Uncategorized – Analytics51

Oct 22, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Productivity Source

[ 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: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]

Unraveling the Mystery of #BigData

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[ QUOTE OF THE WEEK]

The most valuable commodity I know of is information. Gordon Gekko

[ PODCAST OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Akamai analyzes 75 million events per day to better target advertisements.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 15, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Ethics Source

[ 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

[ 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:What is A/B testing?
A: * Two-sample hypothesis testing
* Randomized experiments with two variants: A and B
* A: control; B: variation
* User-experience design: identify changes to web pages that increase clicks on a banner
* Current website: control; NULL hypothesis
* New version: variation; alternative hypothesis

Source

[ VIDEO OF THE WEEK]

@ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. Jeff Weiner

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

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[ FACT OF THE WEEK]

In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 08, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[ COVER OF THE WEEK ]

SQL Database Source

[ FEATURED COURSE]

Hadoop Starter Kit

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

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

Antifragile is a standalone book in Nassim Nicholas Talebs landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we dont understand. The… 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:Give examples of data that does not have a Gaussian distribution, nor log-normal?
A: * Allocation of wealth among individuals
* Values of oil reserves among oil fields (many small ones, a small number of large ones)

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

With data collection, the sooner the better’ is always the best answer. Marissa Mayer

[ PODCAST OF THE WEEK]

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

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[ FACT OF THE WEEK]

According to Twitter’s own research in early 2012, it sees roughly 175 million tweets every day, and has more than 465 million accounts.

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 01, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Complex data Source

[ AnalyticsWeek BYTES]

>> Energy companies have more data than they know what to do with by analyticsweekpick

>> The Final Frontier of the Internet of Things: Machine to Machine Action by jelaniharper

>> Biometrics, security and contagion control â Securiport explores the future of the air transport industry by analyticsweekpick

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[ FEATURED COURSE]

A Course in Machine Learning

[ 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]

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

Major tasks:
– Machine translation
– Question answering: whats 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]

RShiny Tutorial: Turning Big Data into Business Applications

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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]

Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

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[ FACT OF THE WEEK]

Brands and organizations on Facebook receive 34,722 Likes every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 24, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Accuracy Source

[ AnalyticsWeek BYTES]

>> Getting to Love: Customer Word Clouds by bobehayes

>> Got Empathy? by bobehayes

>> New open-source Machine Learning Framework written in Java by administrator

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[ FEATURED COURSE]

Artificial Intelligence

This course includes interactive demonstrations which are intended to stimulate interest and to help students gain intuition about how artificial intelligence methods work under a variety of circumstances…. more

[ FEATURED READ]

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

The eagerly anticipated Fourth Edition of the title that pioneered the comparison of qualitative, quantitative, and mixed methods research design is here! For all three approaches, Creswell includes a preliminary conside… more

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q:What is your definition of big data?
A: Big data is high volume, high velocity and/or high variety information assets that require new forms of processing
– Volume: big data doesnt sample, just observes and tracks what happens
– Velocity: big data is often available in real-time
– Variety: big data comes from texts, images, audio, video

Difference big data/business intelligence:
– Business intelligence uses descriptive statistics with data with high density information to measure things, detect trends etc.
– Big data uses inductive statistics (statistical inference) and concepts from non-linear system identification to infer laws (regression, classification, clustering) from large data sets with low density information to reveal relationships and dependencies or to perform prediction of outcomes or behaviors

Source

[ VIDEO OF THE WEEK]

@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

The goal is to turn data into information, and information into insight. Carly Fiorina

[ PODCAST OF THE WEEK]

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

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[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 17, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[ COVER OF THE WEEK ]

SQL Database Source

[ AnalyticsWeek BYTES]

>> Speed and Trust with Azure Synapse Analytics by analyticsweekpick

>> Mastering Deep Learning with Self-Service Data Science for Business Users by jelaniharper

>> UX Says, UI Says by analyticsweekpick

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[ FEATURED COURSE]

Learning from data: Machine learning course

This is an introductory course in machine learning (ML) that covers the basic theory, algorithms, and applications. ML is a key technology in Big Data, and in many financial, medical, commercial, and scientific applicati… more

[ FEATURED READ]

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

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

[ TIPS & TRICKS OF THE WEEK]

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 an outlier? Explain how you might screen for outliers and what would you do if you found them in your dataset. Also, explain what an inlier is and how you might screen for them and what would you do if you found them in your dataset
A: Outliers:
– An observation point that is distant from other observations
– Can occur by chance in any distribution
– Often, they indicate measurement error or a heavy-tailed distribution
– Measurement error: discard them or use robust statistics
– Heavy-tailed distribution: high skewness, cant use tools assuming a normal distribution
– Three-sigma rules (normally distributed data): 1 in 22 observations will differ by twice the standard deviation from the mean
– Three-sigma rules: 1 in 370 observations will differ by three times the standard deviation from the mean

Three-sigma rules example: in a sample of 1000 observations, the presence of up to 5 observations deviating from the mean by more than three times the standard deviation is within the range of what can be expected, being less than twice the expected number and hence within 1 standard deviation of the expected number (Poisson distribution).

