Uncategorized – AnalyticsCube

Jan 14, 21: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://events.analytics.club/tw/eventpull.php?cat=WEB): failed to open stream: HTTP request failed! HTTP/1.0 404 Not Found
in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Data interpretation Source

[ 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:How to clean data?
A: 1. First: detect anomalies and contradictions
Common issues:
* Tidy data: (Hadley Wickam paper)
column names are values, not names, e.g. 26-45
multiple variables are stored in one column, e.g. m1534 (male of 15-34 years old age)
variables are stored in both rows and columns, e.g. tmax, tmin in the same column
multiple types of observational units are stored in the same table. e.g, song dataset and rank dataset in the same table
*a single observational unit is stored in multiple tables (can be combined)
* Data-Type constraints: values in a particular column must be of a particular type: integer, numeric, factor, boolean
* Range constraints: number or dates fall within a certain range. They have minimum/maximum permissible values
* Mandatory constraints: certain columns cant be empty
* Unique constraints: a field must be unique across a dataset: a same person must have a unique SS number
* Set-membership constraints: the values for a columns must come from a set of discrete values or codes: a gender must be female, male
* Regular expression patterns: for example, phone number may be required to have the pattern: (999)999-9999
* Misspellings
* Missing values
* Outliers
* Cross-field validation: certain conditions that utilize multiple fields must hold. For instance, in laboratory medicine: the sum of the different white blood cell must equal to zero (they are all percentages). In hospital database, a patients date or discharge cant be earlier than the admission date
2. Clean the data using:
* Regular expressions: misspellings, regular expression patterns
* KNN-impute and other missing values imputing methods
* Coercing: data-type constraints
* Melting: tidy data issues
* Date/time parsing
* Removing observations

Source

[ VIDEO OF THE WEEK]

@JustinBorgman on Running a data science startup, one decision at a time #Futureofdata #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

What we have is a data glut. Vernon Vinge

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ 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

Jan 07, 21: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://analyticsweek.com/tw/blogpull.php): failed to open stream: HTTP request failed! HTTP/1.0 404 Not Found
in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Statistics 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]

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 Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:What is the difference between supervised learning and unsupervised learning? Give concrete examples
?
A: * Supervised learning: inferring a function from labeled training data
* Supervised learning: predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction)
* Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting
* Supervised learning examples: predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.
* Unsupervised learning: inferring a function to describe hidden structure of unlabeled data
* Unsupervised learning: we lack a response variable that can supervise our analysis
* Unsupervised learning: clustering, principal component analysis, singular value decomposition; identify group of customers
* Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.

Source

[ VIDEO OF THE WEEK]

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

Subscribe to Youtube

[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

iTunes GooglePlay

[ FACT OF THE WEEK]

40% projected growth in global data generated per year vs. 5% growth in global IT spending.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 31, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Statistically Significant Source

[ AnalyticsWeek BYTES]

>> Big corporations suck the marrow out of the COVID-19 economy, leaving devastation behind them by awnewsfeed

>> The Bifurcation of Data Science: Making it Work by jelaniharper

>> Measuring Customer Satisfaction and Loyalty (3rd Edition)! by bobehayes

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]

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]

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]

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

Subscribe to Youtube

[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

@AmyGershkoff on building #winning #DataScience #team #FutureOfData #Podcast

iTunes GooglePlay

[ FACT OF THE WEEK]

2.7 Zetabytes of data exist in the digital universe today.

Sourced from: Analytics.CLUB #WEB Newsletter

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

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Weak data Source

[ AnalyticsWeek BYTES]

>> Janet Amos Pribanic says: âBusiness Analytics â Itâs really OK that itâs not perfect first time out!â by analyticsweek

>> What Analytics Capabilities Do Your Customers Need? Ask These 5 Questions by analyticsweek

>> Meet Us in DC for the 2019 Logi Conference by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ 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]

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: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]

Decision-Making: The Last Mile of Analytics and Visualization

Subscribe to Youtube

[ 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]

#BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

iTunes GooglePlay

[ FACT OF THE WEEK]

Three-quarters of decision-makers (76 per cent) surveyed anticipate significant impacts in the domain of storage systems as a result of the Big Data phenomenon.

