October 2020 – Analytics51

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

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[ COVER OF THE WEEK ]

Correlation-Causation 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: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]

@CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

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[ QUOTE OF THE WEEK]

If you can’t explain it simply, you don’t understand it well enough. – Albert Einstein

[ PODCAST OF THE WEEK]

@Schmarzo @DellEMC on Ingredients of healthy #DataScience practice #FutureOfData #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

The largest AT&T database boasts titles including the largest volume of data in one unique database (312 terabytes) and the second largest number of rows in a unique database (1.9 trillion), which comprises AT&T’s extensive calling records.

Sourced from: Analytics.CLUB #WEB Newsletter

Originally Posted at: by

Originally Posted at:

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

[ COVER OF THE WEEK ]

Productivity Source

[ AnalyticsWeek BYTES]

>> Embedded Analytics in Manufacturing: The Real Revolution [Guest Post] by analyticsweek

>> Global Payments signs cloud deal with AWS by analyticsweekpick

>> Big Data: The Next Revolution of Sport 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]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

[ 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

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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

iTunes GooglePlay

[ 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

Originally Posted at: by

The Last Layer of Cyber Security: Business Continuity and Disaster Recovery with Incremental Backups

Due to burgeoning regulatory penalties and a seemingly interminable amount of threats, cyber security is a foremost concern for the contemporary enterprise.

Oftentimes, the most dependable protection involves combining methods and technologies to preserve the integrity of IT systems and their data. In this regard, an organizationâs security stack is often as important as its technology stack.

According to BackupAssist Chief Executive Officer Linus Chang, there is considerable advantage to topping the former with reliable, systematic backups to ensure business continuity.

âA few years ago, people were saying that backup is dead, people are moving to the cloud, itâs all about high availability and so on,â Chang mentioned. âBut these new regulations, and especially the onset of crypto ransomware, has really brought back the spotlight for having multiple layers of protection for data. Thereâs a lot of interest in keeping historical versions of data, and having backups as the last layer of protection should perimeter security fail.â

Ransomware
The advent of cryptographic ransomware makes for a compelling use case to preserve backup copies of information assets, and serves to highlight the various areas in which business continuity impacts the enterprise. Business continuity involves elements of cyber security, network availability, organizational risk, and disaster recoveryâmeaning that timely backups are assistive in each of these areas as well. Chang referenced an occurrence in which users âwere struck by ransomware and didnât have a proper backup system. They never got the data back and had to re-key in six weeks of data. Having backups would have minimized that to two, three hours maximum.â Modern backup systems are able to address instances of ransomware (and other types of malware) in three ways:

ProtectionâProtective capabilities can help ward off ransomware or malware attacks.
DetectionâScanning mechanisms enable the detection of any sort of large scale corruption or file system modifications typical of ransomware. Such routine scanning is a component of the backup process.
ResponseâOnce ransomware or infected files are detected, backup jobs are ceased and prevented from running on those files. Alerts are sent to users notifying them of infected files.

High Availability
Network availability is an integral aspect of business continuity, particularly in the event of security breaches or disaster recovery. Nonetheless, there are a few key differences between conventional high availability methods (which frequently involve redundancy and cloud failover capabilities) and those provided by timely backups. According to Chang, âHigh availability is about minimizing downtime. Commonly you would say three nines [99.9%], four nines [99.99%], five nines [99.999%] of availability. Over the course of a year you might be down for 20 or 30 minutes.â Backing up data can also decrease downtime in certain network failure events. Still, backups issue benefits in addition to availability. âBackup is about being able to restore whole systems to get back historical data,â Chang noted. âHigh availability only talks about the current version of data. It doesnât talk about being able to pull something back from two years ago.â Thus, when prompted by regulators or legal discovery measures for data companies may have possessed years ago, data backupsânot high availabilityâprovide ideal solutions. As such, backups are necessary for business continuity. âBusiness continuityâs all about the business and do they have the data when they need it,â Chang explained.

Flexibility
Viewed from a business continuity perspective, backups require a degree of flexibility to serve an ever evolving ecosystem of enterprise data needs. Cloud backups are usually the most common variety of backups deployed. However, there are certain situations in which backing up data to local, physical storage (typically on disk) is much more preferable to cloud backups. âIf youâve got small data sets, then absolutely itâs more feasible to put that data in the cloud,â Chang said. âWhen youâve got large datasets and complete servers that you need to get up after a disaster, you need to bring it back up for full metal disaster recovery, then itâs always faster to do that when itâs stored on a hard disk at your local office. Imagine downloading terabytes of data from the cloud. Itâs just too slow.â Other backup options involve cold storage backups, in which copies of data are âdisconnected from the computer and the network,â Chang commented. Best practices include storing backups in multiple locations, utilizing cold storage, and leveraging ubiquitous file formats as opposed to proprietary, vendor formats that are difficult to access once versions of software or files have progressed.

Secure Implementations
In addition to investing in measures to fortify perimeter security, itâs becoming more and more necessary to preserve data with backups for any variety of use cases. Doing so is instrumental to business continuity in all of its facets, which include high availability, risk, cyber security, and disaster recovery.

Source by jelaniharper

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