Aug 30, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data security  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Enterprise Architecture for the Internet of Things: Containerization and Microservices by jelaniharper

>> Future of Public Sector and Jobs in #BigData World #FutureOfData #Podcast by v1shal

>> Data And Analytics Collaboration Is A Win-Win-Win For Manufacturers, Retailers And Consumers by analyticsweekpick

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[ NEWS BYTES]

>>
 Master the fundamentals of cloud application security – TechTarget Under  Cloud Security

>>
 Hadoop and Big Data Analytics Market Segmentation, Opportunities, Trends & Future Scope to 2026 – Coherent Chronicle (press release) (blog) Under  Hadoop

>>
 HR Tech Startup meQuilibrium Raises $7M in Series C – American Inno Under  Talent Analytics

More NEWS ? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… more

[ FEATURED READ]

Superintelligence: Paths, Dangers, Strategies

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The human brain has some capabilities that the brains of other animals lack. It is to these distinctive capabilities that our species owes its dominant position. Other animals have stronger muscles or sharper claws, but … 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 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]

The History and Use of R

 The History and Use of R

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[ QUOTE OF THE WEEK]

Information is the oil of the 21st century, and analytics is the combustion engine. – Peter Sondergaard

[ PODCAST OF THE WEEK]

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

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

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[ FACT OF THE WEEK]

Estimates suggest that by better integrating big data, healthcare could save as much as $300 billion a year — that’s equal to reducing costs by $1000 a year for every man, woman, and child.

Sourced from: Analytics.CLUB #WEB Newsletter

What Is a Residential IP, Data Center Proxy and what are the Differences?

A residential IP could simply mean a connection from an ISP to a residential owner. When you connect to the internet, you connect using an IP address. To know your current IP address, you can use the What Is My IP site. It will display your IP address and your ISP name as well as the country you are connecting the internet from.

An IP address is a set of numbers appearing in a pattern and separated with a full stop such as 198.162.122.1. If you use a residential IP address as your proxy when connecting the internet in your residence, your real IP address will be masked so you will be assigned a different IP address which is called residential IP address.

What is a datacenter proxy?

Unlike a residential IP that is owned by an ISP, a datacenter proxy is not. It acts a shield between you and the web. So anyone spying on what you are doing cannot track you. Your home IP address and all the information related to it is hidden and only the datacenter proxy is displayed together with the details of the datacenter proxy provider. A datacenter proxy can also work as a shield that masks your actual IP address and all your information, however, its performance is not as effective as that of a residential IP.

Difference between a residential IP and a datacenter proxy

Let’s say you are browsing the web from a public Wi-Fi and you need to hide your real IP since most public Wi-Fi connections are not secure which could make no sense to use a residential IP as a proxy.The real essence to use a residential IP address it to ensure that sites don’t know who exactly you are since no information associated with you is made available to those websites you visit.

Residential IP Proxies

Genuine and legitimate: It is easier to create multiple data center proxies but obtaining many residential proxies is difficult since residential IPs are mainly used for residential purposes. This is the reason why residential IPs are considered to be more genuine and legitimate when compared to datacenter proxies.

Costly with few providers: Residential IPs are difficult to obtain so this makes them be more expensive since fewer providers offer them, in fact, obtaining a monthly subscription for hundreds of residential IPs is extremely expensive. However, sometimes the monthly subscription for hundreds of residential IPs could be cheaper when compared with a larger monthly subscription of data center IP proxies.

Residential IPs are sometimes prone to be blacklisted: Although they are genuine and legitimate, they are also likely to be abused.  In such situations, they get blacklisted by some security technologies and databases. Therefore, using a residential proxy connection is good although not perfect.

Datacenter proxies

Less genuine though still protective: Websites have the ability to detect a user who is accessing them via a proxy connection, and since there are many users who are spamming these websites, you could be held accountable when accessing these websites using one. However, what the websites can detect is the datacenter proxy since you real IP address and all information associated with you is shielded.  It is, therefore, good to use fresh data center proxy for different accounts than accessing the web with your real IP for all your account.

