Oct 22, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Productivity  Source

[ AnalyticsWeek BYTES]

>> Embedded Analytics in Manufacturing: The Real Revolution [Guest Post] by analyticsweek

>> Global Payments signs cloud deal with AWS by analyticsweekpick

>> Big Data: The Next Revolution of Sport by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

A Course in Machine Learning

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Machine learning is the study of algorithms that learn from data and experience. It is applied in a vast variety of application areas, from medicine to advertising, from military to pedestrian. Any area in which you need… more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

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People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… 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:How do you know if one algorithm is better than other?
A: * In terms of performance on a given data set?
* In terms of performance on several data sets?
* In terms of efficiency?
In terms of performance on several data sets:

– ‘Does learning algorithm A have a higher chance of producing a better predictor than learning algorithm B in the given context?”
– ‘Bayesian Comparison of Machine Learning Algorithms on Single and Multiple Datasets”, A. Lacoste and F. Laviolette
– ‘Statistical Comparisons of Classifiers over Multiple Data Sets”, Janez Demsar

In terms of performance on a given data set:
– One wants to choose between two learning algorithms
– Need to compare their performances and assess the statistical significance

One approach (Not preferred in the literature):
– Multiple k-fold cross validation: run CV multiple times and take the mean and sd
– You have: algorithm A (mean and sd) and algorithm B (mean and sd)
– Is the difference meaningful? (Paired t-test)

Sign-test (classification context):
Simply counts the number of times A has a better metrics than B and assumes this comes from a binomial distribution. Then we can obtain a p-value of the HoHo test: A and B are equal in terms of performance.

Wilcoxon signed rank test (classification context):
Like the sign-test, but the wins (A is better than B) are weighted and assumed coming from a symmetric distribution around a common median. Then, we obtain a p-value of the HoHo test.

Other (without hypothesis testing):
– AUC
– F-Score

Source

[ VIDEO OF THE WEEK]

Unraveling the Mystery of #BigData

 Unraveling the Mystery of #BigData

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[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

@JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

 @JohnNives on ways to demystify AI for enterprise #FutureOfData #Podcast

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[ FACT OF THE WEEK]

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

Sourced from: Analytics.CLUB #WEB Newsletter

Oct 15, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Ethics  Source

[ FEATURED COURSE]

Intro to Machine Learning

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Machine Learning is a first-class ticket to the most exciting careers in data analysis today. As data sources proliferate along with the computing power to process them, going straight to the data is one of the most stra… more

[ FEATURED READ]

The Signal and the Noise: Why So Many Predictions Fail–but Some Don’t

image

People love statistics. Statistics, however, do not always love them back. The Signal and the Noise, Nate Silver’s brilliant and elegant tour of the modern science-slash-art of forecasting, shows what happens when Big Da… 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 A/B testing?
A: * Two-sample hypothesis testing
* Randomized experiments with two variants: A and B
* A: control; B: variation
* User-experience design: identify changes to web pages that increase clicks on a banner
* Current website: control; NULL hypothesis
* New version: variation; alternative hypothesis

Source

[ VIDEO OF THE WEEK]

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

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

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[ QUOTE OF THE WEEK]

Data really powers everything that we do. – Jeff Weiner

[ PODCAST OF THE WEEK]

#FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

 #FutureOfData Podcast: Conversation With Sean Naismith, Enova Decisions

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[ FACT OF THE WEEK]

In late 2011, IDC Digital Universe published a report indicating that some 1.8 zettabytes of data will be created that year.

Sourced from: Analytics.CLUB #WEB Newsletter

The Last Layer of Cyber Security: Business Continuity and Disaster Recovery with Incremental Backups

Due to burgeoning regulatory penalties and a seemingly interminable amount of threats, cyber security is a foremost concern for the contemporary enterprise.

Oftentimes, the most dependable protection involves combining methods and technologies to preserve the integrity of IT systems and their data. In this regard, an organization’s security stack is often as important as its technology stack.

According to BackupAssist Chief Executive Officer Linus Chang, there is considerable advantage to topping the former with reliable, systematic backups to ensure business continuity.

“A few years ago, people were saying that backup is dead, people are moving to the cloud, it’s all about high availability and so on,” Chang mentioned. “But these new regulations, and especially the onset of crypto ransomware, has really brought back the spotlight for having multiple layers of protection for data. There’s a lot of interest in keeping historical versions of data, and having backups as the last layer of protection should perimeter security fail.”

