Jul 30, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Statistics  Source

[ AnalyticsWeek BYTES]

>> Borrowing Technology from Media & Entertainment for Big Data Analytics in the Cloud by analyticsweekpick

>> Janet Amos Pribanic says: ‘Business Analytics – It’s really OK that it’s not perfect first time out!’ by analyticsweek

>> Making Artificial Intelligence (AI) More Human – Weekly Guide by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Statistical Thinking and Data Analysis

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This course is an introduction to statistical data analysis. Topics are chosen from applied probability, sampling, estimation, hypothesis testing, linear regression, analysis of variance, categorical data analysis, and n… more

[ FEATURED READ]

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

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“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

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:When you sample, what bias are you inflicting?
A: Selection bias:
– An online survey about computer use is likely to attract people more interested in technology than in typical

Under coverage bias:
– Sample too few observations from a segment of population

Survivorship bias:
– Observations at the end of the study are a non-random set of those present at the beginning of the investigation
– In finance and economics: the tendency for failed companies to be excluded from performance studies because they no longer exist

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Health Informatics Analytics

 @AnalyticsWeek Panel Discussion: Health Informatics Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data that is loved tends to survive. – Kurt Bollacker, Data Scientist, Freebase/Infochimps

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

IDC Estimates that by 2020,business transactions on the internet- business-to-business and business-to-consumer – will reach 450 billion per day.

Sourced from: Analytics.CLUB #WEB Newsletter

New Mob4Hire Report “The Impact of Mobile User Experience on Network Operator Customer Loyalty” Ranks Performance Of Global Wireless Industry

Mob4Hire, in collaboration with leading customer loyalty scientist Business Over Broadway, today announced its Summer Report 2010 of its “Impact of Mobile User Experience on Network Operator Customer Loyalty” international research, conducted during the Spring. The 111-country survey analyzes the impact of mobile apps across many dimensions of the app ecosystem as it relates to customer loyalty of network operators.

Read the full press release here: http://www.prweb.com/releases/2010/08/prweb4334684.htm; The report is available at http://www.mob4hire.com/services/global-mobile-research for only $495 Individual License (1-3 people), $995 Corporate License (3+ people).

Source by bobehayes

Jul 23, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Tour of Accounting  Source

[ AnalyticsWeek BYTES]

>> Artificial intelligence – A genie out of the bottle! Winning in the age of data and computing by administrator

>> #FutureOfData with @CharlieDataMine, @Oracle discussing running analytics in an enterprise by v1shal

>> Best practices for configuring your Amazon Elasticsearch Service domain 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]

Storytelling with Data: A Data Visualization Guide for Business Professionals

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

[ TIPS & TRICKS OF THE WEEK]

Data Analytics Success Starts with Empowerment
Being Data Driven is not as much of a tech challenge as it is an adoption challenge. Adoption has it’s root in cultural DNA of any organization. Great data driven organizations rungs the data driven culture into the corporate DNA. A culture of connection, interactions, sharing and collaboration is what it takes to be data driven. Its about being empowered more than its about being educated.

[ DATA SCIENCE Q&A]

Q: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]

@AnalyticsWeek Panel Discussion: Big Data Analytics

 @AnalyticsWeek Panel Discussion: Big Data Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Processed data is information. Processed information is knowledge Processed knowledge is Wisdom. – Ankala V. Subbarao

[ PODCAST OF THE WEEK]

@DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

 @DrewConway on creating socially responsible data science practice #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

In 2008, Google was processing 20,000 terabytes of data (20 petabytes) a day.

Sourced from: Analytics.CLUB #WEB Newsletter

5 Inbound Marketing Analytics Best Practices

analytics best practices

How do know which metrics are important? What should you be tracking and how do you track it?

With the amount of data now available at the click of a mouse (or trackpad), many marketers find themselves overwhelmed by information. To make your life easier, we’ve compiled this list of five inbound marketing analytics best practices. Pay attention to these key metrics, and you’ll be able to adjust your inbound marketing strategies to be more effective.

