Landing A Job In The Big Data Era by analyticsweekpick

>> The Reliability and Validity of the Consumer Financial Protection Bureau (CFPB) Complaint Database by bobehayes

>> Linking Constituency and VoC Metrics by bobehayes

Wanna write? 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

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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]

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 does NLP stand for?
A: * Interaction with human (natural) and computers languages
* Involves natural language understanding

Major tasks:
– Machine translation
– Question answering: “what’s the capital of Canada?”
– Sentiment analysis: extract subjective information from a set of documents, identify trends or public opinions in the social media

– Information retrieval

Source

[ VIDEO OF THE WEEK]

Advanced #Analytics in #Hadoop

 Advanced #Analytics in #Hadoop

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

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[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter