AI and Privacy: What’s in store for the the future?

One of the most common use cases of artificial intelligence at the moment is its ability to handle massive datasets, processing and interpreting them. A task that human data analysts would take ages to complete, if at all, is performed in no time, and without the possibility of human error. At the same time, the average person creates an increasingly larger digital footprint, leaving a trace in the form of a vast amount of personal information on the internet.

Corporations and governments, then, gather, store, and feed that information to powerful AI algorithms in order to learn as much as possible about that person for marketing (and other) purposes. All this has led to heated debates over the safety of our personal data and its potential misuse.

No doubt AI holds tremendous potential to disrupt and improve our lives, but there are some hidden traps and pitfalls that have to be discussed and overcome.

Is There Such a Thing as Too Much Data?

It depends on the point of view. Brands seem to need every single bit of information on their target audience in order to better understand their needs and preferences so that they can tailor the right marketing message.

While that’s in a way a legitimate thing, the rise of advanced technologies, including AI, has led this thirst for information to get in the way of their customers’ privacy.

Namely, before AI and big data analytics, it was impossible to properly interpret unstructured data coming from different sources and in different formats, which left a big chunk of information uninterpretable and thus unused.

But, once the technologies managed to crack this code and translate illegible data into the actual information, the concept of digital privacy became an issue.

In 2012, an incident showed how intimidatingly accurate data analytics can be, and what that means for an ordinary user. In an attempt to assist its customers in finding everything they might need, Target sent coupons for cribs and baby clothes to a high school girl through the mail. Her unsuspecting father went to complain, only to find out that this wasn’t just a random mistake – the store’s algorithm picked up different cues based on what kind of products the pregnant girl purchased and viewed.

Similarly, it’s possible to track and locate people with the help of their own mobile devices and wearables, which means that it’s virtually impossible to go off the radar and seclude oneself.

Voice and facial recognition additionally complicate things as these technologies are capable of completely obliterating anonymity in public places.

Although it’s somewhat comforting to know that this way many wrongdoings and crimes can be prevented and identified, the lack of regulations might put us all under surveillance. Besides, there are growing fears of misidentification and wrongful convictions. According to research studies, this technology isn’t accurate when it comes to identifying people of color, which can have grave consequences.

The Concept of Privacy in a Digital Age

The Facebook-Cambridge Analytica Scandal was just one in line of numerous incidents that demonstrated how unprotected our data is and how easy it is to obtain it, with almost no repercussions.

Just 20 years ago privacy was still a concept that existed only in the offline, physical world. And it was much easier to protect yourself and your personal data by not disclosing your credit card or Social Security number.

Today, as we use numerous online services, it’s hard to keep your data to yourself. If you want to purchase something online, you’ll have to provide your credit card number and authorize the transaction. Websites store this sensitive information online, and a single hacker attack can expose it.

For example, the data of up to 500 million Marriott International guests was compromised in a data breach in 2018.

But, it’s not only hackers and cybercriminals that jeopardize our privacy.

It’s not a secret that many companies use social media and the internet to find out more about their potential and existing employees. This can have severe implications, as people can be (and usually are) held accountable for what they post online. Some have even lost their jobs due to certain online activities like posting a profanity-laced tweet, which is exactly what happened to a NASA intern.

Is There a Solution to This Issue?

It can’t be denied that being constantly monitored and under surveillance can be frustrating.

But it would be a shame to curb the development of such immense technological advancement because of unresolved privacy issues.

AI, big data analytics, IoT, and 5G, for example, are much maligned in some circles due to the fact that they heavily rely on gargantuan amounts of data as well as that they enable a massive network of interconnected devices that can be controlled remotely.

What does this mean?

It can be both a gigantic blessing and a curse. When combined, these technologies allow, for example, the possibility of remote surgery that could save millions of lives. Similarly, IoT is a network that enables remote control of cars, homes, and appliances.

On the other hand, the data generated by these technologies can be compromised or used for harmful purposes.

Another example is AI-powered chatbots that have become indispensable in numerous industries, thanks to the fact that they can improve customer engagement and juggle multiple customer queries at the same time. This way, they help customers and increase satisfaction.

