Dec 26, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Statistics  Source

[ AnalyticsWeek BYTES]

>> Talend Connect Europe 2018: Liberate your Data. Become a Data Hero by analyticsweekpick

>> Part 4 of 9, In-Memory Database/Grid Platforms: Removing Silos & Operationalizing Your Data by analyticsweekpick

>> Can Big Data Help You Plan Your Commute? by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

CPSC 540 Machine Learning

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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]

The Black Swan: The Impact of the Highly Improbable

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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]

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:Is it better to spend 5 days developing a 90% accurate solution, or 10 days for 100% accuracy? Depends on the context?
A: * “premature optimization is the root of all evils”
* At the beginning: quick-and-dirty model is better
* Optimization later
Other answer:
– Depends on the context
– Is error acceptable? Fraud detection, quality assurance

Source

[ VIDEO OF THE WEEK]

The History and Use of R

 The History and Use of R

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It’s easy to lie with statistics. It’s hard to tell the truth without statistics. – Andrejs Dunkels

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Brands and organizations on Facebook receive 34,722 Likes every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

Agile Data Warehouse Design for Big Data

21 Big Data Master Data Management Best Practices
21 Big Data Master Data Management Best Practices

On Nov 14th 2013 Big Data Analytics, Discovery & Visualization meetup hosted “Agile Data Warehouse Design for Big Data” by Jim Stagnitto & John Di Pietro from A2C.

Here’s the synopsis:

Synopsis:

Jim Stagnitto and John DiPietro of consulting firm a2c) will discuss Agile Data Warehouse Design – a step-by-step method for data warehousing / business intelligence (DW/BI) professionals to better collect and translate business intelligence requirements into successful dimensional data warehouse designs.

 

The method utilizes BEAM✲ (Business Event Analysis and Modeling) – an agile approach to dimensional data modeling that can be used throughout analysis and design to improve productivity and communication between DW designers and BI stakeholders. BEAM✲ builds upon the body of mature “best practice” dimensional DW design techniques, and collects “just enough” non-technical business process information from BI stakeholders to allow the modeler to slot their business needs directly and simply into proven DW design patterns.

 

BEAM✲ encourages DW/BI designers to move away from the keyboard and their entity relationship modeling tools and begin “white board” modeling interactively with BI stakeholders.  With the right guidance, BI stakeholders can and should model their own BI data requirements, so that they can fully understand and govern what they will be able to report on and analyze.

 

The BEAM✲ method is fully described in

Agile Data Warehouse Design – a text co-written by Lawrence Corr and Jim Stagnitto.

 

About the speaker:

Jim Stagnitto Director of a2c Data Services Practice

Data Warehouse Architect: specializing in powerful designs that extract the maximum business benefit from Intelligence and Insight investments.

Master Data Management (MDM) and Customer Data Integration (CDI) strategist and architect.

Data Warehousing, Data Quality, and Data Integration thought-leader: co-author with Lawrence Corr of “Agile Data Warehouse Design”, guest author of Ralph Kimball’s “Data Warehouse Designer” column, and contributing author to Ralph and Joe Caserta’s latest book: “The DW ETL Toolkit”.

 

John DiPietro Chief Technology Officer at A2C IT Consulting

John DiPietro is the Chief Technology Officer for a2c. Mr. DiPietro is responsible
for setting the vision, strategy, delivery, and methodologies for a2c’s Solution
Practice Offerings for all national accounts. The a2c CTO brings with him an
expansive depth and breadth of specialized skills in his field.

 

Sponsor Note:

Thanks to:

Microsoft NERD for providing awesome venue for the event.

A2C IT Consulting for providing the food/drinks.

Cognizeus for providing book to give away as raffle.

Here’s the youtube link for the presentation:

And Slideshare:

Source: Agile Data Warehouse Design for Big Data by v1shal

Achieving tribal leadership in 5 easy steps

Using tribal leadership to improve culture and build world-class customer experienceBefore we delve into the core of this blog, let me take a moment and spread some light on tribe leadership and what it means.

Every organization is made up of tribes, groups of 20 to 150 people who are bound together by familiarity and shared work. Tribes are the little-acknowledged, basic building block of any large human effort. David Logan, a faculty from USC stated tribe leadership in a video (Attaching the video below for listening pleasure). He categorized tribes into 5 stages:

 

·       Stage 1: “Life sucks” for everyone, and therefore it is okay for me to behave badly to make my way. Only 2% workforce accounts for this category.

·       Stage 2: “My life sucks, as people can see that life is okay for some other people, but in this stage, people have little to no motivation to change because they believe their life (or their work) is bad.  It’s all “their” fault.  The authors claim about 25% of workplace tribes operate in this mode.

