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[  COVER OF THE WEEK ]

Data interpretation  Source

[ AnalyticsWeek BYTES]

>> Why Using the ‘Cloud’ Can Undermine Data Protections by analyticsweekpick

>> Leveraging Social Media to Showcase Your Expertise [Infographic] by v1shal

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

Wanna write? Click Here

[ NEWS BYTES]

>>
 Delta Risk CEO Scott Kaine Featured on “Insights & Intelligence” Cloud Security Podcast – Security Boulevard Under  Cloud Security

>>
 Delta Risk CEO Scott Kaine Featured on “Insights & Intelligence” Cloud Security Podcast – Security Boulevard Under  Cloud Security

>>
 Delta Risk CEO Scott Kaine Featured on “Insights & Intelligence” Cloud Security Podcast – Security Boulevard Under  Cloud 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]

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

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 control for biases?
A: * Choose a representative sample, preferably by a random method
* Choose an adequate size of sample
* Identify all confounding factors if possible
* Identify sources of bias and include them as additional predictors in statistical analyses
* Use randomization: by randomly recruiting or assigning subjects in a study, all our experimental groups have an equal chance of being influenced by the same bias

Notes:
– Randomization: in randomized control trials, research participants are assigned by chance, rather than by choice to either the experimental group or the control group.
– Random sampling: obtaining data that is representative of the population of interest

Source

[ VIDEO OF THE WEEK]

 Decision-Making: The Last Mile of Analytics and Visualization

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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]

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

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[ FACT OF THE WEEK]

For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Tour of Accounting  Source

[ AnalyticsWeek BYTES]

>> 3 Emerging Big Data Careers in an IoT-Focused World by kmartin

>> October 24, 2016 Health and Biotech analytics news roundup by pstein

>> Feb 14, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ NEWS BYTES]

>>
 Statistics Canada hits pause on plan to obtain banking records, halts TransUnion credit requests – Globalnews.ca Under  Statistics

>>
 Chubb launches terrorism risk service for multinational businesses … – Insurance Business Under  Risk Analytics

>>
 IIM Calcutta’s Business Analytics Programme Ranks 14 In World Ranking – NDTV Under  Business Analytics

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]

Superintelligence: Paths, Dangers, Strategies

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

[ TIPS & TRICKS OF THE WEEK]

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:What is root cause analysis? How to identify a cause vs. a correlation? Give examples
A: Root cause analysis:
– Method of problem solving used for identifying the root causes or faults of a problem
– A factor is considered a root cause if removal of it prevents the final undesirable event from recurring

Identify a cause vs. a correlation:
– Correlation: statistical measure that describes the size and direction of a relationship between two or more variables. A correlation between two variables doesn’t imply that the change in one variable is the cause of the change in the values of the other variable
– Causation: indicates that one event is the result of the occurrence of the other event; there is a causal relationship between the two events
– Differences between the two types of relationships are easy to identify, but establishing a cause and effect is difficult

Example: sleeping with one’s shoes on is strongly correlated with waking up with a headache. Correlation-implies-causation fallacy: therefore, sleeping with one’s shoes causes headache.
More plausible explanation: both are caused by a third factor: going to bed drunk.

Identify a cause Vs a correlation: use of a controlled study
– In medical research, one group may receive a placebo (control) while the other receives a treatment If the two groups have noticeably different outcomes, the different experiences may have caused the different outcomes

Source

[ VIDEO OF THE WEEK]

 @CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

 Pascal Marmier (@pmarmier) @SwissRe discusses running data driven innovation catalyst

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[ FACT OF THE WEEK]

Every second we create new data. For example, we perform 40,000 search queries every second (on Google alone), which makes it 3.5 searches per day and 1.2 trillion searches per year.In Aug 2015, over 1 billion people used Facebook FB +0.54% in a single day.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Convincing  Source

[ AnalyticsWeek BYTES]

>> Matt Ward(@itsmattward) on #FutureOfJobs and #startups in #eCommence #JobsOfFuture #Podcast by v1shal

>> Why Is Big Data Is So Big In Health Care? by analyticsweek

>> What Crying Baby Could Teach Big Data Discovery Solution Seekers? by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 Prescriptive and Predictive Analytics Market by 2025 Overview and Forecast by Consumption, Trend, Vendors, Types … – Business News Under  Talent Analytics

>>
 Who’s afraid of the big, bad hybrid cloud? – ITProPortal Under  Hybrid Cloud

>>
 Adobe Analytics: Black Friday hit $6.2 billion in online sales – WNCT Under  Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Intro to Machine Learning

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 Black Swan: The Impact of the Highly Improbable

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]

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: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]

 Using Analytics to build A #BigData #Workforce

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[ 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 John Young, @Epsilonmktg

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[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

• Mar 20, 2019 #WEB Analytical Methods Validation for FDA Compliance
• Feb 20, 2019 #WEB Thinkful Webinar | Getting Started in Data Science
• Feb 27, 2019 #WEB Webinar – Implementing AI applications with Power BI

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Battling Misinformation in Customer Experience Management by bobehayes

>> RSPB Conservation Efforts Take Flight Thanks To Data Analytics by analyticsweekpick

>> @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 North America Quality Management In Healthcare Market Growth Analysis, and Forecast Including Factors … – Industry Strategy Under  Health Analytics

>>
 New Orioles GM Mike Elias brings former Astros analytics chief Sig Mejdal along to Baltimore – Baltimore Sun Under  Analytics

>>
 Nutanix Joins IoT And Edge Computing Bandwagon With Xi IoT Platform – Forbes Under  IOT

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]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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: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]

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

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

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[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

[  COVER OF THE WEEK ]

Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

• Feb 22, 2019 #WEB Live-Webinar “Orientierung im Big Data Dschungel” mit Inga Ketels
• Feb 21, 2019 #WEB [REPLAY] How To Design a Patient-Centered Mobile Health App
• Feb 21, 2019 #WEB UKAT Research Showcase Webinar – Knowing you: Learning Analytics, the Johari Window and Personal Tutoring

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Global Business By The Big Analytics – Playcast – Data Analytics Leadership Playbook Podcast by v1shal

>> Jason Carmel ( @defenestrate99 / @possible ) Leading Analytics, Data, Digital & Marketing by v1shal

>> It’s Official! Talend to Welcome Stitch to the Family! by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Big Data Made Simple – insideBIGDATA Under  Big Data

>>
 India cannot afford to ignore Data Science – Economic Times (blog) Under  Data Science

>>
 Have you heard? Hybrid cloud is the ideal IT model – Information Age Under  Hybrid Cloud

More NEWS ? Click Here

[ FEATURED COURSE]

Intro to Machine Learning

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 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]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:How to detect individual paid accounts shared by multiple users?
A: * Check geographical region: Friday morning a log in from Paris and Friday evening a log in from Tokyo
* Bandwidth consumption: if a user goes over some high limit
* Counter of live sessions: if they have 100 sessions per day (4 times per hour) that seems more than one person can do

Source

[ VIDEO OF THE WEEK]

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

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 @Beena_Ammanath, @GE

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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