1. Know Your Keywords and How You Rank

First of all, it’s important to know:

  • What keywords you want to organically rank for.
  • How you measure up against your competition for those keywords.

When it comes to finding keywords, in addition to Google’s Keyword Planner, check out Ubersuggest and SEO chat’s keyword suggest tool. You can also use these tools to see how your competitors are performing.

2. Know Where Your Organic Traffic is Coming From

It’s great that you’re getting organic traffic, but do you know where it’s coming from? Check out which terms you’re showing up organically for, and how you can use those as a springboard for future growth.

By consistently blogging and marketing great content, you’ll start ranking for many terms you may have never even considered. Make sure to keep this in mind during the writing process so that you’re using your time dedicated for content creation wisely.

3. Know What Content Produces Leads

You want content that increases your organic ranking as well as content that generates leads. If you’re a Hubspot user, you can create an Attribution Report to see what blog posts are generating the most leads.

attribution report

It’s also important to keep an eye on your visits to leads ratio. Increasing traffic to your site is awesome, but if that traffic isn’t producing leads then you’re attracting the wrong people.

4. Know Your Referral Traffic Origins

So we know we want organic traffic that generates leads, but what about referral traffic? We want to create content that provides so much value that others want to link to us.

Check out how your referral traffic is doing and where it’s coming from. You can build your referral traffic byguest blogging on other sites and by building relationships with others in the industry that may be interested in linking to your content.

5. Know Your Level of Social Media Engagement

It’s important to keep an eye on how many leads and customers are coming from social media. Check each social media site’s level of engagement and then focus your efforts on the sites that are providing the biggest ROI.

social-media-traffic

The most important metrics to measure when it comes to social media are interactions, clicks and visits.

Conclusion

Keeping track of all of your website metrics can be confusing, but staying on top of these five analytics best practices will help you better assess your website’s performance and make improvements where needed.

Note: This article originally appeared in Xoombi. Click for link here.

Originally Posted at: 5 Inbound Marketing Analytics Best Practices

Jul 16, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Trust the data  Source

[ AnalyticsWeek BYTES]

>> Inovalon’s Next Generation Big Data Platform Solution Achieves NCQA Measure Certification by analyticsweekpick

>> Jim Harter (@Gallup) on Defining the winning traits of Manager #FutureofWork #Work2dot0 #Podcast by v1shal

>> Leadership paradox in technology led disruptive times by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

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Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… 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]

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

Source

[ VIDEO OF THE WEEK]

Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

 Discussing Forecasting with Brett McLaughlin (@akabret), @Akamai

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

Data is not information, information is not knowledge, knowledge is not understanding, understanding is not wisdom. – Clifford Stoll

[ PODCAST OF THE WEEK]

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

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

Subscribe 

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[ FACT OF THE WEEK]

Data production will be 44 times greater in 2020 than it was in 2009.

Sourced from: Analytics.CLUB #WEB Newsletter

How innovative tech is transforming the manufacturing industry

Technology has undoubtedly reshaped almost every part of society, changing how people communicate, travel and live, among other things. It also upended processes and practices in the manufacturing sector, including through these advancements.

Artificial intelligence can reduce unplanned downtime

It wasn’t long ago that artificial intelligence (AI) was something the mainstream public only knew about through science-fiction films and books. They’re now more familiar with it because AI is more accessible and starting to influence all parts of society.

The opportunities to use AI in manufacturing are seemingly limitless. One practical application is to deploy a predictive maintenance solution that gauges the likelihood of a single part of an entire machine failing and shutting down production. An AI-based system can warn factory managers of the need to schedule repair appointments before it’s too late.

Before that option existed, issues typically only became apparent once a piece of equipment started having problems, such as not starting up at the beginning of the workday. Predictive maintenance, on the other hand, can detect minute changes in functionality. It can also extend the remaining useful life (RUL) of a machine by sending alerts as soon as it’s time to set up a service call.