Sourced from: Analytics.CLUB #WEB Newsletter

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

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! HTTP/1.0 403 Forbidden
in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Insights Source

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

Analytics with the Chadwick tools, dplyr, and ggplot…. 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]

[ 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]

@TimothyChou on World of #IOT & Its #Future Part 1 #FutureOfData #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

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

[ COVER OF THE WEEK ]

Data security Source

[ AnalyticsWeek BYTES]

>> Data Scientist? Programmer? Are They Mutually Exclusive? by analyticsweek

>> Venu (@VenuV62 | @ProcterGamble) on creating a rockstar data science team #FutureOfData by admin

>> Podcast â How Talend Is Helping Companies Liberate Their Data by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

A practical Python course for beginners with examples and exercises…. 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 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:How do you take millions of users with 100s transactions each, amongst 10ks of products and group the users together in meaningful segments?
A: 1. Some exploratory data analysis (get a first insight)

* Transactions by date
* Count of customers Vs number of items bought
* Total items Vs total basket per customer
* Total items Vs total basket per area

2.Create new features (per customer):

Counts:

* Total baskets (unique days)
* Total items
* Total spent
* Unique product id

Distributions:

* Items per basket
* Spent per basket
* Product id per basket
* Duration between visits
* Product preferences: proportion of items per product cat per basket

3. Too many features, dimension-reduction? PCA?

4. Clustering:

* PCA

5. Interpreting model fit
* View the clustering by principal component axis pairs PC1 Vs PC2, PC2 Vs PC1.
* Interpret each principal component regarding the linear combination its obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)

Source

[ VIDEO OF THE WEEK]

@ReshanRichards on creating a learning startup for preparing for #FutureOfWork #JobsOfFuture #Podcast

Subscribe to Youtube

[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST OF THE WEEK]

Understanding Data Analytics in Information Security with @JayJarome, @BitSight

iTunes GooglePlay

[ 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

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

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Pacman Source

[ FEATURED COURSE]

Tackle Real Data Challenges

Learn scalable data management, evaluate big data technologies, and design effective visualizations…. 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]

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: 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]

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

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Torture the data, and it will confess to anything. Ronald Coase

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with John Young, @Epsilonmktg

iTunes GooglePlay

[ 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

Nov 26, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Convincing Source

[ AnalyticsWeek BYTES]

>> The Future of Big Data [Infographics] by v1shal

>> White paper: Next-gen virtual hardware security module (vHSM) by analyticsweekpick

>> What’s in a Juicebox: Connected Visuals by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

Machine learning (ML) is one of the fastest growing areas of science. It is largely responsible for the rise of giant data companies such as Google, and it has been central to the development of lucrative products, such … more

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

Storytelling with Data teaches you the fundamentals of data visualization and how to communicate effectively with data. You’ll discover the power of storytelling and the way to make data a pivotal point in your story. Th… more

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:What are feature vectors?
A: * n-dimensional vector of numerical features that represent some object
* term occurrences frequencies, pixels of an image etc.
* Feature space: vector space associated with these vectors

Source

[ VIDEO OF THE WEEK]

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

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Without big data, you are blind and deaf and in the middle of a freeway. Geoffrey Moore

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @Beena_Ammanath, @GE

iTunes GooglePlay

[ FACT OF THE WEEK]

Bad data or poor data quality costs US businesses $600 billion annually.

Sourced from: Analytics.CLUB #WEB Newsletter

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

[ COVER OF THE WEEK ]

Pacman Source

[ 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]

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

[ 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]

[ VIDEO OF THE WEEK]

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

Subscribe to Youtube

[ QUOTE OF THE WEEK]

Data really powers everything that we do. Jeff Weiner

[ PODCAST OF THE WEEK]

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

iTunes GooglePlay

[ FACT OF THE WEEK]

Big data is a top business priority and drives enormous opportunity for business improvement. Wikibon’s own study projects that big data will be a $50 billion business by 2017.

Sourced from: Analytics.CLUB #WEB Newsletter

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

Warning: file_get_contents(http://news.analyticsweek.com/tw/newspull.php): failed to open stream: HTTP request failed! in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ COVER OF THE WEEK ]

Ethics Source

[ AnalyticsWeek BYTES]

>> Building Data Models to Empower Self-Service Users by analyticsweekpick

>> How to Use XGBoost for Time Series Forecasting by administrator

>> Startup Styles [Infographic] by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

[ TIPS & TRICKS OF THE WEEK]

[ DATA SCIENCE Q&A]

Q:Is it beneficial to perform dimensionality reduction before fitting an SVM? Why or why not?
A: * When the number of features is large comparing to the number of observations (e.g. document-term matrix)
* SVM will perform better in this reduced space

Source