May 31, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Convincing  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Understanding the Mobile User Experience Will Help you Build a Better App by bobehayes

>> BOB in The Netherlands by bobehayes

>> The Practice of Customer Experience Management: An Overview by bobehayes

Wanna write? Click Here

[ NEWS BYTES]

>>
 North Hilo Crime Statistics for April – Big Island Now Under  Statistics

>>
 Hadoop & Big Data Analytics Market 2017-2024 Research Report – Technical Progress Under  Hadoop

>>
 Real-time Analytics Requires Modern IT Infrastructure – RTInsights (press release) (blog) Under  Streaming Analytics

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]

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]

Finding a success in your data science ? Find a mentor
Yes, most of us dont feel a need but most of us really could use one. As most of data science professionals work in their own isolations, getting an unbiased perspective is not easy. Many times, it is also not easy to understand how the data science progression is going to be. Getting a network of mentors address these issues easily, it gives data professionals an outside perspective and unbiased ally. It’s extremely important for successful data science professionals to build a mentor network and use it through their success.

[ DATA SCIENCE Q&A]

Q:Provide a simple example of how an experimental design can help answer a question about behavior. How does experimental data contrast with observational data?
A: * You are researching the effect of music-listening on studying efficiency
* You might divide your subjects into two groups: one would listen to music and the other (control group) wouldn’t listen anything!
* You give them a test
* Then, you compare grades between the two groups

Differences between observational and experimental data:
– Observational data: measures the characteristics of a population by studying individuals in a sample, but doesn’t attempt to manipulate or influence the variables of interest
– Experimental data: applies a treatment to individuals and attempts to isolate the effects of the treatment on a response variable

Observational data: find 100 women age 30 of which 50 have been smoking a pack a day for 10 years while the other have been smoke free for 10 years. Measure lung capacity for each of the 100 women. Analyze, interpret and draw conclusions from data.

Experimental data: find 100 women age 20 who don’t currently smoke. Randomly assign 50 of the 100 women to the smoking treatment and the other 50 to the no smoking treatment. Those in the smoking group smoke a pack a day for 10 years while those in the control group remain smoke free for 10 years. Measure lung capacity for each of the 100 women.
Analyze, interpret and draw conclusions from data.

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek Panel Discussion: Big Data Analytics

 @AnalyticsWeek Panel Discussion: Big Data Analytics

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Every person in the world having more than 215m high-resolution MRI scans a day.

Sourced from: Analytics.CLUB #WEB Newsletter

What Marketers Really Need To Know About Big Data

Big data is a game changer. But is big data a tech or marketing tool? The answer is both. Big data plays a role in marketing campaigns and gathering insight. To utilize big data most effectively, marketers must understand its use in the following roles.

salesforce

Trends and Predictive Analytics

Salesforce.com highlights the role of Google Trends as a key player. Google Trends takes big data analysis to the big leagues. It cuts out all of the current trending topics and focuses on the ones with the greatest reach, quantifying the frequency of each searched term and comparing it to the total amount of searches. This helps marketers to expand their reach more than ever. It’s easy to get complacent with search words you already know. With tools like Google Trends, you can find new trends and new markets. Think about the growth of video screens in places of worship, for example. Marketers consistently focus on entertainment and office environments that need large video screens. But by using tools like Google trends can you see the demand and need for services outside of your usual targets and grow to meet changing needs and expanding your reach.

Predictive analytics is another big data strategy. Predictive analytics is an area of data mining that extracts information from existing data sets to determine behavior patterns and predict future trends. Forbes talks about its progressive and aggressive nature. It looks at scores of historical data and analyzes it with incredible speeds. It can literally pinpoint the exact process that makes for successful leads. Predictive data allows marketers to learn more about their target customers than ever before. Using predictive data analytics will drastically change your selling cycles. Instead of waiting for your clients to request additional services, you can predict their needs.