Cheaper with more providers: It’s easy to collect datacenter proxies and they are offered by hundreds of providers. This makes them be less expensive; in fact, they cost a fraction of what residential IP proxies could cost you.

Which is best for residential IPs and data center proxy?

This post is not aimed at selling either of the two so you can take it up to yourself to decide which one best suits your needs. However, it is good to be careful when getting advice from a proxy or VPN provider.

Data centers are easy to get and they are less expensive. Using them could cost you a fraction of what residential IPs could cost you, however, if you consider legitimacy, you are better off using residential IPs.

Conclusion

Having learned about the difference between residential IPs and datacenter proxy, it’s your turn to choose which one is suitable for your needs. However, it is good to consider using something that is genuine all time.

Source

Assess Your Data Science Expertise

Data Skills Scoring System
Data Skills Scoring System

What kind of a data scientist are you? Take the free Data Skills Scoring System Survey at http://pxl.me/awrds3

Companies rely on experts who can make sense of their data. Often referred to as data scientists, these people bring their specific skills to bear in helping extract insight from the data. These skills include such things as Hacking, Math & Statistics and Substantive Expertise. In an interesting study published by O’Reilly, Harlan D. Harris, Sean Patrick Murphy and Marck Vaisman surveyed several hundred practitioners, asking them about their proficiency in 22 different data skills. They found that data skills fell into five broad areas: Business, ML / Big Data, Math / OR, Programming and Statistics.

Complementary Data Skills Required

There are three major tasks involved in analytics projects. First, you need to ask the right questions, requiring deep knowledge of your domain of interest, whether that be for-profit business, non-profits or healthcare organizations. When you know your domain area well, you are better equipped to know what questions to ask to get the most value from your data. Second, you need access to the data to help you answer those questions. These data might be housed in multiple data sources, requiring a data worker with programming skills to access and intelligently integrate data silos. Finally, you need somebody to make sense of the data to answer the questions proposed earlier. This step requires data workers who are more statistically-minded and can apply the right analytics to the data. Answering these questions could be more exploratory or intentional in nature, requiring different types of statistical and mathematical approaches.

Getting value from data is no simple task, often requiring data experts with complementary skills. After all, I know of nobody who possesses all the data skills to successfully tackle data problems. No wonder why data science has been referred to as a team sport.

Data Skills Scoring System (DS3)

We at AnalyticsWeek have developed the Data Skills Scoring System (DS3), a free web-based self-assessment survey that measures proficiency across five broad data science skills: business, technology, math and modeling, programming and statistics. Our hope is that the DS3 can optimize the value of data by improving how data professionals work together. If you are a data professional, the DS3 can help you:

  1. identify your analytics strengths
  2. understand where to improve your analytics skill set
  3. identify team members who complement your skills
  4. capitalize on job postings that match your skill set

While the publicly available DS3 is best suited for individual data professionals, we are customizing the DS3 for enterprises to help them optimize the value of their data science teams. By integrating DS3 scores with other data sources, enterprises will be able to improve how they acquire, retain and manage data professionals.

Find out your data skills score by taking the free Data Skills Scoring System Survey:

http://pxl.me/awrds3

We are also conducting research using the DS3 that will advance our understanding of the emerging field of data science. Some questions we would like to answer are:

  • Do certain data skills cluster together?
  • Are some data skills more important than others in determining project success?
  • Are data science teams with comprehensive data skills more satisfied with their work than data science teams where some skills are lacking?

Respondents will receive a free executive summary of our findings.

