Analytics@ – By For and From Data Science Professionals

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

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[ COVER OF THE WEEK ]

Accuracy Source

[ 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]

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]

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 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]

Unraveling the Mystery of #BigData

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[ 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]

@TimothyChou on World of #IOT & Its #Future Part 2 #FutureOfData #Podcast

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[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter

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Nov 12, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Fake data Source

[ 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:Why is naive Bayes so bad? How would you improve a spam detection algorithm that uses naive Bayes?
A: Naïve: the features are assumed independent/uncorrelated
Assumption not feasible in many cases
Improvement: decorrelate features (covariance matrix into identity matrix)

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[ VIDEO OF THE WEEK]

#BigData #BigOpportunity in Big #HR by @MarcRind #JobsOfFuture #Podcast

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[ 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]

#DataScience Approach to Reducing #Employee #Attrition

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[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

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Nov 05, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[ COVER OF THE WEEK ]

Productivity Source

[ AnalyticsWeek BYTES]

>> Are U.S. Hospitals Delivering a Better Patient Experience? by bobehayes

>> Lessons Big Data Projects Could Learn From Startups by v1shal

>> @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… 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]

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 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

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[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Dr. Nipa Basu, @DnBUS

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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]

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

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Oct 29, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[ COVER OF THE WEEK ]

Data Accuracy Source

[ FEATURED COURSE]

Machine Learning

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]

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

[ 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:Explain Tuftes concept of ‘chart junk’?
A: All visuals elements in charts and graphs that are not necessary to comprehend the information represented, or that distract the viewer from this information

Examples of unnecessary elements include:
– Unnecessary text
– Heavy or dark grid lines
– Ornamented chart axes
– Pictures
– Background
– Unnecessary dimensions
– Elements depicted out of scale to one another
– 3-D simulations in line or bar charts

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