·       Stage 3: “I am great, you are not.”  This is where majority of corporation lives (50% of workplace tribes).  Organizations promoting individual excellence as they hire best and brightest.

·       Stage 4: “We are great, they are not.”  A shift from individual competition to the entire tribe competing against other tribes.  In organizational settings, Stage 4 is a combination of having common goals and values as well as a common “enemy tribe” to compare against.  This represents 22% of workplace tribes.

·       Stage 5: “Life is great.”  The pinnacle of workplace tribes, they seek and promote good life for everyone.  Values are the central glue that holds the tribe together – and violation of those values can rip the tribe apart if the leader lets the violation stand.  There are no tribal competitors, not because they don’t exist, but because the tribe is striving to make an impact (on the world) rather than striving to win (against another tribe).

 

Each stage pretty much functions as it reads.  As per David Logan, we all belong to some or the other tribe and we only understand one stage up or down. He also suggests that good leaders should be able to lead tribes at every stage.

 

Sounds pretty neat, ha? I am certain that Zappos being on Stage 4 of Tribal Leadership is not surprised to learn about its pioneer position as a customer centric company. It won’t be tough to imagine Apple as Stage 5 tribal organization.

 

 

Best customer centric organizations are directly linked with organizations that deliver world-class products. So, it is important to help companies achieve stage 5 tribes. This promotes brands with strong and deep culture roots that care and want to make products that not only makes customers happy but also changes the world.

 

Following steps are needed to help companies achieve a culture to progress to stage 5 tribes.

 

Understand/Identify tribes that live or could live in your organization:

Before we groom companies to thrive in its corporate culture we need to identify various tribes that exists. Knowing each tribe and how people are aligned will not only help learn more about existing corporate culture but also give us a trajectory needed to progressively improve culture.

Promote free speech and open communication to help people align:

It is also important to let employees, vendors, and clients align themselves with tribes without any external influence. Therefore, every effort should be made to embrace free speech and open communication. This will help everyone in aligning themselves to respective tribes and verticals. This ultimately helps in identifying overall corporate positioning. This will then help in understanding weak and strong areas of the business.

 

Create a culture to identify tribe leaders:

Another important task in identifying tribe leadership is to identify tribe leaders and help them in every possible way to lead and manage tribe. This will not only help in better alignment of the tribes but also in identifying leadership that works in favor of building strong culture. Tribe leader normally carries more influence on tribe and therefore could lead the way for an organization.

 

Connect tribes to network and learn from each other:

Tribes should also be given a chance to learn from higher stages and see how and what it takes to bridge the gap. Mingling the tribal leaders together, or hosting networking sessions between tribes could do the job. This could result in thinking shift within tribes and if done properly, it could result in improving tribal stages. Therefore, improving overall corporate culture.

 

Embrace tribal leadership on management level:

Nothing is possible without leadership buyin. It is of uttermost importance that leadership/management buy-into the idea of building tribes and their leadership. Leadership should also make sure that customer centricity is an integral part of corporate DNA.

 

So, above stated 5 steps are good for starting a corporate culture that embraces effective leadership. This help builds stronger corporate culture that facilitates creating world changing groundbreaking products. Products, that people love and cherish, thereby delivering a company with great customer centricity.

 

Please leave me your suggestion and critic on comment section below. I would love to have an interactive discussion on this and would appreciate follow up discussion.

Source

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

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[  COVER OF THE WEEK ]

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Correlation-Causation  Source

[ AnalyticsWeek BYTES]

>> Why I stopped practicing law? Because data is king. by analyticsweekpick

>> Charts Vs. Tables – Choosing the Right Visualization by analyticsweek

>> Where Big Data Projects Fail by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

image

Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython

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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:Is mean imputation of missing data acceptable practice? Why or why not?
A: * Bad practice in general
* If just estimating means: mean imputation preserves the mean of the observed data
* Leads to an underestimate of the standard deviation
* Distorts relationships between variables by “pulling” estimates of the correlation toward zero

Source

[ VIDEO OF THE WEEK]

Data-As-A-Service (#DAAS) to enable compliance reporting

 Data-As-A-Service (#DAAS) to enable compliance reporting

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data is the new science. Big Data holds the answers. – Pat Gelsinger

[ PODCAST OF THE WEEK]

#FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

 #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise

Subscribe 

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

Talend increases its investments in Research & Development in Nantes

Almost three years ago today, to the day, Talend opened its fourth global research and development center, and its second one in France, in Nantes. It was clear to Talend from the very beginning of this new innovation center that it would not be a simple satellite of existing centers, but a key element in our strategy and overall R&D efforts.