They are also capable of collecting, analyzing, and storing customer information in order to personalize every subsequent customer touchpoint and offer the best and most personalized service. This way, companies can reduce operational costs and boost customer retention rates.

Luckily, there are ways to make the most of all these AI benefits but not at the sake of compromising users’ privacy.

A New Dawn of Digital Privacy

How are we going to achieve this win-win situation and give brands our data without any fears of it being misused?

The trick is in combining cryptography and machine learning, which will result in AI’s ability to learn from data without actually seeing it.

This way, the privacy of end-users will be protected, and at the same time, companies will be able to leverage their data without breaking any laws of ethics.

Several technologies will make this happen:

  • Federated learning: This concept describes a decentralized AI framework distributed across millions of devices. Federated learning will enable scientists to create, train, improve, and assess a shared prediction model while keeping all the data on the device. In a nutshell, companies won’t have access to users’ raw data as well as no possibility of labeling it. This technology is a synergy of AI, blockchain, and IoT, keeps users’ privacy safe, and yet provides all the benefits of aggregated model improvement.
  • Differential privacy: A number of applications, including maps or fitness and health apps, collect individual users’ data so that they can make traffic predictions and or analyze users’ fitness levels and other parameters. At the moment, it’s theoretically possible to match individual contributors and their data. Differential privacy will add some randomness to the entire procedure and make it impossible to trace back the information. As a result, it won’t be possible to expose the identity of individual contributors, while allowing for their data to be collected and analyzed.
  • Homomorphic encryption: This technology uses machine learning algorithms to process and analyze encrypted data without accessing sensitive information. This data is encrypted and analyzed on a remote system. The results are sent in an encrypted form too, and they can be unlocked only by using a unique key so that the privacy of users whose data is being analyzed can be protected.

We’re still far from finding the right solution to the problem of privacy, but these small steps help in keeping things under control. AI and other technologies keep on evolving, which means that other obstacles will emerge, meaning that scientists and security experts will have to keep pace and constantly upgrade security protocols.

The post AI and Privacy: What’s in store for the the future? appeared first on Big Data Made Simple.

Source: AI and Privacy: What’s in store for the the future? by administrator

Jun 04, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Data security  Source

[ AnalyticsWeek BYTES]

>> Is this video software company set to take hockey analytics to the next level? by analyticsweekpick

>> Dave Ulrich (@dave_ulrich) talks about role / responsibility of HR in #FutureOfWork #JobsOfFuture #Podcast by v1shal

>> Six Do’s and Don’ts of Collaborative Data Management by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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

[ FEATURED READ]

Hypothesis Testing: A Visual Introduction To Statistical Significance

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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]

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 the life cycle of a data science project ?
A: 1. Data acquisition
Acquiring data from both internal and external sources, including social media or web scraping. In a steady state, data extraction and routines should be in place, and new sources, once identified would be acquired following the established processes

2. Data preparation
Also called data wrangling: cleaning the data and shaping it into a suitable form for later analyses. Involves exploratory data analysis and feature extraction.

3. Hypothesis & modelling
Like in data mining but not with samples, with all the data instead. Applying machine learning techniques to all the data. A key sub-step: model selection. This involves preparing a training set for model candidates, and validation and test sets for comparing model performances, selecting the best performing model, gauging model accuracy and preventing overfitting

4. Evaluation & interpretation

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

5. Deployment

6. Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

7. Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Steps 2 to 4 are repeated a number of times as needed; as the understanding of data and business becomes clearer and results from initial models and hypotheses are evaluated, further tweaks are performed. These may sometimes include step5 and be performed in a pre-production.

Deployment

Operations
Regular maintenance and operations. Includes performance tests to measure model performance, and can alert when performance goes beyond a certain acceptable threshold

Optimization
Can be triggered by failing performance, or due to the need to add new data sources and retraining the model or even to deploy new versions of an improved model

Note: with increasing maturity and well-defined project goals, pre-defined performance can help evaluate feasibility of the data science project early enough in the data-science life cycle. This early comparison helps the team refine hypothesis, discard the project if non-viable, change approaches.

