May 28, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ AnalyticsWeek BYTES]

>> How To Turn Your Data Into Content Marketing Gold by analyticsweekpick

>> 5 Recipes for Not Becoming the Data Turkey of Your Organization by analyticsweekpick

>> Movie Recommendations? How Does Netflix Do It? A 9 Step Coding & Intuitive Guide Into Collaborative Filtering by nbhaskar

Wanna write? Click Here

[ FEATURED COURSE]

Master Statistics with R

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In this Specialization, you will learn to analyze and visualize data in R and created reproducible data analysis reports, demonstrate a conceptual understanding of the unified nature of statistical inference, perform fre… more

[ FEATURED READ]

Data Science from Scratch: First Principles with Python

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Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn … 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:Name a few famous API’s (for instance GoogleSearch)
A: Google API (Google Analytics, Picasa), Twitter API (interact with Twitter functions), GitHub API, LinkedIn API (users data)…
Source

[ VIDEO OF THE WEEK]

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

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

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Every person in the US tweeting three tweets per minute for 26,976 years.

Sourced from: Analytics.CLUB #WEB Newsletter

The future of viruses and AI

Awesome, not awesome.

#Awesome
“…In theory, AIs could be used to predict the evolution of the coronavirus too. Inam imagines running unsupervised learning algorithms to simulate all possible evolution paths. You could then add potential vaccines to the mix and see if the viruses mutate to develop resistance. “This will allow virologists to be a few steps ahead of the viruses and create vaccines in case any of these doomsday mutations occur,” he says. It’s an exciting possibility, but a far-off one. We don’t yet have enough information about how the virus mutates to be able to simulate it this time around.” — Will Douglas Heaven, Editor Learn More from MIT Technology Review >

#Not Awesome
“Momentarily put aside your positions on immigration policy, if you will, and consider this case of alleged algorithmic rigging. Whatever your feelings on people seeking legal status in the US, you may find cause for concern about humanity’s growing reliance on machines to determine liberty. Last week, the Bronx Defenders and New York Civil Liberties Union filed a complaint in New York federal district court against local Immigration and Customs Enforcement (ICE) authorities. They allege that the agency adjusted the algorithm it uses to decide when someone should be released on bond. Now, detainees being held on civil immigration offenses overwhelmingly remain in custody even when they pose no flight or public safety risk and regardless of medical conditions.” — Ephrat Livni, Reporter Learn More from Quartz >

What we’re reading.

1/ A team of scientists uses a deep learning algorithm to discover an antibiotic that fights against drug-resistant bacteria in an unconventional way. Learn More from Quanta Magazine >

2/ Now that 700,000 people around the world die each year from infections that were formerly treated by antibiotics, scientists have a moonshot goal of using AI to create “resistance-proof” antibiotics. Learn More from The Atlantic >

3/ As we can use more and more data to predict human behavior, shows like Westworld and Devs try to show us what it will look like as free will erodes. Learn More from The Atlantic >

4/ Many executives are paying for AI tools that eventually need be duct-tapped together with other tools before they’ll ever be useful for their organization. Learn More from Harvard Business Review >

5/ Some AI algorithms can increase bias in the workplace, but a new company is finding ways to use algorithms that nudge people in ways that will decrease bias. Learn More from The New York Times >

6/ To better prepare students for a workplace in which roles are augmented by AI algorithms, business schools begin focusing coursework on topics like ethics, leadership, and emotional intelligence. Learn More from Knowledge @ Wharton >

7/ Google releases an open source tool to help developers build quantum machine learning algorithms that can be duplicated and used by others. Learn More from MIT Technology Review >

Links from the community.

“google-research/automl_zero/” submitted by Samiur Rahman (@samiur1204). Learn More from GitHub >

“20 women doing fascinating work in AI, machine learning and data science” submitted by Avi Eisenberger (@aeisenberger). Learn More from Silicon Republic >

🤖 First time reading Machine Learnings? Sign up to get an early version of the newsletter next Sunday evening. Get the newsletter >


The future of viruses and AI was originally published in Machine Learnings on Medium, where people are continuing the conversation by highlighting and responding to this story.