Source by bobehayes

Aug 23, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Productivity  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Which Customer Loyalty Metric is the Best? My Interview with Jeff Olsen of Allegiance Radio by bobehayes

>> Measuring Customer Loyalty in Non-Competitive Environments by bobehayes

>> Four Use Cases for Healthcare Predictive Analytics, Big Data by analyticsweekpick

Wanna write? Click Here

[ FEATURED COURSE]

Process Mining: Data science in Action

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Process mining is the missing link between model-based process analysis and data-oriented analysis techniques. Through concrete data sets and easy to use software the course provides data science knowledge that can be ap… 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]

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:Do you think 50 small decision trees are better than a large one? Why?
A: * Yes!
* More robust model (ensemble of weak learners that come and make a strong learner)
* Better to improve a model by taking many small steps than fewer large steps
* If one tree is erroneous, it can be auto-corrected by the following
* Less prone to overfitting

Source

[ VIDEO 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

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[ QUOTE OF THE WEEK]

Numbers have an important story to tell. They rely on you to give them a voice. – Stephen Few

[ PODCAST OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

 #BigData @AnalyticsWeek #FutureOfData with Jon Gibs(@jonathangibs) @L2_Digital

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[ FACT OF THE WEEK]

73% of organizations have already invested or plan to invest in big data by 2016

Sourced from: Analytics.CLUB #WEB Newsletter

Is Big Data The Most Hyped Technology Ever?

I read an article today on the topic of Big Data. In the article, the author claims that the term Big Data is the most hyped technology ever, even compared to such things as cloud computing and Y2K. I thought this was a bold claim and one that is testable. Using Google Trends, I looked at the popularity of three IT terms to understand the relative hype of each (as measured by number of searches on the topic): Web 2.0, cloud computing and big data. The chart from Google Trends appears below.

We can learn a couple of things from this graph. First, the interest in Big Data continues to grow since its first measurable growth appeared in early 2011. Still, the number of searches for the respective terms clearly shows that Web 2.0 and cloud computing received more searches than Big Data. While we don’t know if interest in Big Data will continue to grow, Google Trends, in fact, predicts very a very slow growth rate for Big Data through the end of 2015.

Second, the growth rates of Web 2.0 and cloud computing are faster compared to the growth rate of Big Data, showing that public interest grew more quickly for those terms than for Big Data. Interest in Web 2.0 reached its maximum in a little over 2 years since its initial ascent. Interest in cloud computing reached its peak in about 3.5 years. Interest in Big Data has been growing steadily for over 3.7 years.

One thing of interest. For these three technology terms, the growth of the two latter technology terms started at the peak of the previous term. As one technology becomes commonplace, another takes its place.

So, is Big Data the most hyped technology ever? No.

Source: Is Big Data The Most Hyped Technology Ever? by bobehayes

How to be Data-Driven when Data Economics are Broken

The day an IBM scientist invented the relational database in 1970 completely changed the nature of how we use data. For the first time, data became readily accessible to business users.  Businesses began to unlock the power of data to make decisions and increase growth. Fast-forward 48 years to 2018, and all the leading companies have one thing in common: they are intensely data-driven.

The world has woken up to the fact that data has the power to transform everything that we do in every industry from finance to retail to healthcare– if we use it the right way. And businesses that win are maximizing their data to create better customer experiences, improve logistics, and derive valuable business intelligence for future decision-making. But right now, we are at a critical inflection point. Data is doubling each year, and the amount of data available for use in the next 48 years is going to take us to dramatically different places than the world’s ever seen.

Let’s explore the confluence of events that have brought us to this turning point, and how your enterprise can harness all this innovation – at a reasonable cost.

Today’s Data-driven Landscape

We are currently experiencing a “perfect storm” of data. The incredibly low cost of sensors, ubiquitous networking, cheap processing in the Cloud, and dynamic computing resources are not only increasing the volume of data, but the enterprise imperative to do something with it. We can do things in real-time and the number of self-service practitioners is tripling annually. The emergence of machine learning and cognitive computing has blown up the data possibilities to completely new levels.