Source

[ VIDEO OF THE WEEK]

Understanding #FutureOfData in #Health & #Medicine - @thedataguru / @InovaHealth #FutureOfData #Podcast

 Understanding #FutureOfData in #Health & #Medicine – @thedataguru / @InovaHealth #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

I’m sure, the highest capacity of storage device, will not enough to record all our stories; because, everytime with you is very valuable da

[ PODCAST OF THE WEEK]

@BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

 @BrianHaugli @The_Hanover ?on Building a #Leadership #Security #Mindset #FutureOfData #Podcast

Subscribe 

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

Seizing AI and the Edge for Cloud Service Providers (CSPs)

This free 1-hour webinar from GigaOm Research brings together experts in Cloud Computing, featuring GigaOm analyst David Linthicum and special guest from Intel Ananth Sankaranarayanan. The discussion will focus on understanding the future of cloud service providers as platforms for innovation, including changing-enabling technology, and how to prepare.

So what will be important to cloud services providers next year, two years, or five years? It’s a changing game where those that are most innovative, and leading the market, carry the day. So, what will be the focus?

This webinar takes an in-depth look at what’s next for cloud service providers, and those that consume cloud services. We’ll address obvious trends, such as artificial intelligence (AI) and machine learning (ML), use of containers, growth of 5G, serverless options, as well as new trends and approaches yet to arrive on the technology radar screen.

In this 1-hour webinar, key questions will be answered:

  • How existing hype-driven trends are reflective of cloud server provider services, including how ML, use of containers, and serverless will likely grow in the future?
  • Where are the AI use cases moving, and how CSPs can proactively keep up?
  • How cloud service providers are learning to work and play well together, including hosting multi-cloud tools and technology?
  • How cloud infrastructure is changing, and how you can stay ahead of the resource consumption curve?
  • What aspects of AI should CSPs focus on now, and how CSPs can win a multi-billion dollar market?
  • How Edge and 5G is changing the game, and why you’re already behind?
  • The role of Intel in providing the “power to the clouds,” including key technology to accelerate the reach and capabilities of CSPs today?
  • Where does open source play? Where should you place your bets now?

Join us to discover what CSPs need to know now, explore what the public cloud will be like in both the short and long term, and learn where to invest your time today, as well as in the future.

Register now to join GigaOm and Intel for this free expert webinar.

Who Should Attend:

  • Chief Architects or Chief Engineers
  • Product or Solution Architecture Leaders
  • Enterprise, Solutions, Infrastructure or Cloud Architects
  • Software or Infrastructure Engineers
  • Product Management Leaders

Source: Seizing AI and the Edge for Cloud Service Providers (CSPs) by analyticsweekpick

Building a Customer First Insurance Experience

Bajaj Allianz Life Insurance Company recently hosted a unique insurance summit focused on putting customer experience first. The event titled  “Future Perfect -Customer First Insurance Industry Summit 2018” was a full day event saw attendance by 21 insurance companies out of the 24 carriers that operate in India. While the focus was life insurance, the summit also saw participation from a few general insurance carriers.

The central theme of the summit was building and optimizing processes that would keep customer experience at the forefront of every customer interaction and transaction. In line with theme, experts from the insurance industry, regulatory bodies and cyber crime divisions of the Police spoke about upcoming trends in the insurance industry, fraud mitigation using advanced analytics, fighting cyber fraud etc…

Dr. Karnik, Chief Data Scientist at Aureus, spoke about how artificial intelligence and machine learning are being used to predict, and prevent fraud in the insurance industry. His presentation was based on his experience designing predictive models for early claims and fraud prevention across some of the largest life insurance carriers.

Dr. Karnik talking about ML in Insurance

 

Dr. Karnik Presenting at Insurance Summit

The complete presentation can be viewed below.

Using Machine Learning to Find a Needle in a Haystack by Dr. Nilesh Karnik

 

Dr. Karnik’s talk was very well received as  it derived from learnings from real life scenarios.  The complete presentation can be downloaded from here.

 

Future Perfect -Customer First Insurance Industry Summit 2018 put into focus the immediate challenges that the insurance industry as a whole is facing. The perspectives from multiple stakeholders – regulators, carriers, technology partners and cyber teams will help shape the next best action to make the end consumer experience epic and safe.

Originally Posted at: Building a Customer First Insurance Experience

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

image

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

image

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 ]

image
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

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[ 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

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[ 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

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[ 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

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[ 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

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[ 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