Dedicated to innovative cloud, big data, and machine-learning technologies, this center plays a key role in our research and development efforts, creating new products, adding new features and services, increasing functionality, and improving our cloud data integration infrastructure. 

 

A winning investment!

Today, Talend is increasing its investments in France with the expansion of its research and development center in Nantes. This new innovation center of more than 2600m2 will make it possible to meet the requirements of supporting Talend’s strong growth but also serve to strengthen our foothold in the region’s digital ecosystem.

In 2016, when this center of excellence was opened, our objective was to recruit up to 100 engineers by the end of 2018. This target has been exceeded, with the recruitment of 120 engineers. We are now planning to increase our workforce in Nantes to 250 by 2022. 

We are proud of our ability to attract and retain the best talent to our R&D team, to create a challenging but also rewarding environment where employees can thrive, solve complex issues, and find innovative ways to address current and future challenges in data integration, processing, and governance.

At Talend, we apply agile development methodologies, work with the latest technologies, and have created a modern, flexible, and automated software development process that allows us to deliver high-quality applications and quickly adapt to market changes and the new requirements of our customers and partners.

 

A local footprint

This day, our team is moving into a new office space where they will have every opportunity to thrive in an environment that is conducive to innovation and collaboration. We also hope that this innovation hub will contribute to the development of the digital economy in Nantes and the broader region. We will therefore also have the pleasure of opening this space to the booming environment of local technology companies, by organizing regular meetings and events, meetups, or hackathons.

By establishing ourselves in Nantes, we had chosen a dynamic, innovative city and region, benefiting from a living environment recognized by those who live there on a daily basis. Nantes benefits from a highly developed digital ecosystem with many startups and innovative companies. And what better example to illustrate this than to mention Talend’s acquisition in November 2017 of Restlet, a Nantes-based leader in cloud API design and testing.

But the area of Nantes also benefits from a pool of students and leading engineering schools that are recognized internationally for the quality of their training. We will work closely with these educational centers of excellence to create joint programs around new cloud and big data technologies, work-linked training, or through the sharing of our expertise around open source technologies such as Apache Spark, Apache Beam, or Hadoop.

It is with great pride and emotion that I would like to thank all of Talend’s employees – developers, DevOps, UX designers and other automation specialists – the public stakeholders who have supported us and made our implementation a success, the digital and educational ecosystem for the opportunities we are given to exchange and learn together.

 

The post Talend increases its investments in Research & Development in Nantes appeared first on Talend Real-Time Open Source Data Integration Software.

Originally Posted at: Talend increases its investments in Research & Development in Nantes

Nick Howe (@Area9Nick) talks about fabric of learning organization to bring #JobsOfFuture #podcast

[youtube https://www.youtube.com/watch?v=-1ZP_tbZFgI]

In this podcast Nick Howe (@NickJHowe) from @Area9Learning talks about the transforming world of learning landscape. He shed light on some of the learning challenges and some of the ways learning could match the evolving world and its learning needs. Nick sheds light on some tactical steps that businesses could adopt to create world class learning organization. This podcast is must for learning organization.

Nick’s Recommended Read:
The End of Average: Unlocking Our Potential by Embracing What Makes Us Different by Todd Rose https://amzn.to/2kiahYN
Superintelligence: Paths, Dangers, Strategies by Nick Bostrom https://amzn.to/2IAPURg

Podcast Link:
iTunes: http://math.im/jofitunes
GooglePlay: http://math.im/jofgplay

Nick’s BIO:
Nick Howe is an award winning Chief Learning Officer and business leader with a focus on the application of innovative education technologies. He is the Chief Learning Officer at Area9 Lyceum – one of global leaders in adaptive learning technology, a Strategic Advisor to the Institute of Simulation and Training at the University of Central Florida, and board advisor to multiple EdTech startups.

For twelve years Nick was the Chief Learning Officer at Hitachi Data Systems where he built and led the corporate university and online communities serving over 50,000 employees, resellers and customers.

With over 25 years’ global sales, sales enablement, delivery and consulting experience with Hitachi, EDS Corporation and Bechtel Inc., Nick is passionate about the transformation of customer experiences, partner relationships and employee performance through learning and collaboration

About #Podcast:
#JobsOfFuture podcast is a conversation starter to bring leaders, influencers and lead practitioners to come on show and discuss their journey in creating the data driven future.

