Sep 06, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Complex data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> On Ingesting Kate Crawford’s “The Trouble with Bias” by analyticsweek

>> Customer Loyalty 2.0 Article in Quirk’s Marketing Research Review by bobehayes

>> Traditional Retailers, It’s 15 Minutes to midnight. How to Strike Back at Your Online Competitors. #Retail Transformation, #Retail Analytics by tony

Wanna write? Click Here

[ NEWS BYTES]

>>
 Mass Incarceration’s Complex Statistics – CityLab Under  Statistics

>>
 Most hospitals won’t reach HIMSS Analytics Stage 7 until 2035, study finds – Becker’s Hospital Review Under  Analytics

>>
 Elder abuse statistics are state’s worst for the ‘quiet epidemic’ – Staunton News Leader Under  Statistics

More NEWS ? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

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Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

image

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]

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

[ DATA SCIENCE Q&A]

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

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

2.Create new features (per customer):

Counts:

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

Distributions:

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

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

4. Clustering:

* PCA

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

Source

[ VIDEO OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

According to execs, the influx of data is putting a strain on IT infrastructure. 55 percent of respondents reporting a slowdown of IT systems and 47 percent citing data security problems, according to a global survey from Avanade.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 30, 18: #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]

>> How Big Data And The Internet Of Things Improve Public Transport In London by analyticsweekpick

>> Startup vs. Corporation, IT disrupts the way we conduct business by martin

>> Audience segment management, audience sharing and data integration with dedicated marketing cloud software by thomassujain

Wanna write? Click Here

[ NEWS BYTES]

>>
 HR Can Deconstruct Bias In The Workplace With Machine Learning – Forbes Under  Machine Learning

>>
 Does a Soft Sell Conflict With Content Marketing Analytics? – Business 2 Community Under  Marketing Analytics

>>
 SEC ‘Forces Innovation’ With New Data Science Program and a Massive IT Contract – Nextgov Under  Data Science

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]

Thinking, Fast and Slow

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Drawing on decades of research in psychology that resulted in a Nobel Prize in Economic Sciences, Daniel Kahneman takes readers on an exploration of what influences thought example by example, sometimes with unlikely wor… more

[ TIPS & TRICKS OF THE WEEK]

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 Q&A]

Q:How do you handle missing data? What imputation techniques do you recommend?
A: * If data missing at random: deletion has no bias effect, but decreases the power of the analysis by decreasing the effective sample size
* Recommended: Knn imputation, Gaussian mixture imputation

Source

[ VIDEO OF THE WEEK]

Reimagining the role of data in government

 Reimagining the role of data in government

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[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

Ashok Srivastava(@aerotrekker @intuit) on Winning the Art of #DataScience #FutureOfData #Podcast

 Ashok Srivastava(@aerotrekker @intuit) on Winning the Art of #DataScience #FutureOfData #Podcast

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[ FACT OF THE WEEK]

2.7 Zetabytes of data exist in the digital universe today.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 23, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Analyzing Big Data: A Customer-Centric Approach by bobehayes

>> Big Data: The Management Revolution – Harvard Business Review by v1shal

>> 2018 Trends in Machine Learning: Intelligent Decision-Making by jelaniharper

Wanna write? Click Here

[ NEWS BYTES]

>>
 Coping With Big Data And Big Influence – Forbes Under  Big Data

>>
 Nvidia rides the next wave in artificial intelligence with Google … – MarketWatch Under  Artificial Intelligence

>>
 Adam Brown Social Media Command Center Announces Partnership to Form Social Analytics Consortium – UT Knoxville News (press release) Under  Social 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]

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]

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:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: “cherry picking”, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it

Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as it’s based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

Unraveling the Mystery of #BigData

 Unraveling the Mystery of #BigData

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. – Atul Butte, Stanford

[ PODCAST OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe 

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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

Aug 16, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Ethics  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> How Retailer Should Use QR Code To Hit The Pot Of Gold by v1shal

>> Jun 21, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> A Super Scary Halloween Story for Data Scientists and other Change Agents by groumeliotis

Wanna write? Click Here

[ NEWS BYTES]

>>
 FCC Deploys Cloud-Ready Security for a Cloud-First World – MeriTalk (blog) Under  Cloud Security