Machine learning and cognitive computing allows us to deal with data at an unprecedented scale and find correlations that no amount of brain power could conceive.  Knowing we can use data in a completely transformative way makes the possibilities seem limitless.  Theoretically, we should all be data-driven enterprises. Realistically, however, there are some roadblocks that make it seem difficult to take advantage of the power of data:

Trapped in the Legacy Cycle with a Flat Budget

 The “perfect storm” of data is driving a set of requirements that is dramatically outstripping what most IT shops can do. Budgets are flat —increasing only 4.5% annually — leaving companies to feel locked into a set of technology choices and vendors. In other words, they’re stuck in the “legacy cycle”.  Many IT teams are still spending most of budget just trying to keep the lights on. The remaining budget is spent trying to modernize and innovate, and then a few years later, all that new modern stuff that you brought is legacy all over again, and the cycle repeats. That’s the cycle of pain that we’ve all lived through for the last 20 years.

Lack of Data Quality and Accessibility

Most enterprise data is bad. Incorrect, inconsistent, inaccessible…these factors hold enterprises back from extracting the value from data. In a Harvard Business Review study, only 3% of the data surveyed was found to be of “acceptable” quality. That is why data analysts are spending 80% of their time preparing data as opposed to doing the analytics that we’re paying them for. If we can’t ensure data quality, let alone access the data we need, how will we ever realize its value?

Increasing Threats to Data

The immense power of data also increases the threat of its exploitation. Hacking and security breaches are on the rise; the global cost of cybercrime fallout is expected to reach $6 trillion by 2021, double the $3 trillion cost in 2015. In light of the growing threat, the number of security and privacy regulations are multiplying.  Given the issues with data integrity, organizations want to know: Is my data both correct and secure? How can data security be ensured in the middle of this data revolution?

Vendor Competition is Intense

The entire software industry is being reinvented from the ground up and all are in a race to the cloud. Your enterprise should be prepared to take full advantage of these innovations and choose vendors most prepared to liberate your data, not just today, but tomorrow, and the year after that.

Meet the Data Disruptors

It might seem impossible to harness all this innovation at a reasonable cost. Yet, there are companies that are thriving amid this data-driven transformation. Their secret? They have discovered a completely disruptive way, a fundamentally new economic way, to embrace this change.

We are talking about the data disruptors – and their strategy is not as radical as it sounds. These are the ones who have found a way to put more data to work with the same budget. For the data disruptors, success doesn’t come from investing more budget in the legacy architecture. These disruptors take an approach with a modern data architecture that allows them to liberate their data from the underlying infrastructure.

Put More of Your Data to Work

The organizations that can quickly put right data to work will have a competitive advantage. Modern technologies make it possible to liberate your data and thrive in today’s hybrid, multi-cloud, real-time, machine learning world.  Here are three prime examples of innovations that you need to know about:

  • Cloud Computing: The cloud has created new efficiencies and cost savings that organizations never dreamed would be possible. Cloud storage is remote and fluctuates to deliver only the capacity that is needed. It eliminates the time and expense of maintaining on-premise servers, and gives business users real-time self-service to data, anytime, anywhere. There is no hand-coding required, so business users can create integrations between any SaaS and on-premise application in the cloud without requiring IT help. Cloud offers cost, capability and productivity gains that on-premise can’t compete with, and the data disruptors have already entrusted their exploding data volumes to the cloud.
  • Containers: Containers are quickly overtaking virtual machines. According to a recent study, the adoption of application containers will grow by 40% annually through 2020. Virtual machines require costly overhead and time-consuming maintenance, with full hardware and operating system (OS) that needs managed. Containers are portable with few moving parts and minimal maintenance required. A company using stacked container layers pays only for a small slice of the OS and hardware on which the containers are stacked, giving data disruptors unlimited operating potential, at a huge cost savings.
  • Serverless Computing: Deploying and managing big data technologies can be complicated, costly and requires expertise that is hard to find. Research by Gartner states, “Serverless platform-as-a-service (PaaS) can improve the efficiency and agility of cloud services, reduce the cost of entry to the cloud for beginners, and accelerate the pace of organizations’ modernization of IT.”

Serverless computing allows users to run code without provisioning or managing any underlying system or application infrastructure. Instead, the systems automatically scale to support increasing or decreasing workloads on-demand as data becomes available.

