AnalyticsCube – Blogs Sourced from Data Science Experts

Nov 26, 20: #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

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

[ 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 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

Source by

Originally Posted at:

Nov 19, 20: #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! HTTP/1.0 403 Forbidden
in /home3/vishaltao/public_html/mytao/script/includeit.php on line 15

[ 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

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]

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: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]

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

Source:

Source: by

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

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]

Storytelling with Data: A Data Visualization Guide for Business Professionals

[ 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: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

[ VIDEO OF THE WEEK]

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

Using Analytics to build A #BigData #Workforce

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

Source by

Source: by

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

[ COVER OF THE WEEK ]

Data interpretation Source

[ AnalyticsWeek BYTES]

>> Enhancing the Security of Cloud-Native Tools and Apps by analyticsweekpick

>> Where Chief Data Scientist & Open Source Meets – @dandegrazia #FutureOfData #Podcast by v1shal

>> Most MAT and school leaders see tech as essential part of ânew normalâ education by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CS229 – Machine Learning

This course provides a broad introduction to machine learning and statistical pattern recognition. … more

[ FEATURED READ]

Storytelling with Data: A Data Visualization Guide for Business Professionals

[ 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:How to optimize algorithms? (parallel processing and/or faster algorithms). Provide examples for both?
A: Premature optimization is the root of all evil – Donald Knuth

Parallel processing: for instance in R with a single machine.
– doParallel and foreach package
– doParallel: parallel backend, will select n-cores of the machine
– for each: assign tasks for each core
– using Hadoop on a single node
– using Hadoop on multi-node

Faster algorithm:
– In computer science: Pareto principle; 90% of the execution time is spent executing 10% of the code
– Data structure: affect performance
– Caching: avoid unnecessary work
– Improve source code level
For instance: on early C compilers, WHILE(something) was slower than FOR(;;), because WHILE evaluated something and then had a conditional jump which tested if it was true while FOR had unconditional jump.

Source