Ransomware
The advent of cryptographic ransomware makes for a compelling use case to preserve backup copies of information assets, and serves to highlight the various areas in which business continuity impacts the enterprise. Business continuity involves elements of cyber security, network availability, organizational risk, and disaster recovery—meaning that timely backups are assistive in each of these areas as well. Chang referenced an occurrence in which users “were struck by ransomware and didn’t have a proper backup system. They never got the data back and had to re-key in six weeks of data. Having backups would have minimized that to two, three hours maximum.” Modern backup systems are able to address instances of ransomware (and other types of malware) in three ways:

  • Protection—Protective capabilities can help ward off ransomware or malware attacks.
  • Detection—Scanning mechanisms enable the detection of any sort of large scale corruption or file system modifications typical of ransomware. Such routine scanning is a component of the backup process.
  • Response—Once ransomware or infected files are detected, backup jobs are ceased and prevented from running on those files. Alerts are sent to users notifying them of infected files.

High Availability
Network availability is an integral aspect of business continuity, particularly in the event of security breaches or disaster recovery. Nonetheless, there are a few key differences between conventional high availability methods (which frequently involve redundancy and cloud failover capabilities) and those provided by timely backups. According to Chang, “High availability is about minimizing downtime. Commonly you would say three nines [99.9%], four nines [99.99%], five nines [99.999%] of availability. Over the course of a year you might be down for 20 or 30 minutes.” Backing up data can also decrease downtime in certain network failure events. Still, backups issue benefits in addition to availability. “Backup is about being able to restore whole systems to get back historical data,” Chang noted. “High availability only talks about the current version of data. It doesn’t talk about being able to pull something back from two years ago.” Thus, when prompted by regulators or legal discovery measures for data companies may have possessed years ago, data backups—not high availability—provide ideal solutions. As such, backups are necessary for business continuity. “Business continuity’s all about the business and do they have the data when they need it,” Chang explained.

Flexibility
Viewed from a business continuity perspective, backups require a degree of flexibility to serve an ever evolving ecosystem of enterprise data needs. Cloud backups are usually the most common variety of backups deployed. However, there are certain situations in which backing up data to local, physical storage (typically on disk) is much more preferable to cloud backups. “If you’ve got small data sets, then absolutely it’s more feasible to put that data in the cloud,” Chang said. “When you’ve got large datasets and complete servers that you need to get up after a disaster, you need to bring it back up for full metal disaster recovery, then it’s always faster to do that when it’s stored on a hard disk at your local office. Imagine downloading terabytes of data from the cloud. It’s just too slow.” Other backup options involve cold storage backups, in which copies of data are “disconnected from the computer and the network,” Chang commented. Best practices include storing backups in multiple locations, utilizing cold storage, and leveraging ubiquitous file formats as opposed to proprietary, vendor formats that are difficult to access once versions of software or files have progressed.

Secure Implementations
In addition to investing in measures to fortify perimeter security, it’s becoming more and more necessary to preserve data with backups for any variety of use cases. Doing so is instrumental to business continuity in all of its facets, which include high availability, risk, cyber security, and disaster recovery.

Source by jelaniharper

Oct 08, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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SQL Database  Source

[ FEATURED COURSE]

Hadoop Starter Kit

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Hadoop learning made easy and fun. Learn HDFS, MapReduce and introduction to Pig and Hive with FREE cluster access…. 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]

Keeping Biases Checked during the last mile of decision making
Today a data driven leader, a data scientist or a data driven expert is always put to test by helping his team solve a problem using his skills and expertise. Believe it or not but a part of that decision tree is derived from the intuition that adds a bias in our judgement that makes the suggestions tainted. Most skilled professionals do understand and handle the biases well, but in few cases, we give into tiny traps and could find ourselves trapped in those biases which impairs the judgement. So, it is important that we keep the intuition bias in check when working on a data problem.

[ DATA SCIENCE Q&A]

Q:Give examples of data that does not have a Gaussian distribution, nor log-normal?
A: * Allocation of wealth among individuals
* Values of oil reserves among oil fields (many small ones, a small number of large ones)

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

 @AnalyticsWeek: Big Data Health Informatics for the 21st Century: Gil Alterovitz

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

With data collection, ‘the sooner the better’ is always the best answer. – Marissa Mayer

[ PODCAST OF THE WEEK]

#FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

 #FutureOfData with @theClaymethod, @TiVo discussing running analytics in media industry

Subscribe 

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[ FACT OF THE WEEK]

According to Twitter’s own research in early 2012, it sees roughly 175 million tweets every day, and has more than 465 million accounts.

Sourced from: Analytics.CLUB #WEB Newsletter

Learning to drive sustainable practices through smarter technology

As the demand for agricultural commodities rises around the world, the ag industry is recognizing the need for sustainable practices and processes. Sustainable agriculture practices are designed to protect both our natural environment and our resources, while promoting environmental stewardship and enhancing the quality of life for farmers and producers. To achieve this new approach to agribusiness, the industry must embrace the tools of success, including smart technology, data analysis algorithms and connectivity devices.