Source: The future of viruses and AI

Reinforcement Machine Learning

Reading Time: 6 minutesYou might have seen robots doing mundane tasks like cleaning room or serving beer to people. However, these actions are usually remote-controlled by a human. These robots are physically capable of doing things following a set of instructions given to them, but they lack the basic intelligence to decide and do things by themselves. Embedding intelligence is a software challenge, and reinforcement learning, a subfield in machine learning, provides a promising direction towards developing intelligent robotics. 

Reinforcement learning is concerned with how an agent uses the feedback to evaluate its actions and plan about future actions in the given environment to maximize the results. In reinforcement learning, the agent is empowered to decide how to perform a task, which makes it different from other such machine learning models where the agent blindly follows a set of instructions given to it. The machine acts on its own, not according to a set of pre-written commands. Thus, reinforcement learning denotes those algorithms, which work based on the feedback of their actions and decide how to accomplish a complex task. 

These algorithms are rewarded when they make the right decision and are punished when they make the wrong decision. Under favourable conditions, they can do a superhuman performance. Here is an introduction to reinforcement machine learning and its applications. 

[youtube https://www.youtube.com/watch?v=wL3KyYurkSk?feature=oembed&w=660&h=371]

Importance of Reinforce Learning

We need technological assistance to simplify life, improve productivity and to make better business decisions. To achieve this goal, we need intelligent machines. While it is easy to write programs for simple tasks, we need a way out to build machines that carry out complex tasks. To Achieve this is to create machines that are capable of learning things by themselves. Reinforce learning does this.

Originally Posted at: Reinforcement Machine Learning by administrator

May 21, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Insights  Source

[ AnalyticsWeek BYTES]

>> Multi-Session & Multi-Channel Funnel Reporting in Google Analytics BigQuery by administrator

>> What is Machine Learning? A definition by administrator

>> Nov 22, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

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[ FEATURED COURSE]

R Basics – R Programming Language Introduction

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Learn the essentials of R Programming – R Beginner Level!… more

[ FEATURED READ]

Antifragile: Things That Gain from Disorder

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Antifragile is a standalone book in Nassim Nicholas Taleb’s landmark Incerto series, an investigation of opacity, luck, uncertainty, probability, human error, risk, and decision-making in a world we don’t understand. The… more

[ 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:When would you use random forests Vs SVM and why?
A: * In a case of a multi-class classification problem: SVM will require one-against-all method (memory intensive)
* If one needs to know the variable importance (random forests can perform it as well)
* If one needs to get a model fast (SVM is long to tune, need to choose the appropriate kernel and its parameters, for instance sigma and epsilon)
* In a semi-supervised learning context (random forest and dissimilarity measure): SVM can work only in a supervised learning mode

Source

[ VIDEO OF THE WEEK]

Using Analytics to build A #BigData #Workforce

 Using Analytics to build A #BigData #Workforce

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The world is one big data problem. – Andrew McAfee

[ PODCAST OF THE WEEK]

@DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

 @DrewConway on fabric of an IOT Startup #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

More than 200bn HD movies – which would take a person 47m years to watch.

Sourced from: Analytics.CLUB #WEB Newsletter

Steph Curry’s Season Stats in 13 lines of R Code

At Stattleship, we’re always on the lookout for stellar sports data vizzes and analyses. We came across Carl Allchin‘s succinct Tableau visualization of Steph Curry’s points per game compared to his season average.

We believe in simple and fast access to sports data for everyone. Here’s 13 lines of R code in order to get the same data (+9 games since he published and minus the SportsVu data) that Carl used for his visualization. You can then read the .csv file directly into Tableau Public. Updating the data to get Steph’s latest game performances is as simple as re-running this simple script. So go ahead, play around! For more information on what kind of sports data is available head on over to the playbook.

https://gist.github.com/tcash21/6578779cfbf9804d67de

And just to prove that the above code does indeed produce up-to-date results, here’s the Tableau dashboard re-created (+9 more games since Carl created his). Click the image to launch the dashboard.