Want to sponsor?
Email us @ info@analyticsweek.com

Keywords:
#JobsOfFuture #Leadership #Podcast #Future of #Work #Worker & #Workplace

Source: Nick Howe (@Area9Nick) talks about fabric of learning organization to bring #JobsOfFuture #podcast

Dec 12, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> Customer centric fix to save Indian Maharaja (Air India) from financial mess by v1shal

>> Best Practices For Building Talent In Analytics by analyticsweekpick

>> Logi Tutorial: How to Set up Spark on Amazon EMR for Use with Logi Analytics by analyticsweek

Wanna write? Click Here

[ FEATURED COURSE]

Lean Analytics Workshop – Alistair Croll and Ben Yoskovitz

image

Use data to build a better startup faster in partnership with Geckoboard… more

[ FEATURED READ]

The Misbehavior of Markets: A Fractal View of Financial Turbulence

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Mathematical superstar and inventor of fractal geometry, Benoit Mandelbrot, has spent the past forty years studying the underlying mathematics of space and natural patterns. What many of his followers don’t realize is th… 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:How do you take millions of users with 100’s transactions each, amongst 10k’s 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 it’s obtained from; example: PC1=spendy axis (proportion of baskets containing spendy items, raw counts of items and visits)

Source

[ VIDEO OF THE WEEK]

Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

 Andrea Gallego(@risenthink) / @BCG on Managing Analytics Practice #FutureOfData #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

The most valuable commodity I know of is information. – Gordon Gekko

[ PODCAST OF THE WEEK]

Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

 Solving #FutureOfWork with #Detonate mindset (by @steven_goldbach & @geofftuff) #JobsOfFuture #Podcast

Subscribe 

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

MLPERF Benchmarks Showcase NVIDIA Dominance In AI Space

NVIDIA was the first of the large scale technology providers to see the opportunity for artificial intelligence (AI), particularly as applied to autonomous machines. This early focus allowed them to build up a set of skills, tools, and focused hardware that substantially enhanced the AI efforts for their customers, including IBM, another AI pioneer. As […]

The post MLPERF Benchmarks Showcase NVIDIA Dominance In AI Space appeared first on TechSpective.

Originally Posted at: MLPERF Benchmarks Showcase NVIDIA Dominance In AI Space by administrator

Possible “Unintended Consequences” of the General Data Protection Regulation

The intentions of the European Union’s General Data Protection Regulation are as laudable as they are simple. According to Osterman Research Group Principal Analyst and Founder Michael Osterman, the goal of this standard “forces companies to get their data governance under control and allows consumers, or owners of data, to be in control of their data.”

The social forces impacting both of these repercussions are well documented. The ‘own your data movement’ has been gaining traction for the past several years, while the increasing regularity (and severity, it seems) of data breaches certainly warrants more stringent data governance and security measures.

What is less documented, however, is the potential for decidedly unintended consequences of GDPR that may produce noxious effects on even those organizations that have made concerted efforts to comply. The drivers for those effects, of course, are the costly penalties associated with non-compliance, which are either 2 to 4 percent of a company’s annual turnover or 10 to 20 million Euros—whichever is more.

When one considers these penalties are applicable to organizations that possess the data of even a single resident of the EU, the potential for unintended consequences becomes more distinct. Once organizations fully understand the extremely granular nature of the regulations involved in GDPR, the potential for the negative impact of unintended consequences becomes clear. The most obvious ones pertain to organizational targeting, diminishing e-commerce, and restricting freedom of the internet. Others may also arise.

Protecting Personally Identifiable Data
The general pretext for the costly penalties of GDPR is the prevention of the loss of data that are personally identifiable. To that end, some of the specific mandates of GDPR may surprise organizations not prepared for its May 25 enforcement date. Among other facets of this standard, organizations are responsible for:

  • Downstream Data Effects: According to Osterman, organizations with data of those who reside in the EU are considered “data controllers”. Thus, such companies are responsible for virtually anything they do with those data, including issuing them to third parties for marketing or communication purposes. “If I have a mailing list and I want to send out an email offer, I have to make sure not only that I’ve cleaned the data of any EU residents that haven’t opted in to receive my communications, but I have to make sure that anybody I give that to is going to process it in a way that’s GDPR compliant,” Osterman explained.
  • Data Disassociation: In certain cases, organizations are expected to disassociate a person’s identity from their data to preserve his or her anonymity. Although this requirement is horizontal, the healthcare field serves as a good example of what’s involved. “You can process their medical information to do statistics on, let’s say how many people have certain conditions, but you can’t associate that with a person,” Osterman said. “So you’re going to have to have those technologies in place.”
  • Data Movement: The movement of data highlights the need to secure data in motion, as opposed to when they’re sitting in a secure repository. Osterman mentioned organizations will “have to do application security testing, to make sure you don’t have vulnerabilities in your web browsers, Microsoft applications, Salesforce, and what have you.”

