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

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[  COVER OF THE WEEK ]

image
Weak data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> [Step-by-Step] Using Talend to Bulk Load Data in Snowflake Cloud Data Warehouse by analyticsweekpick

>> Jan 24, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Best & Worst Time for Cold Call by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

image

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

[ FEATURED READ]

The Industries of the Future

image

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]

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:What is random forest? Why is it good?
A: Random forest? (Intuition):
– Underlying principle: several weak learners combined provide a strong learner
– Builds several decision trees on bootstrapped training samples of data
– On each tree, each time a split is considered, a random sample of m predictors is chosen as split candidates, out of all p predictors
– Rule of thumb: at each split m=?p
– Predictions: at the majority rule

Why is it good?
– Very good performance (decorrelates the features)
– Can model non-linear class boundaries
– Generalization error for free: no cross-validation needed, gives an unbiased estimate of the generalization error as the trees is built
– Generates variable importance

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]

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

[ 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]

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

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 11, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Trust the data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Marketing Analytics – Success Through Analysis by analyticsweekpick

>> Closer Than You Think: Data Strategies Across Your Company by analyticsweek

>> CISOs’ newest fear? Criminals with a big data strategy by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

image

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

image

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

[ TIPS & TRICKS OF THE WEEK]

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: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]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

@EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

 @EdwardBoudrot / @Optum on #DesignThinking & #DataDriven Products #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

94% of Hadoop users perform analytics on large volumes of data not possible before; 88% analyze data in greater detail; while 82% can now retain more of their data.

Sourced from: Analytics.CLUB #WEB Newsletter

Jul 04, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Accuracy check  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> The Methods UX Professionals Use (2018) by analyticsweek

>> How Data Science Is Fueling Social Entrepreneurship by analyticsweekpick

>> 10 Visualizations of Juicebox by analyticsweek

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[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

image

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

[ FEATURED READ]

Superintelligence: Paths, Dangers, Strategies

image

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]

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 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]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. – Alvin Tof

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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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

Jun 27, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Assess Your Data Science Expertise by bobehayes

>> Six Practices Critical to Creating Value from Data and Analytics [INFOGRAPHIC] by bobehayes

>> How to Choose a Database for Your Predictive Project by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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The Numpy, Scipy, Pandas, and Matplotlib stack: prep for deep learning, machine learning, and artificial intelligence… more

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

image

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more

[ TIPS & TRICKS OF THE WEEK]

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:Given two fair dices, what is the probability of getting scores that sum to 4? to 8?
A: * Total: 36 combinations
* Of these, 3 involve a score of 4: (1,3), (3,1), (2,2)
* So: 3/36=1/12
* Considering a score of 8: (2,6), (3,5), (4,4), (6,2), (5,3)
* So: 5/36

Source

[ VIDEO OF THE WEEK]

Understanding #Customer Buying Journey with #BigData

 Understanding #Customer Buying Journey with #BigData

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]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe 

iTunes  GooglePlay

[ 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

Jun 20, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Accuracy  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Removing Silos & Operationalizing Your Data: The Key to Analytics – Part 7, Business Intelligence on Big Data/Data Lakes by analyticsweekpick

>> Self-Reported Intentions vs Actual Behaviors: Comparing Two Employee Turnover Metrics by bobehayes

>> Dec 06, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

Wanna write? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

image

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

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

image

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

[ TIPS & TRICKS OF THE WEEK]

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: lift, KPI, robustness, model fitting, design of experiments, 80/20 rule?
A: Lift:
It’s measure of performance of a targeting model (or a rule) at predicting or classifying cases as having an enhanced response (with respect to the population as a whole), measured against a random choice targeting model. Lift is simply: target response/average response.

Suppose a population has an average response rate of 5% (mailing for instance). A certain model (or rule) has identified a segment with a response rate of 20%, then lift=20/5=4

Typically, the modeler seeks to divide the population into quantiles, and rank the quantiles by lift. He can then consider each quantile, and by weighing the predicted response rate against the cost, he can decide to market that quantile or not.
“if we use the probability scores on customers, we can get 60% of the total responders we’d get mailing randomly by only mailing the top 30% of the scored customers”.