Persona Creation

Much like SEO techniques in the past, big data will change the way we create and use buyer personas. Buyer personas are generally use in marketing efforts to pinpoint a certain customer. According to Salesforce.com, companies usually create personas from data gleaned from their websites and from feedback from sales teams and call centers, which misses huge pools of data. Using big data, social media, blog posts, and marketing campaigns can be geared to more specific customers by targeting demographics in much larger ways. A multimarket approach breaks the generic mould. A small business owner will use your services if they see blog posts geared to them; this is the same for other markets like health care and education. By gearing posts to different people, you gain loyalty from a larger group of markets.

Personalization and Customization

Every marketer knows the effectiveness of personalization and customization. When your customers think you are talking to them directly, they become loyal to your brand. It’s also important to make sure you send the right message at the right time to secure customer loyalty. Using big data, you can see who’s interacting with your brand in real time. Armed with this information, marketers can send personalized and customized responses. You can merge big data with existing CRM practices, and keep tabs on trends of potential buyers as well as current customers. As you track these patterns, you can send customers individualized content creating greater customer retention. Let’s say your customer is looking for new services for the latest project they are taking on. By sending personalized content, you create a relationship that makes them feel like you care about their success. This is how you create loyalty.

Marketers who ignore big data, even small data, will be left behind. Don’t let that be you.

To read the original article on HuffPost Business, click here.

Source: What Marketers Really Need To Know About Big Data by analyticsweekpick

Improve the Customer Experience by Adopting Customer Feedback Best Practices

tcelab_taglineTake the Customer Feedback Best Practices Survey now and receive free executive summary of results

Collecting, centralizing and acting on customer feedback is an important foundation for building a winning customer experience management (CEM) program. Past research shows that adoption of specific business practices improves the effectiveness of CEM programs (see results of this prior research here). Clicktools, TCELab and SurveyMonkey are working together to continue this research to clarify the earlier findings and extend our understanding about what works and what doesn’t.

The research examines six critical areas of a customer feedback program:

clicktools

  • Strategy
  • Governance
  • Integration with the business
  • Methods of data collection
  • Reporting of results
  • Research, analysis and action

surveymonkey

As thanks for completing the survey, you will receive a free executive summary of the results which will help you understand how you can improve your feedback program in ways that will help you accelerate business growth. The report will examine how practices differ across industries, organisation size and geography.

The research will help companies:

  1. Identify their CEM program’s strengths and weaknesses
  2. Understand how to improve the CEM program
  3. Facilitate their customer experience improvement efforts
  4. Increase customer loyalty to accelerate business growth

Take the Customer Feedback Best Practices Survey now and receive free executive summary of results.

————————–

tcethebookfinalsmall

 

Buy TCE: Total Customer Experience at Amazon >>

In TCE: Total Customer Experience, learn more about how you can integrate your business data around the customer and apply a customer-centric analytics approach to gain deeper customer insights. Also, learn about how Oracle integrates their business data around the customer to draw greater insights for improved customer loyalty and business results.

 

 

 

 

Source: Improve the Customer Experience by Adopting Customer Feedback Best Practices

May 24, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Data interpretation  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ NEWS BYTES]

>>
 The case for one giant, multibillion-dollar cloud contract for DoD – C4ISRNet Under  Cloud

>>
 Streaming Analytics Market Is Constantly Growing On Account Of the Increasing Operational Efficiency And Production … – Expert Consulting Under  Streaming Analytics

>>
 ‘Salah’s statistics in his debut season at Anfield are quite astonishing’ – how the papers saw Liverpool FC’s … – Daily Post North Wales Under  Statistics

More NEWS ? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

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Learn scalable data management, evaluate big data technologies, and design effective visualizations…. more

[ FEATURED READ]

The Future of the Professions: How Technology Will Transform the Work of Human Experts

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This book predicts the decline of today’s professions and describes the people and systems that will replace them. In an Internet society, according to Richard Susskind and Daniel Susskind, we will neither need nor want … 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 Tufte’s concept of ‘chart junk’?
A: All visuals elements in charts and graphs that are not necessary to comprehend the information represented, or that distract the viewer from this information

Examples of unnecessary elements include:
– Unnecessary text
– Heavy or dark grid lines
– Ornamented chart axes
– Pictures
– Background
– Unnecessary dimensions
– Elements depicted out of scale to one another
– 3-D simulations in line or bar charts

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]

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

[ 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]

29 percent report that their marketing departments have ‘too little or no customer/consumer data.’ When data is collected by marketers, it is often not appropriate to real-time decision making.

