Uncategorized – Analytics51

Jan 16, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Insights 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: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]

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

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[ QUOTE OF THE WEEK]

The temptation to form premature theories upon insufficient data is the bane of our profession. – Sherlock Holmes

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Jan 09, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Statistics Source

[ AnalyticsWeek BYTES]

>> October 30, 2017 Health and Biotech analytics news roundup by pstein

>> 9th edition of Aegis Graham Bell Award nominations open by administrator

>> Logi is Named #1 in Embedded Business Intelligence in The BI Survey 19 by analyticsweek

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[ FEATURED COURSE]

The Analytics Edge

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]

On Intelligence

Jeff Hawkins, the man who created the PalmPilot, Treo smart phone, and other handheld devices, has reshaped our relationship to computers. Now he stands ready to revolutionize both neuroscience and computing in one strok… 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: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]

Using Topological Data Analysis on your BigData

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[ QUOTE OF THE WEEK]

I keep saying that the sexy job in the next 10 years will be statisticians. And I’m not kidding. Hal Varian

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

Every person in the US tweeting three tweets per minute for 26,976 years.

Sourced from: Analytics.CLUB #WEB Newsletter

Jan 02, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Weak data Source

[ AnalyticsWeek BYTES]

>> Biased Machine-Learning Models: How to Recognize and Avoid Them by analyticsweek

>> Hitting the Road to BigData Analytics and Discovery by v1shal

>> Explaining Data Teams to HR by analyticsweek

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[ 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]

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 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:Compare R and Python
A: R
– Focuses on better, user friendly data analysis, statistics and graphical models
– The closer you are to statistics, data science and research, the more you might prefer R
– Statistical models can be written with only a few lines in R
– The same piece of functionality can be written in several ways in R
– Mainly used for standalone computing or analysis on individual servers
– Large number of packages, for anything!

Python
– Used by programmers that want to delve into data science
– The closer you are working in an engineering environment, the more you might prefer Python
– Coding and debugging is easier mainly because of the nice syntax
– Any piece of functionality is always written the same way in Python
– When data analysis needs to be implemented with web apps
– Good tool to implement algorithms for production use

Source

[ VIDEO OF THE WEEK]

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

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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]

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

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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

Dec 26, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data interpretation Source

[ AnalyticsWeek BYTES]

>> @ReshanRichards on creating a learning startup for preparing for #jobsoffuture #podcast by admin

>> PHP Exceeds the Generic Human Expectations. Hereâs how the Brand got it Done by thomassujain

>> Guide to business intelligence and health IT analytics by analyticsweekpick

Wanna write? Click Here

[ 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]

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]

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:What is star schema? Lookup tables?
A: The star schema is a traditional database schema with a central (fact) table (the observations, with database keys for joining with satellite tables, and with several fields encoded as IDs). Satellite tables map IDs to physical name or description and can be joined to the central fact table using the ID fields; these tables are known as lookup tables, and are particularly useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve multiple layers of summarization (summary tables, from granular to less granular) to retrieve information faster.

Lookup tables:
– Array that replace runtime computations with a simpler array indexing operation

Source

[ VIDEO OF THE WEEK]

Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

Information is the oil of the 21st century, and analytics is the combustion engine. Peter Sondergaard

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

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

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[ COVER OF THE WEEK ]

Conditional Risk Source

[ AnalyticsWeek BYTES]

>> June 5, 2017 Health and Biotech analytics news roundup by pstein

>> Make Every Sprint Count with DevOps Analytics by analyticsweek

>> [Step-by-step] Using Talend for cloud-to-cloud deployments and faster analytics in Snowflake by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more

[ FEATURED READ]

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

Ray Kurzweil is arguably today’s most influentialand often controversialfuturist. In How to Create a Mind, Kurzweil presents a provocative exploration of the most important project in human-machine civilizationreverse… more

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:You have data on the durations of calls to a call center. Generate a plan for how you would code and analyze these data. Explain a plausible scenario for what the distribution of these durations might look like. How could you test, even graphically, whether your expectations are borne out?
A: 1. Exploratory data analysis
* Histogram of durations
* histogram of durations per service type, per day of week, per hours of day (durations can be systematically longer from 10am to 1pm for instance), per employee
2. Distribution: lognormal?