This demand for more sustainable practices has grown over the last decade, changing the industry’s focus from productivity to sustainability. From increasing concerns over weather changes, to the rapidly growing world population and the constantly changing business environment within the ag industry, there are strong drivers to achieve success.

Today’s agribusiness leaders are establishing their sincere motivation to achieve measurable and manageable practices across the value chain. From farmers and ranchers to agronomists, ag retailers, buyers and manufacturers, there are clear changes needed to achieve sustainable practices, including building trusting partnerships within the industry and creating work standards across the industry that make employees proud.

The emergence of smart farming tools and discussions around regenerative agriculture are driving industry leaders to look for solutions. And the technology available today could provide us the path toward those solutions. “Proagrica has stayed very close to the agronomists in developing our algorithms,” says Brandon Buie, Director of Business Development, Enterprise Solutions for Proagrica. “This helps us remain close to the science of improving production and the utilization of resources and reducing waste. While still helping producers to achieve the level of production and the quality to serve their customers.”

In order to truly improve sustainability efforts across the ag industry, technology, data analysis and connectivity must be enhanced to allow:

Improved Communications. Communication plays a vital role in strategy, execution and sustainability. Communication and connectivity allow team members to discuss challenges, share solutions and keep in contact regarding shared goals. For farmers who have experienced connectivity struggles for decades, the ability to trust their technology and rely on their ability to communication and transmit information changes their daily practices in significant, positive ways.

Stronger Partnerships. “Agriculture is such a huge industry, and in so many ways, the most important industry. Yet, it is fragmented; what we need is a true value network – not a supply chain – but a value network,” says Lindsay Suddon, Chief Strategy Officer for Proagrica. True partnerships and a culture of sharing information allows members of the industry to learn from the successes and failures of others, to more quickly identify efficiencies and to celebrate shared successes.

Algorithms and Answers. According to estimates by IBM, by the year 2020, there will be 300 times more data and information available than we had in 2005. In order to advance sustainable practices in agribusiness, we must not only utilize the available data, but understand how to analyze it to find answers and use it to create improved algorithms. “We need more data and we need it from multiple sources,” says Mr. Buie. “Proagrica understands the importance of integrating with our customer’s systems to provide actionable insights. That is the way forward.”

Proactive Planning. Utilizing data to manage and measure results to process changes can help to identify both problems and solutions. The more information that is available and shared, the more proactive farmers and producers can be. “Proactive, rather than reactive, is the key,” says Mr. Suddon. “We must be able to plan for the future or we will never move forward.”

Accountability Across the Industry. In order to meet the goals shared across the industry, there must also be shared accountability. “We are looking at collaboration between multiple stakeholders, all focused on regenerative practices that move the industry forward,” says Mr. Buie. “This is our opportunity to come together, from manufacturers through the value chain to growers and achieve not only the UN Sustainability Goals, but real sustainability in our daily practices and ensuring more effective and sustainable use of products.”

Looking forward, business leaders at every stage of the agriculture value cycle must take ownership of their responsibilities and the opportunities for innovation and improvement. Obtaining value from data is a complicated process; Proagrica is leading the way, depending on their team of data scientists and expertise in environmental sciences to compile data for their customers, track and trend information gained from the data and use that information to put real-world solutions in place to advance the cause of sustainability and the quest for improved production and quality in agribusiness.

To learn more about Proagrica’s data solutions tailored to support sustainable practices, click here.

The post Learning to drive sustainable practices through smarter technology appeared first on Proagrica.

Source: Learning to drive sustainable practices through smarter technology

What is in an API?

Adoption and use of an API are heavily dependent upon the consistency, reliability, and intuitiveness of a given API. It is a delicate balance. If the API is too difficult to use or the results are inconsistent across uses, its value will be limited and, hence, adoption will be low. Our research has found that many enterprises have focused on the technical requirements to deliver and scale and API, but have not focused on the corresponding non-functional requirements of a comprehensive API strategy that includes support, service level management, ecosystem development and usability in the face of legacy integration or stream large amounts of data in response to a request.

Key Findings:

  • Enterprises are creating APIs at an incredibly fast pace often without paying attention to the implications for operational overhead or a comprehensive strategy that goes beyond the technology for building and deployment.
  • A successful API strategy must include provisions for developer onboarding and support, clear policies related to service levels and use, the ability to operate in a hybrid cloud model, and a proactive monetization scheme. With regard to the latter primarily focused on support for various sizing approaches and scalability.
  • There is still a strong requirement to “roll your own” API infrastructure to support a comprehensive API strategy. While many of today’s API gateway and management products to address the basics, businesses will still need to invest in building a developer portal, providing support, growing a community and addressing many of the operational issues. Microservices architecture is helping to alleviate some of the overhead associated with deployment and scalability by packaging APIs into manageable entities.

Originally Posted at: What is in an API?