Sourced from: Analytics.CLUB #WEB Newsletter

How To Turn Your Data Into Content Marketing Gold

With every brand out there becoming a publisher, it’s harder than ever to make your content stand out. Each day, you have a choice. You can play it safe and do what everyone else is doing: re-blog the same industry studies and curate uninspired listicles. Or you can be original and craft a story that only you can tell. The good news for most of you: there is content gold right under your nose. If used correctly, this will enable you to create truly compelling content that is not only shareable, but will set you apart from your peers.

The gold is your own data.

This data is often used to inform your business strategies and tactics, such as assessing which headlines performed better or what time of day you should tweet. And while those things are important, we’re talking about a close cousin of those efforts. This is about looking at the data your team has gathered and analyzed, and identifying original insights that you can craft into engaging stories to fuel your content marketing.

Visage, a platform meant to help marketers create branded visual content, conducted a survey of 504 marketers to see just how well they are taking advantage of this opportunity for original data storytelling. 75% of those surveyed are directly responsible for creating content, and 75% work in a company with 10 or less people working in their marketing department. Here’s what they found out:

1. Everyone is creating a lot of content

Most organizations (73%) publish original content on at least a weekly basis, and many (21%) are publishing content multiple times per day. Most brands are doing this because they know that if you aren’t sharing your latest thinking with the digital world (or at least being entertaining), your brand doesn’t exist for most people outside of your family.

content marketing data

2. It’s still not enough

Relatively few modern marketers believe that their organization creates enough original content. The fact is, as anyone who has rescheduled dates on an editorial calendar knows, getting into a publishing rhythm is hard. We can get enamored or overwhelmed by other brands who we see publishing a high volume of content. In such a state, it’s easy for some to play copycat and fall into regurgitating news and curating stories covered by other people. But your real challenge is differentiating from competitors and earning the trust of a potential customer. So, you need to use your limited resources to give yourself a shot for your content to either stand out and be remembered. Otherwise, it will be just one little drop flowing past in the social river.

content marketing data

3. Marketers are sitting on gold

Visage’s survey found that 41% of organizations are doing original market research more than once per year. Conducting a quick survey or poll is one powerful way to create a fresh, original story that hasn’t been told before. Start with a small experiment aimed at helping you understand your own market better, and keep your ideal customer profile in mind as you write your questions. The advantage to this approach is that you can structure your data collection and save yourself the time and money associated with cleaning up and organizing outside data. Finally, format your questions to gather the information and answers that you know your audience will find valuable.

content marketing data

4. Marketers aren’t using their data to its full potential.

The biggest shocker was that 60% of respondents claim to be sitting on interesting data, but only 18% are publishing it externally. There are many valid reasons to keep your internal data private (eg. security, competitive advantage), but you don’t need to take an all-or-nothing approach to this. For example, there’s a big opportunity to share aggregated trends and behaviors. Spotify does this with their music maps, and OKcupid does this with theirOKTrends blog.

content marketing data

5. They see the opportunity

Brand marketers aren’t just hoarding this gold. 82% of companies said it was important or extremely important that their marketing team learn to tell better data stories. You might notice the growing number of situations that require you to communicate with data in your own work, even just in your own internal reports and presentations.

content marketing data

6. The struggle is real

So, if so many marketers are sitting on interesting data and think it is important to craft original stories from it – why isn’t it happening? As the survey showed, many marketers don’t feel they have the skills or tools to craft the story from their data. Only 34% feel their teams have above average data literacy. Even when the data is cleaned, analyzed and ready to be visualized, modern marketers still have a hard job to do. Your audience needs context, and a strong narrative is a key ingredient of communicating with data. Often, the most successful data stories come as a result of combining powerful talents – the journalist working with a graphic designer, or a content marketer working closely with a data analyst. Get both sides of the brain firing in your content creation, even if you need to combine forces.

content marketing data
content marketing data

7. How to get started

Like any new marketing initiative, success in crafting original data stories as a means of differentiating your brand will take time and money. Start where you are and do what you can, even if it feels microscopic at first. If the prospect of getting rolling with your own data seems overwhelming, get some practice with public data available from credible sources like the Census Bureau or Pew Research. The cool news is that it’s easier than ever to get started with a plethora of great tools and educational material on the web.