>>
 Mudavanhu takes over as CBZ group CEO – Chronicle Under  Risk Analytics

>>
 Google’s artificial intelligence ethics won’t curb war by algorithm – Wired.co.uk Under  Artificial Intelligence

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]

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]

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: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]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

235 Terabytes of data has been collected by the U.S. Library of Congress in April 2011.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 09, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Productivity  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Automating Intelligent Data Governance by jelaniharper

>> One Thing You Need To Know About Statistics. Maybe Two by bobehayes

>> Build a Mobile Gaming Events Data Pipeline with Databricks Delta by analyticsweek

Wanna write? Click Here

[ NEWS BYTES]

>>
 Why It’s Time For Retail To Stop Experimenting With IoT, And Start Implementing It – PYMNTS.com Under  Internet Of Things

>>
 Orderly Conquers Restaurant Inventory and Food Cost Management Woes Using Data Science – Total Food Service Under  Data Science

>>
 Top 10 Disruptive Companies to Watch in the Healthcare Space – Health IT Analytics Under  Health Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. 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]

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

[ DATA SCIENCE Q&A]

Q:How to clean data?
A: 1. First: detect anomalies and contradictions
Common issues:
* Tidy data: (Hadley Wickam paper)
column names are values, not names, e.g. 26-45…
multiple variables are stored in one column, e.g. m1534 (male of 15-34 years’ old age)
variables are stored in both rows and columns, e.g. tmax, tmin in the same column
multiple types of observational units are stored in the same table. e.g, song dataset and rank dataset in the same table
*a single observational unit is stored in multiple tables (can be combined)
* Data-Type constraints: values in a particular column must be of a particular type: integer, numeric, factor, boolean
* Range constraints: number or dates fall within a certain range. They have minimum/maximum permissible values
* Mandatory constraints: certain columns can’t be empty
* Unique constraints: a field must be unique across a dataset: a same person must have a unique SS number
* Set-membership constraints: the values for a columns must come from a set of discrete values or codes: a gender must be female, male
* Regular expression patterns: for example, phone number may be required to have the pattern: (999)999-9999
* Misspellings
* Missing values
* Outliers
* Cross-field validation: certain conditions that utilize multiple fields must hold. For instance, in laboratory medicine: the sum of the different white blood cell must equal to zero (they are all percentages). In hospital database, a patient’s date or discharge can’t be earlier than the admission date
2. Clean the data using:
* Regular expressions: misspellings, regular expression patterns
* KNN-impute and other missing values imputing methods
* Coercing: data-type constraints
* Melting: tidy data issues
* Date/time parsing
* Removing observations

Source

[ VIDEO OF THE WEEK]

@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

 @JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The data fabric is the next middleware. – Todd Papaioannou

[ PODCAST OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

235 Terabytes of data has been collected by the U.S. Library of Congress in April 2011.

Sourced from: Analytics.CLUB #WEB Newsletter

Aug 02, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Productivity  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Dec 28, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Meet the Robot Reading Your Resume [infographics] by v1shal

>> What Insurance companies could do to save others from eating their lunch by d3eksha

Wanna write? Click Here

[ NEWS BYTES]

>>
 Module 1: Your employee value proposition- Making every moment count – People Matters Under  Talent Analytics

>>
 Lots of data science jobs, not enough workers. TransUnion hopes new UIC professorship can help change that. – Chicago Tribune Under  Data Science

>>
 Big data in agriculture focus of Houston conference Aug. 20-21 – AgriLife Today Under  Big Data

More NEWS ? Click Here

[ FEATURED COURSE]

Master Statistics with R

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In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… 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]

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 handle missing data? What imputation techniques do you recommend?
A: * If data missing at random: deletion has no bias effect, but decreases the power of the analysis by decreasing the effective sample size
* Recommended: Knn imputation, Gaussian mixture imputation

Source

[ VIDEO OF THE WEEK]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

What we have is a data glut. – Vernon Vinge

[ 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]

More than 200bn HD movies – which would take a person 47m years to watch.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 26, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Trust the data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> @ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData by v1shal

>> Malaysia opens digital government lab for big data analytics by analyticsweekpick

>> Customer centric fix to save Indian Maharaja (Air India) from financial mess by v1shal

Wanna write? Click Here

[ NEWS BYTES]