Its name is a misnomer; serverless computing still requires servers, but the cost is only for the actual server capacity used; companies are only charged for what they are running at any given time, eliminating the waste associated with on-premise servers.  It scales up as much as it needs to solve that problem, runs it, and scales it back down, turns off. The future is serverless, and its potential to liberate your data is limitless.

Join the Data Disruptors

Now is the time to break free from the legacy trap and liberate your data so its potential can be maximized by your business. In the face of growing data volumes, the data disruptors have realized the potential of the latest cloud-based technologies. Their business and IT teams can work together in a collaborative way, finding an end-to-end solution to the problem, all in a secure and compliant fashion. Harness this innovation and create a completely disruptive set of data economics so your organization can efficiently surf the tidal wave of data.

 

The post How to be Data-Driven when Data Economics are Broken appeared first on Talend Real-Time Open Source Data Integration Software.

Source

Aug 16, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Accuracy check  Source

[ AnalyticsWeek BYTES]

>> How can a few snippets of code help you clean up your database for a faster performance? by thomassujain

>> Jul 27, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> Top Online Video Analytics Tools That You Should Use by thomassujain

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[ NEWS BYTES]

>>
 IoT Is Building Higher Levels Of Customer Engagement – Forbes Under  IOT

>>
 Global Hadoop Market Insights and Trends 2016 – 2022 – Paris Ledger Under  Hadoop

>>
 Cloud Security: 3 Identity and Access Management Musts | CSO … – CSO Online Under  Cloud Security

More NEWS ? Click Here

[ FEATURED COURSE]

Baseball Data Wrangling with Vagrant, R, and Retrosheet

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Analytics with the Chadwick tools, dplyr, and ggplot…. more

[ FEATURED READ]

Introduction to Graph Theory (Dover Books on Mathematics)

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

[ TIPS & TRICKS OF THE WEEK]

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:How do you handle missing data? What imputation techniques do you recommend?
A: * If data missing at random: deletion has no bias effect, but decreases the power of the analysis by decreasing the effective sample size
* Recommended: Knn imputation, Gaussian mixture imputation

Source

[ VIDEO OF THE WEEK]

Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

 Discussing #InfoSec with @travturn, @hrbrmstr(@rapid7) @thebearconomist(@boozallen) @yaxa_io

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

 Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

Subscribe 

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[ FACT OF THE WEEK]

14.9 percent of marketers polled in Crain’s BtoB Magazine are still wondering ‘What is Big Data?’

Sourced from: Analytics.CLUB #WEB Newsletter

The History and Use of R

Looking for a refresher on R? Checkout the following talk covering in depth about R and answering some of the important question concerning it’s adoption.

A presentation on the history, design, and use of R. The talk will focus on companies that use and support R, use cases, where it is going, competitors, advantages and disadvantages, and resources to learn more about R. Speaker Bio

Joseph Kambourakis has been the Lead Data Science Instructor at EMC for over two years. He has taught in eight countries and been interviewed by Japanese and Saudi Arabian media about his expertise in Data Science. He holds a Bachelors in Electrical and Computer Engineering from Worcester Polytechnic Institute and an MBA from Bentley University with a concentration in Business Analytics.

Sponsor: MediaMath | HackReduce

Video:

Slideshare:

Originally Posted at: The History and Use of R

How big data is transforming the construction industry

Big data analytics is being adopted at a rapid rate across every industry. It enables businesses to manage and analyze vast amounts of data at ultrafast speeds, and obtain valuable insights that can improve their decision-making processes.

One of the industries that are reaping the benefits of this technology is the construction industry. Construction companies are using big data to perform a wide range of tasks, from data management to pre-construction analysis.

Here is a look at how big data is transforming the construction industry…

How Construction Companies are Leveraging Big Data Analytics

Handling Large Amounts of Data

Many construction companies need to juggle many projects at the same time, and they have to collect, produce, organize and analyze a lot of data because of these projects.

Other than creating work reports and progress reports, they also have to manage technical information on various aspects of their projects. All the unstructured data that is collected and generated can burden their databases.