Data storytelling is a skill that modern marketers can and must learn. If you are committed to creating original content that makes your brand shine, consider the precious gold insights that are ready to be mined from your data to provide tangible value to your audience.

To read the original article on NewsCred, click here.

Source: How To Turn Your Data Into Content Marketing Gold

Measuring The Customer Experience Requires Fewer Questions Than You Think

Figure 1. Three Phases of the Customer Lifecycle

A formal definition of customer experience, taken from Wikipedia, states that customer experience is: “The sum of all experiences a customer has with a supplier of goods or services, over the duration of their relationship with that supplier.” In practical terms, customer experience is the customer’s perception of, and attitude about, different areas of your company or brand across the entire customer lifecycle (see Figure 1 to right).

We know that the customer experience has a large impact on customer loyalty. Customers who are satisfied with the customer experience buy more, recommend you and are easier to up/cross-sell than customers who are dissatisfied with the customer experience. Your goal for the customer relationship survey, then, is to ensure it includes customer experience questions asking about important customer touchpoints.

Table 1. General and Specific Customer Experience Questions. In practice, survey asks customers to rate their satisfaction with each area.

Customer Experience Questions

Customer experience questions typically account for most of the questions in customer relationship surveys. There are two types of customer experience questions: General and Specific. General questions ask customers to rate broad customer touchpoints. Specific customer experience questions focus on specific aspects of the broader touchpoints.  As you see in Table 1, general customer experience questions might ask the customers to rate their satisfaction with 1. Product Quality, 2. Account Management, 3. Technical Support and so on. Specific customer experience questions ask customers to rate their satisfaction with detailed aspects of each broader customer experience area.

I typically see both types of questions in customer relationship surveys for B2B companies. The general experience questions are presented first and then are followed-up with specific experience questions. As such, I have seen customer relationship surveys that have as little as five (5) customer experience questions and other surveys that have 50+ customer experience questions.

Figure 2. General Customer Experience Questions

General Customer Experience Questions

Here are some general customer experience questions I typically use as a starting point for helping companies build their customer survey. As you can see in Figure 2, these general questions address broad areas across the customer lifecycle, from marketing and sales to service.

While specific customer experience questions are designed to provide greater understanding of customer loyalty, it is important to consider their usefulness. Given that we already have general customer loyalty question in our survey, do we need the specific questions? Do the specific questions help us explain customer loyalty differences above what we know through the general questions?

Customer Experience Questions Predicting Customer Loyalty

To answer these questions, I analyzed four different B2B customer relationship surveys, each from four different companies. These companies represented midsize to large enterprise companies. Their semi-annual customer surveys included a variety of loyalty questions and specific and general customer experience questions. The four companies had different combinations of general (5 to 7) and specific customer experience questions (0 to 34).

Figure 3. Impact of General and Specific Customer Experience Questions on Customer Loyalty (overall sat, recommend, buy again). Percent of variability is based on stepwise regression analysis.

The goal of the analysis was to show whether the inclusion of specific experience questions added to our understanding of customer loyalty differences beyond what the general experience questions explained. The results of the analysis are presented in Figure 3.  Through step-wise regression analysis, I first calculated the percent of variance in customer loyalty that is explained by the general customer experience questions (green area). Then, I calculated the percent of variance in customer loyalty explained by the specific questions above what the general questions explained (blue area). Clearly, the few general experience questions explain a lot of the variability in customer loyalty (42% to 85%) while the specific customer experience questions account for very little extra (2% to 4%).