>>
 University of Akron’s College of Business Administration to build $5.2 million Professional Development Center – Crain’s Cleveland Business Under  Business Analytics

>>
 Data Center Virtualization Market Top Manufactures, Challenges And Drivers By 2026 – Coherent Chronicle (press release) (blog) Under  Virtualization

>>
 Google and Coursera launch a new machine learning specialization – TechCrunch Under  Machine Learning

More NEWS ? Click Here

[ FEATURED COURSE]

Applied Data Science: An Introduction

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As the world’s data grow exponentially, organizations across all sectors, including government and not-for-profit, need to understand, manage and use big, complex data sets—known as big data…. more

[ FEATURED READ]

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

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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 Q&A]

Q:Give examples of bad and good visualizations?
A: Bad visualization:
– Pie charts: difficult to make comparisons between items when area is used, especially when there are lots of items
– Color choice for classes: abundant use of red, orange and blue. Readers can think that the colors could mean good (blue) versus bad (orange and red) whereas these are just associated with a specific segment
– 3D charts: can distort perception and therefore skew data
– Using a solid line in a line chart: dashed and dotted lines can be distracting

Good visualization:
– Heat map with a single color: some colors stand out more than others, giving more weight to that data. A single color with varying shades show the intensity better
– Adding a trend line (regression line) to a scatter plot help the reader highlighting trends

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

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]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe 

iTunes  GooglePlay

[ 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

Jul 19, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Correlation-Causation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> After trying its own data center, Zynga retreats to the cloud by analyticsweekpick

>> Measuring Customer Loyalty is Essential for a Successful CEM Program by bobehayes

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

Wanna write? Click Here

[ NEWS BYTES]

>>
 New ETF Targets AI, Big Data – ETF.com Under  Big Data

>>
 Sydney Uni harvests big data to boost crops – iTnews Under  Big Data

>>
 The Next Generation of DevOps: ML Ops – insideBIGDATA Under  Data Science

More NEWS ? Click Here

[ 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]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

image

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]

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 are the drawbacks of linear model? Are you familiar with alternatives (Lasso, ridge regression)?
A: * Assumption of linearity of the errors
* Can’t be used for count outcomes, binary outcomes
* Can’t vary model flexibility: overfitting problems
* Alternatives: see question 4 about regularization

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]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @MPFlowersNYC, @enigma_data

Subscribe 

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[ FACT OF THE WEEK]

The Hadoop (open source software for distributed computing) market is forecast to grow at a compound annual growth rate 58% surpassing $1 billion by 2020.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 12, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Productivity  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Follow the Money: The Demand for Deep Learning by jelaniharper

>> Apache Spark for Big Analytics by thomaswdinsmore

>> The Horizontal Impact of Advanced Machine Learning: Network Optimization by jelaniharper

Wanna write? Click Here

[ NEWS BYTES]

>>
 The Next Step for Hybrid Cloud in Infrastructure and Operations – CIOReview Under  Hybrid Cloud

>>
 The Four C’s Of Winning With IoT – Forbes Under  IOT

>>
 Pioneering data science tool — Jupyter — receives top software prize – UC Berkeley Under  Data Science

More NEWS ? Click Here

[ FEATURED COURSE]

Master Statistics with R

image

In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… 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]

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: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]

Venu Vasudevan @VenuV62 (@ProcterGamble) on creating a rockstar data science team #FutureOfData #Podcast

 Venu Vasudevan @VenuV62 (@ProcterGamble) on creating a rockstar data science team #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Data are becoming the new raw material of business. – Craig Mundie

[ PODCAST OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

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[ 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

Jul 05, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 Hybrid cloud deployment demands a change in security mindset – TechTarget Under  Cloud Security

>>
 PTC CEO: IoT Growth Shows It’s A ‘Major Strategic Differentiator For Industrial Companies’ – CRN Under  IOT

>>
 Top 5 Data Center Stories of the Week: Jun 30 2018 – Data Center Knowledge Under  Data Center

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]

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 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: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]

RShiny Tutorial: Turning Big Data into Business Applications

 RShiny Tutorial: Turning Big Data into Business Applications

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[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

@AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

 @AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Walmart handles more than 1 million customer transactions every hour, which is imported into databases estimated to contain more than 2.5 petabytes of data.

Sourced from: Analytics.CLUB #WEB Newsletter