Unexpected downtime can cost hundreds or thousands of dollars per minute, depending on what are manufactured at a plant. Moreover, depending on the role a broken machine plays in making products promptly and how soon a technician can fix the problem, a breakdown could substantially impact a company’s ability to remain profitable. Predictive maintenance using AI algorithms can help manufacturing businesses respond more proactively.

Additive manufacturing can create products faster and with less expense

Additive manufacturing, also known as 3D printing, involves using a computer to specify the necessary design details about an item, then instructing a specialized printer how to operate and make the product. The printer usually deposits material layer by layer, gradually creating three-dimensional results.

This method already shows incredible promise, especially for getting things made more efficiently and at a lower price point. 3D printing also makes products available to people who formerly couldn’t access them due to living in rural areas. One hospital in Guatemala uses a handheld portable scanner to generate the files that 3D printers need to work.

Before 3D printing arrived, prosthetics were too expensive for many people who needed them, and the process took weeks, requiring many appointments. Digitizing the approach with help from additive manufacturing increases patient access, plus cuts down the overall expense.

3D printing also enables manufacturers to print extra parts for their equipment. That option is especially convenient if it might otherwise take several weeks before a piece comes in stock and gets shipped. Jeannette Song, a professor at Duke University, built a mathematical model that assists manufacturing companies in deciding which parts to keep readily available on-site and which to 3D print when needed. Her model shows that even when manufacturers don’t use 3D printing to take care of their extra parts requirements very often, they save massive amounts of inventory.

Now, it’s no longer necessary to keep lots of parts available, but taking up space when the plant might not ever use them. 3D printing also enables a manufacturer to take more control of its parts needs without relying on outside suppliers.

Data-gathering helps manufacturing companies have enhanced visibility

Most manufacturing companies have collected data for a while, but the process for doing it was extremely segmented. If a plant manager wanted to compare performance between two facilities in different states, they’d have to contact people at each location and request information such as quarterly reports or weekly spreadsheets.

The time and effort required to find and compile the data often meant that the people needing the information might not have it for months. The individuals preparing it may have to communicate with employees in multiple departments while carrying out the research. Things are different now, and that’s primarily due to the Industrial Internet of Things (IIoT) concerning connected equipment.

IIoT sensors are internet-enabled and collect data in the background throughout a workday. Then, they automatically send it to a software-based interface — often one that functions in the cloud. Manufacturers use the IIoT in a wide variety of ways, but one of them is to enjoy better visibility into the lifecycle of a product. Then, companies can ensure the items they make are high-quality and will last as long as customers expect.

Data-gathering can also help manufacturers pinpoint what’s going wrong after customers receive products. For example, a company could use a database to capture all the instances of people making warranty claims or repair requests. Then, it could investigate further and uncover any commonalities about those events. It may become apparent that a particular part is most likely to break.

This boosted visibility can also lead to process improvement. In one case, a company that mined precious metals scrutinized its data to determine which factors had the biggest impacts on yields. It used the resultant conclusions to make small changes that caused a 3.7% increase in average yield within only three months. Modern technology supports facilities in acquiring data, analyzing it and making beneficial changes much faster than older methods allowed.

Collaborative robots safely boost production rates

Before robots were available for manufacturers to invest in, companies had no choice but to hire more people and increase the number of shifts to raise production levels and keep clients satisfied. When the industrial sector initially beefed up its workforce with robots, the machines stayed behind barriers or surrounded by cages to protect humans.

Starting in 2008, however, the first collaborative robots — more frequently called cobots — arrived. Those are smaller, comparatively lightweight and equipped with sensors that make the machines stop moving when people get too close. These features mean employees can work alongside cobots on the factory floor without sacrificing safety.

Grand View Research anticipates a 44.5% combined annual growth rate for the cobots market from 2029-2025. That forecast shows companies are getting on board with this technology and are eager to see what it could do for them. Cobots excel at tasks such as handling or assembling materials. Also, these machines don’t get tired like humans, so they can maintain consistently reliable performance.

Toolcraft Inc, a precision machining shop, needed to automate a multistep process to satisfy the needs of a medical device client. It tasked a collaborative robot with securely inserting a part into a computer numerical control (CNC) fixture. This approach also integrated a pneumatic gripper with the cobot to suit specific steps in the process.