KPI:
– Key performance indicator
– A type of performance measurement
– Examples: 0 defects, 10/10 customer satisfaction
– Relies upon a good understanding of what is important to the organization

More examples:

Marketing & Sales:
– New customers acquisition
– Customer attrition
– Revenue (turnover) generated by segments of the customer population
– Often done with a data management platform

IT operations:
– Mean time between failure
– Mean time to repair

Robustness:
– Statistics with good performance even if the underlying distribution is not normal
– Statistics that are not affected by outliers
– A learning algorithm that can reduce the chance of fitting noise is called robust
– Median is a robust measure of central tendency, while mean is not
– Median absolute deviation is also more robust than the standard deviation

Model fitting:
– How well a statistical model fits a set of observations
– Examples: AIC, R2, Kolmogorov-Smirnov test, Chi 2, deviance (glm)

Design of experiments:
The design of any task that aims to describe or explain the variation of information under conditions that are hypothesized to reflect the variation.
In its simplest form, an experiment aims at predicting the outcome by changing the preconditions, the predictors.
– Selection of the suitable predictors and outcomes
– Delivery of the experiment under statistically optimal conditions
– Randomization
– Blocking: an experiment may be conducted with the same equipment to avoid any unwanted variations in the input
– Replication: performing the same combination run more than once, in order to get an estimate for the amount of random error that could be part of the process
– Interaction: when an experiment has 3 or more variables, the situation in which the interaction of two variables on a third is not additive

80/20 rule:
– Pareto principle
– 80% of the effects come from 20% of the causes
– 80% of your sales come from 20% of your clients
– 80% of a company complaints come from 20% of its customers

Source

[ VIDEO OF THE WEEK]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

 @TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

@AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

 @AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

Subscribe 

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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

Jun 13, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Fake data  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ FEATURED COURSE]

CS109 Data Science

image

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]

The Industries of the Future

image

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]

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:Explain likely differences between administrative datasets and datasets gathered from experimental studies. What are likely problems encountered with administrative data? How do experimental methods help alleviate these problems? What problem do they bring?
A: Advantages:
– Cost
– Large coverage of population
– Captures individuals who may not respond to surveys
– Regularly updated, allow consistent time-series to be built-up

Disadvantages:
– Restricted to data collected for administrative purposes (limited to administrative definitions. For instance: incomes of a married couple, not individuals, which can be more useful)
– Lack of researcher control over content
– Missing or erroneous entries
– Quality issues (addresses may not be updated or a postal code is provided only)
– Data privacy issues
– Underdeveloped theories and methods (sampling methods…)

Source

[ VIDEO OF THE WEEK]

#FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

 #FutureOfData with Rob(@telerob) / @ConnellyAgency on running innovation in agency

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 @MPFlowersNYC, @enigma_data

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

YouTube users upload 48 hours of new video every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Jun 06, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data Storage  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Building Data Science AI Teams by @MikeTamir / @UberATG #FutureOfData #Podcast by v1shal

>> Achieving tribal leadership in 5 easy steps by v1shal

>> Data Science and Big Data: Two very Different Beasts by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS109 Data Science

image

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]

Hypothesis Testing: A Visual Introduction To Statistical Significance

image

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:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set?
* In terms of performance on several data sets?
* In terms of efficiency?
In terms of performance on several data sets:

– ‘Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?”
– ‘Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets”, A. Lacoste and F. Laviolette
– ‘Statistical Comparisons of Classifiers over Multiple Data Sets”, Janez Demsar

In terms of performance on a given data set:
– One wants to choose between two learning algorithms
– Need to compare their performances and assess the statistical significance

One approach (Not preferred in the literature):
– Multiple k-fold cross validation: run CV multiple times and take the mean and sd
– You have: algorithm A (mean and sd) and algorithm B (mean and sd)
– Is the difference meaningful? (Paired t-test)

Sign-test (classification context):
Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance.

Wilcoxon signed rank test (classification context):
Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test.

Other (without hypothesis testing):
– AUC
– F-Score

Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

What we have is a data glut. – Vernon Vinge

[ PODCAST OF THE WEEK]

Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

 Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast

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

May 30, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data interpretation  Source

[ AnalyticsWeek BYTES]

>> Succession Planning by the Number [Infographics] by v1shal

>> How to be Data-Driven when Data Economics are Broken by analyticsweekpick

>> Preparing the leaders for #DataDriven #Future @ErikaAndersen by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

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A practical Python course for beginners with examples and exercises…. 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]

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 false positive and a false negative are. Why is it important these from each other? Provide examples when false positives are more important than false negatives, false negatives are more important than false positives and when these two types of errors are equally important
A: * False positive
Improperly reporting the presence of a condition when it’s not in reality. Example: HIV positive test when the patient is actually HIV negative

* False negative
Improperly reporting the absence of a condition when in reality it’s the case. Example: not detecting a disease when the patient has this disease.

When false positives are more important than false negatives:
– In a non-contagious disease, where treatment delay doesn’t have any long-term consequences but the treatment itself is grueling
– HIV test: psychological impact

When false negatives are more important than false positives:
– If early treatment is important for good outcomes
– In quality control: a defective item passes through the cracks!
– Software testing: a test to catch a virus has failed

Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

Big Data is not the new oil. – Jer Thorp

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

Market research firm IDC has released a new forecast that shows the big data market is expected to grow from $3.2 billion in 2010 to $16.9 billion in 2015.

Sourced from: Analytics.CLUB #WEB Newsletter