Steph Curry Season Stats Tableau

Originally Posted at: Steph Curry’s Season Stats in 13 lines of R Code by stattleship

May 14, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Weak data  Source

[ AnalyticsWeek BYTES]

>> Is My Catalog Too Small to Consider a PIM Solution? by administrator

>> Jul 06, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> PHP Exceeds the Generic Human Expectations. Here’s how the Brand got it Done by thomassujain

Wanna write? Click Here

[ FEATURED COURSE]

Machine Learning

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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]

Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

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Written by renowned data science experts Foster Provost and Tom Fawcett, Data Science for Business introduces the fundamental principles of data science, and walks you through the “data-analytic thinking” necessary for e… 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 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 ID’s). Satellite tables map ID’s 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]

#HumansOfSTEAM feat. Hussain Gadwal, Mechanical Designer via @SciThinkers #STEM #STEAM

 #HumansOfSTEAM feat. Hussain Gadwal, Mechanical Designer via @SciThinkers #STEM #STEAM

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Hiding within those mounds of data is knowledge that could change the life of a patient, or change the world. – Atul Butte, Stanford

[ PODCAST OF THE WEEK]

@RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

 @RCKashyap @Cylance on State of Security & Technologist Mindset #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter

May 07, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Correlation-Causation  Source

[ AnalyticsWeek BYTES]

>> How Google Analytics Uses Cookies To Identify Users by administrator

>> Friends of Juice: Jessica Walker by analyticsweek

>> Australian businesses failing to explore bigger data by analyticsweekpick

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[ FEATURED COURSE]

Pattern Discovery in Data Mining

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Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern disc… more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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A stimulating excursion into pure mathematics aimed at “the mathematically traumatized,” but great fun for mathematical hobbyists and serious mathematicians as well. Requiring only high school algebra as mathematical bac… more

[ 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:When would you use random forests Vs SVM and why?
A: * In a case of a multi-class classification problem: SVM will require one-against-all method (memory intensive)
* If one needs to know the variable importance (random forests can perform it as well)
* If one needs to get a model fast (SVM is long to tune, need to choose the appropriate kernel and its parameters, for instance sigma and epsilon)
* In a semi-supervised learning context (random forest and dissimilarity measure): SVM can work only in a supervised learning mode

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Keynote: The CMO isn't satisfied: Judah Phillips

 @AnalyticsWeek Keynote: The CMO isn’t satisfied: Judah Phillips

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data matures like wine, applications like fish. – James Governor

[ PODCAST OF THE WEEK]

@ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

 @ChuckRehberg / @TrigentSoftware on Translating Technology to Solve Business Problems #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

30 Billion pieces of content shared on Facebook every month.

Sourced from: Analytics.CLUB #WEB Newsletter

October 31, 2016 Health and Biotech analytics news roundup

Here’s the latest in health and biotech analytics:

23andMe Has Given Up On Next-Generation Sequencing: The company is defying current trends and abandoning tests based on whole-genome sequencing. Instead, they’re focusing on their current business, which only examines specific portions of the genome.

Teva Pharmaceuticals and IBM Expand Global Partnership to Enable Drug Development and Chronic Disease Management with Watson: The companies are looking for systematic ways of discovering new ways to use drugs that are already approved for use. They are also working on ways to proactively manage chronic conditions like asthma.

The Johns Hopkins Hospital Launches Capacity Command Center to Enhance Hospital Operations: The center employs 24 people tracking real-time data. It has made many areas of the hospital’s operations, like transferring and discharging patients, more efficient.

Right-Sizing Your Long-Term Computational Needs for Life Sciences Workloads: Michael McManus, Senior Health and Life Sciences Solution Architect at Intel, discusses how companies can use workflow and storage ‘rate constants’ to know how much computing they need and how to scale it up. He also discusses cluster architecture specifically tailored to sequencing, which can make the workflow more efficient.

 

Source by pstein