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**Productivity** Source

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**>>** Important Strategies to Enhance Big Data Access by thomassujain

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NTT Com plans to invest over $160 million for data center expansion in India – ETCIO.com Under Data Center

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Goergen Institute for Data Science provides new opportunities for … – University of Rochester Newsroom Under Data Science

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Hints of iPhone 8 Showing Up in Web Analytics – Mac Rumors Under Analytics

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### [ 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: 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**

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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

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Using Analytics to build A #BigData #Workforce

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### [ FACT OF THE WEEK]

39 percent of marketers say that their data is collected ‘too infrequently or not real-time enough.’