Big data solutions make it possible for construction companies to process massive amounts of data at unprecedented speeds, enabling them to save substantial time and effort, and focus more on the job site instead of IT issues.

Depending on which big data tools they use, they can improve almost every data-related process, from database management to report creation.

According to an article entitled “How Big Data is Transforming the World of Finance“, big data can help businesses create reports on their operations more frequently, or in real time, so that they can make well-informed decisions on a consistent basis.

Predicting Risk

In order to plan and execute projects effectively, construction companies need to be able to predict risks accurately through intelligent use of data.

By implementing big data analytics, they can gain valuable insights that enable them to improve cost certainty, identify and avoid potential problems, and find opportunities for efficiency improvements.

One example of a construction company that is using big data analytics to predict risk is Democrata.

Democrata conducts surveys to gain a better understanding of the impact of new roads, high rail links and other construction projects, and uses big data analytics to perform searches and queries on data sets to obtain insights that can lead to better and faster decision-making.

Solving Problems

The ability to solve problems quickly can contribute significantly to the successful completion of construction projects.

Liberty Building Forensics Group is a company that investigates and solves construction and design problems, and it has provided consultation on over 500 projects worldwide, including a Walt Disney project.

According to the company, forensic issues usually occur in major construction projects, and they can cause big problems, such as failure to meet deadlines, if they are not properly assessed.

In order to fix forensic issues efficiently, construction companies have to be able to collect the right data in an organized way and make the data accessible to the right people at the right time. This can be achieved through the implementation of big data solutions.

Presently, big data analytics is relatively immature in terms of functionality and robustness.

As it continues to become more advanced, it will be more widely adopted in the construction industry.

John McMalcolm is a freelance writer who writes on a wide range of subjects, from social media marketing to technology.

Originally posted via “How big data is transforming the construction industry”

Originally Posted at: How big data is transforming the construction industry

Aug 09, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ LOCAL EVENTS & SESSIONS]

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[ AnalyticsWeek BYTES]

>> 2018 Trends in Artificial Intelligence: Beyond Machine Learning for Internal and External Personalization by jelaniharper

>> Customer Loyalty Resource for Customer Experience Professionals by bobehayes

>> Estimating Other “Likelihood to Recommend” Metrics from Your Net Promoter Score (NPS) by bobehayes

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[ NEWS BYTES]

>>
 Goldman Sachs enlists staff for cyber security war games – Financial Times Under  cyber security

>>
 DECKER NAMED TO GOOGLE CLOUD ACADEMIC ALL-DISTRICT® FIRST TEAM – Dominican College Athletics Under  Cloud

>>
 Kadant Inc (NYSE:KAI) Institutional Investor Sentiment Analysis – Frisco Fastball Under  Sentiment Analysis

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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]

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 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:Compare R and Python
A: R
– Focuses on better, user friendly data analysis, statistics and graphical models
– The closer you are to statistics, data science and research, the more you might prefer R
– Statistical models can be written with only a few lines in R
– The same piece of functionality can be written in several ways in R
– Mainly used for standalone computing or analysis on individual servers
– Large number of packages, for anything!

Python
– Used by programmers that want to delve into data science
– The closer you are working in an engineering environment, the more you might prefer Python
– Coding and debugging is easier mainly because of the nice syntax
– Any piece of functionality is always written the same way in Python
– When data analysis needs to be implemented with web apps
– Good tool to implement algorithms for production use

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[ VIDEO OF THE WEEK]

Making sense of unstructured data by turning strings into things

 Making sense of unstructured data by turning strings into things

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[ QUOTE OF THE WEEK]

Data are becoming the new raw material of business. – Craig Mundie

[ PODCAST OF THE WEEK]

Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

 Understanding #BigData #BigOpportunity in Big HR by @MarcRind #FutureOfData #Podcast

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[ FACT OF THE WEEK]

By 2020, at least a third of all data will pass through the cloud (a network of servers connected over the Internet).

Sourced from: Analytics.CLUB #WEB Newsletter