After making the cobot part of its production, the facility saw a 43% increase in throughput, plus went from producing 255 pieces per week to 370. Once Toolcraft Inc. used the robot for six months, costs went down by 23%, and the company anticipates a return on investment in approximately one year.

Technology will continue to help the manufacturing sector progress

People in today’s society increasingly expect manufacturers to cater to them without delays. They also have little patience for faulty or low-quality products. These examples show that technology has already helped the manufacturing industry thrive. People should look forward to these and other technologies having similarly positive impacts for the foreseeable future.

The post How innovative tech is transforming the manufacturing industry appeared first on Big Data Made Simple.

Source: How innovative tech is transforming the manufacturing industry

Jul 09, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Data Accuracy  Source

[ AnalyticsWeek BYTES]

>> A Gentle Introduction to the Bayes Optimal Classifier by administrator

>> Betting the Enterprise on Data with Cloud-Based Disaster Recovery and Backups by jelaniharper

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

Wanna write? Click Here

[ FEATURED COURSE]

Data Mining

image

Data that has relevance for managerial decisions is accumulating at an incredible rate due to a host of technological advances. Electronic data capture has become inexpensive and ubiquitous as a by-product of innovations… more

[ FEATURED READ]

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

image

“Illuminating and very timely . . . a fascinating — and sometimes alarming — survey of big data’s growing effect on just about everything: business, government, science and medicine, privacy, and even on the way we think… more

[ TIPS & TRICKS OF THE WEEK]

Fix the Culture, spread awareness to get awareness
Adoption of analytics tools and capabilities has not yet caught up to industry standards. Talent has always been the bottleneck towards achieving the comparative enterprise adoption. One of the primal reason is lack of understanding and knowledge within the stakeholders. To facilitate wider adoption, data analytics leaders, users, and community members needs to step up to create awareness within the organization. An aware organization goes a long way in helping get quick buy-ins and better funding which ultimately leads to faster adoption. So be the voice that you want to hear from leadership.

[ DATA SCIENCE Q&A]

Q:Explain selection bias (with regard to a dataset, not variable selection). Why is it important? How can data management procedures such as missing data handling make it worse?
A: * Selection of individuals, groups or data for analysis in such a way that proper randomization is not achieved
Types:
– Sampling bias: systematic error due to a non-random sample of a population causing some members to be less likely to be included than others
– Time interval: a trial may terminated early at an extreme value (ethical reasons), but the extreme value is likely to be reached by the variable with the largest variance, even if all the variables have similar means
– Data: “cherry picking”, when specific subsets of the data are chosen to support a conclusion (citing examples of plane crashes as evidence of airline flight being unsafe, while the far more common example of flights that complete safely)
– Studies: performing experiments and reporting only the most favorable results
– Can lead to unaccurate or even erroneous conclusions
– Statistical methods can generally not overcome it

Why data handling make it worse?
– Example: individuals who know or suspect that they are HIV positive are less likely to participate in HIV surveys
– Missing data handling will increase this effect as it’s based on most HIV negative
-Prevalence estimates will be unaccurate

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

 @AnalyticsWeek: Big Data at Work: Paul Sonderegger

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

 Scott Harrison (@SRHarrisonJD) on leading the learning organization #JobsOfFuture #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Akamai analyzes 75 million events per day to better target advertisements.

Sourced from: Analytics.CLUB #WEB Newsletter

Menlo Security Transcends the Almost Secure Cybersecurity Paradigm

Companies of all sizes, across all industries, and from every region of the world all seem to follow the same basic cybersecurity strategy. That would make sense if it worked, but businesses continue to cling to an outdated model of cybersecurity despite overwhelming evidence that it’s not very effective. There is an implicit acceptance that […]

The post Menlo Security Transcends the Almost Secure Cybersecurity Paradigm appeared first on TechSpective.

Source: Menlo Security Transcends the Almost Secure Cybersecurity Paradigm by administrator