3. Test graphically with QQ plot: sample quantiles of log(durations)log?(durations) Vs normal quantiles

Source

[ VIDEO OF THE WEEK]

Agile Data Warehouse Design for Big Data Presentation

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[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

@chrisbishop on futurist’s lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

Data is growing faster than ever before and by the year 2020, about 1.7 megabytes of new information will be created every second for every human being on the planet.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 12, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Complex data Source

[ AnalyticsWeek BYTES]

>> Clarifying Employee Engagement: A Review of Four Employee Engagement Measures by bobehayes

>> Do Attitudes Predict Behavior? by analyticsweek

>> Using Analytics To Counter Early Claim Fraud by analyticsweek

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[ FEATURED COURSE]

Python for Beginners with Examples

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

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 do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
A: * Effect would be similar to regularization: avoid overfitting
* Used to increase robustness

Source

[ VIDEO OF THE WEEK]

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

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. Todd Papaioannou

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

Facebook stores, accesses, and analyzes 30+ Petabytes of user generated data.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 05, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data Storage Source

[ AnalyticsWeek BYTES]

>> Making the Bet on Open Source by analyticsweekpick

>> Tutorial: Azure Data Lake analytics with R by analyticsweek

>> Accelerating Discovery with a Unified Analytics Platform for Genomics by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

CS109 Data Science

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 Future of the Professions: How Technology Will Transform the Work of Human Experts

This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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: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 its 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 its 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 doesnt 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

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[ QUOTE OF THE WEEK]

What we have is a data glut. Vernon Vinge

[ PODCAST OF THE WEEK]

George (@RedPointCTO / @RedPointGlobal) on becoming an unbiased #Technologist in #DataDriven World #FutureOfData #Podcast

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

Nov 28, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Data shortage Source

[ FEATURED COURSE]

Applied Data Science: An Introduction

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 setsknown as big data…. more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

[ 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: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]

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

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

By then, our accumulated digital universe of data will grow from 4.4 zettabyets today to around 44 zettabytes, or 44 trillion gigabytes.

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 21, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Pacman Source

[ AnalyticsWeek BYTES]

>> Focus on success, not perfection: Look at this data science algorithm for inspiration by analyticsweekpick

>> List of VC firms in Boston by v1shal

>> Using Big Data In A Crisis: Nepal Earthquake by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

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

[ FEATURED READ]

Rise of the Robots: Technology and the Threat of a Jobless Future

What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… more

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:What is: lift, KPI, robustness, model fitting, design of experiments, 80/20 rule?
A: Lift:
Its 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 wed 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]

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

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[ QUOTE OF THE WEEK]

The temptation to form premature theories upon insufficient data is the bane of our profession. – Sherlock Holmes

[ PODCAST OF THE WEEK]

Want to fix #DataScience ? fix #governance by @StephenGatchell @Dell #FutureOfData #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

By 2020, at least a third of all data will pass through the cloud (a network of servers connected over the Internet).

Sourced from: Analytics.CLUB #WEB Newsletter

Nov 14, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Insights Source

[ AnalyticsWeek BYTES]

>> Big Data Introduction to D3 by v1shal

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

>> Exploring the Structure of High-Dimensional Data with HyperTools in Kaggle Kernels by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

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

Illuminating and very timely . . . a fascinating and sometimes alarming survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:What is the maximal margin classifier? How this margin can be achieved?
A: * When the data can be perfectly separated using a hyperplane, there actually exists an infinite number of these hyperplanes
* Intuition: a hyperplane can usually be shifted a tiny bit up, or down, or rotated, without coming into contact with any of the observations
* Large margin classifier: choosing the hyperplance that is farthest from the training observations
* This margin can be achieved using support vectors

Source