Most importantly, perhaps, organizations will have to conduct user awareness training to ensure that personnel are competent in the vast array of measures required for compliance. These concerns and others are addressed in a recent report entitled The Procrastinator’s Guide to Preparing for GDPR.

Unintended Consequence No. 1: Targeting Organizations
Among the newfound rights that GDPR gives consumers is the right to seek an accounting of the data organizations have about them. The desired intention of this mandate is to promote transparency while empowering consumers with information about what is regarded as their data. “If it’s our data it belongs to us and companies…should have to gain our permission to use it in ways that we haven’t authorized,” Osterman commented. “The downside is I think there will be some unintended consequences of GDPR.” Depending on the nature and complexity of the request, organizations have a maximum of 90 days to respond. Osterman discussed scenarios in which individuals or grassroots organizations exploit this measure, issuing tens of thousands of requests simply to cause difficulty for socio-political purposes: “For companies that don’t have the systems in place, it can be prohibitively expensive to gather this information and you can target companies to do that.”

Dampening E-Commerce
There is also the potential for GDPR mandates to considerably slow the progress of e-commerce opportunities. E-commerce applications may be particularly sensitive to these regulations since, depending on how real-time they are, they may involve the movement of data such as credit card or geo-spatial information. According to Osterman, “GDPR may stifle e-commerce. It may be, for example, that marketing to European residents goes down because you don’t want to take the chance that maybe they haven’t opted into this particular communication.” In this respect the enormity of the penalties for non-compliance are interrelated with another prominent aspect of GDPR: data owners (consumers) must consent to how their data is used. Therefore, mass marketing campaigns and spam can have expensive repercussions for organizations that are deemed non-compliant in this respect. Osterman mentioned a similar situation involving Canada’s Anti-Spam Legislation in which organizations “can accidentally send an email to someone who shouldn’t have received it”, in which case it may be better simply to not market to Canadian residents.

Internet Restrictions
The degree of permissions GDPR requires may restrict website trafficking in other ways. For instance, simply accessing a company’s website may prove complicated for those who reside in the EU since potentially “they’re going to have to get permission every step of the way and it’s going to be very cumbersome,” Osterman said. The plurality of this difficulty should not go unnoted. Not only will companies have to decrease their website trafficking, which could possibly reduce their visibility, but potential customers in the EU will also have reduced opportunities to explore the web, shop, or learn about the offerings of different websites. Although such a situation represents the extreme end of the spectrum, it is a possibility nonetheless.

The Cost of Protection
The spirit of many of the stipulations of the GDPR is clear. It’s attempting to maintain the protection of data for their rightful owners, the people whose data organizations routinely capture and use for any variety of purposes. In doing so, this standard is calling for much greater accountability on the part of firms in possession of such sensitive data. According to Osterman, had such notable breaches as Target’s 2013 breach, eBay’s 2014 breach, and Equifax’s 2017 breach been subjected to GDPR mandates, those companies would have been assessed penalties exceeding $2.5 billion, $600 million, and $100 million, respectively.

“I think GDPR is a good thing in that it’s going to require companies to have very good data governance,” Osterman reflected. “I think it’s going to be painful getting there because you have to have the ability not only to archive data and understand where all of your data is, you have to be able to encrypt it. You have to have very good security.”

The hope is that the intended consequences of reinforced security, governance, and individual—as opposed to corporate—ownership of personal data do not inadvertently result in decreased internet trafficking, limited ecommerce, and exploitation of GDPR’s penalties.

Source by jelaniharper

Dec 05, 19: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Complex data  Source

[ AnalyticsWeek BYTES]

>> A Gentle Introduction to Linear Regression With Maximum Likelihood Estimation by administrator

>> Jun 28, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Human-Centric Artificial Intelligence: What and Why? by analyticsweekpick

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[ FEATURED COURSE]

R, ggplot, and Simple Linear Regression

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Begin to use R and ggplot while learning the basics of linear regression… more

[ FEATURED READ]

Rise of the Robots: Technology and the Threat of a Jobless Future

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What are the jobs of the future? How many will there be? And who will have them? As technology continues to accelerate and machines begin taking care of themselves, fewer people will be necessary. Artificial intelligence… 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 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]

#FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

 #FutureOfData Podcast: Peter Morgan, CEO, Deep Learning Partnership

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[ QUOTE OF THE WEEK]

We chose it because we deal with huge amounts of data. Besides, it sounds really cool. – Larry Page

[ PODCAST OF THE WEEK]

@JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

 @JohnTLangton from @Wolters_Kluwer discussed his #AI Lead Startup Journey #FutureOfData #Podcast

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[ 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