If the nature of the distribution is known a priori, it is possible to see if the number of outliers deviate significantly from what can be expected. For a given cutoff (samples fall beyond the cutoff with probability p), the number of outliers can be approximated with a Poisson distribution with lambda=pn. Example: if one takes a normal distribution with a cutoff 3 standard deviations from the mean, p=0.3% and thus we can approximate the number of samples whose deviation exceed 3 sigmas by a Poisson with lambda=3

Identifying outliers:
– No rigid mathematical method
– Subjective exercise: be careful
– Boxplots
– QQ plots (sample quantiles Vs theoretical quantiles)

Handling outliers:
– Depends on the cause
– Retention: when the underlying model is confidently known
– Regression problems: only exclude points which exhibit a large degree of influence on the estimated coefficients (Cooks distance)

Inlier:
– Observation lying within the general distribution of other observed values
– Doesnt perturb the results but are non-conforming and unusual
– Simple example: observation recorded in the wrong unit (°F instead of °C)

Identifying inliers:
– Mahalanobis distance
– Used to calculate the distance between two random vectors
– Difference with Euclidean distance: accounts for correlations
– Discard them

Source

[ VIDEO OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

Unconference Panel Discussion: #Workforce #Analytics Leadership Panel

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[ FACT OF THE WEEK]

IDC Estimates that by 2020,business transactions on the internet- business-to-business and business-to-consumer will reach 450 billion per day.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 10, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Conditional Risk Source

[ AnalyticsWeek BYTES]

>> How Manual vs. Automated Occupancy Counting Makes Or Breaks Your Business by analyticsweekpick

>> From Crisis to Competitive Advantage with Data Catalog Collaboration by analyticsweekpick

>> March 20, 2017 Health and Biotech analytics news roundup by pstein

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[ FEATURED COURSE]

Deep Learning Prerequisites: The Numpy Stack in Python

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

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ TIPS & TRICKS OF THE WEEK]

[ 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]

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

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[ QUOTE OF THE WEEK]

Data matures like wine, applications like fish. James Governor

[ PODCAST OF THE WEEK]

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

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[ FACT OF THE WEEK]

A quarter of decision-makers surveyed predict that data volumes in their companies will rise by more than 60 per cent by the end of 2014, with the average of all respondents anticipating a growth of no less than 42 per cent.

Sourced from: Analytics.CLUB #WEB Newsletter

Sep 03, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Accuracy check Source

[ AnalyticsWeek BYTES]

>> The Four Horsemen of SASE (Secure Access Service Edge) by administrator

>> R now supported in Azure SQL Database by analyticsweekpick

>> Social Media Analytics – What to Measure for Success? by thomassujain

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[ FEATURED COURSE]

Artificial Intelligence

[ 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]

[ DATA SCIENCE Q&A]

Q:How would you define and measure the predictive power of a metric?
A: * Predictive power of a metric: the accuracy of a metrics success at predicting the empirical
* They are all domain specific
* Example: in field like manufacturing, failure rates of tools are easily observable. A metric can be trained and the success can be easily measured as the deviation over time from the observed
* In information security: if the metric says that an attack is coming and one should do X. Did the recommendation stop the attack or the attack never happened?

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

He uses statistics as a drunken man uses lamp postsfor support rather than for illumination. Andrew Lang

[ PODCAST OF THE WEEK]

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

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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 27, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data analyst Source

[ AnalyticsWeek BYTES]

>> Getting Started with Predictive Analytics in Construction by analyticsweekpick

>> Predictive Analytics in Action: 5 Industry Examples by analyticsweek

>> How to integrate big data In security systems? by administrator

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[ 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]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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

[ TIPS & TRICKS OF THE WEEK]

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

[ DATA SCIENCE Q&A]

Q: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:
Its 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]

Understanding #Customer Buying Journey with #BigData

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. Todd Papaioannou

[ PODCAST OF THE WEEK]

@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

Facebook users send on average 31.25 million messages and view 2.77 million videos every minute.

Sourced from: Analytics.CLUB #WEB Newsletter

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

[ COVER OF THE WEEK ]

Data interpretation Source

[ AnalyticsWeek BYTES]

>> 6 Cool Companies Who Are Rethinking How We Work by analyticsweek

>> Processing Data like a Pro(fessional Data Scientist) by michael-li

>> Analytics Hall of Fame: Voices of the Famous Interview Series: What constitutes an Analytics Leader? The importance of Business Problem Framing. by tony

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[ FEATURED COURSE]

Learning from data: Machine learning course

[ 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]

[ 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 its based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

Harsh Tiwari talks about fabric of data driven leader in Financial Sector #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

iTunes GooglePlay

[ 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.