Efficient Customer Relationship Surveys

We may be asking customers too many questions in our relationship surveys. Short relationship surveys, using general experience questions, provide great insight into understanding how to improve customer loyalty. Asking customers about specific, detailed aspects about their experience provides very little additional information about what drives customer loyalty.

Customers’ memories are fallible.  Given the non-trivial time between customer relationship surveys (up to a year between surveys), customers are unable to make fine distinctions regarding their experience with you (as measured in your survey). This might be a good example of the halo effect, the idea that a global evaluation of a company/brand (e.g., great product) influences opinions about their specific attributes (e.g., reliable product, ease of use).

Customers’ ratings about general customer experience areas explain as much of the differences in customer loyalty as we are able to with customer experience questions. Short relationship surveys allow customers the optimal way to give their feedback on a regular basis. Not only do these short relationship surveys provide deep customer insight about the causes of customer loyalty, they also enjoy higher response rates and show that you are considerate of customers’ time.

Source: Measuring The Customer Experience Requires Fewer Questions Than You Think by bobehayes

May 17, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

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Big Data knows everything  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Customer Service Excellence in 8 steps! by martin

>> Underpinning Enterprise Data Governance with Machine Intelligence by jelaniharper

>> Navigating Big Data Careers with a Statistics PhD by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 Startup Dremio Promises Improved Big Data Access For Business Analytics With New Release – CRN Under  Business Analytics

>>
 Teladoc taps IBM Watson machine learning for second opinion service – Healthcare IT News Under  Machine Learning

>>
 Amazon or no, banks are in for big changes, one analyst says … – MarketWatch Under  Financial Analytics

More NEWS ? Click Here

[ 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]

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]

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:What do you think about the idea of injecting noise in your data set to test the sensitivity of your models?
A: * Effect would be similar to regularization: avoid overfitting
* Used to increase robustness

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

 #BigData @AnalyticsWeek #FutureOfData #Podcast with Eloy Sasot, News Corp

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

It is a capital mistake to theorize before one has data. Insensibly, one begins to twist the facts to suit theories, instead of theories to

[ PODCAST 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

Subscribe 

iTunes  GooglePlay

[ 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

@chrisbishop on futurist’s lens on #JobsOfFuture

[youtube https://www.youtube.com/watch?v=L1nLwjiB32A]

@chrisbishop on futurist’s lens on #JobsOfFuture #FutureofWork #JobsOfFuture #Podcast

In this podcast Christopher Bishop, Chief Reinvention Officer, Improvising Careers talks about his journey as a multimodal careerists, and his past as a rockstar. He shared some of hacks / best practices that businesses could adopt to better work through new age of work, worker and workplace. This podcast has lots of thought leadership perspective for future HR leaders.

Chris’s Recommended Reads:
The Industries of the Future by Alec Ross https://amzn.to/2rPjQlo
Disrupted: My Misadventure in the Start-Up Bubble by Dan Lyons https://amzn.to/2k1RAIT
Breakout Nations: In Pursuit of the Next Economic Miracles by Ruchir Sharma https://amzn.to/2KwERcy
How We Got to Now: Six Innovations That Made the Modern World by Steven Johnson https://amzn.to/2L7Dn9v
The New Rules of Work: The Modern Playbook for Navigating Your Career by Alexandra Cavoulacos and Kathryn Minshew https://amzn.to/2rMjU5F

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

Chris’s BIO:
Christopher Bishop has had many different careers since he graduated from Bennington College with a B.A. in German literature. He has worked as a touring rock musician (played with Robert Palmer), jingle producer (sang on the first Kit Kat jingle “Gimme A Break”) and Web site project manager (developed Johnson & Johnson’s first corporate Web site). Chris also spent 15 years at IBM in a variety of roles including business strategy consultant and communications executive driving social media adoption and use of virtual worlds.

Chris is a member of the World Future Society and gave a talk at their annual conference in Washington, D.C. last summer on “How to Succeed at Jobs That Don’t Exist Yet.” In addition, he’s on the Board of TEDxTimesSquare and gave a talk on *Openness* at the New York event in April 2013.

Chris writes, consults and speaks about “improvising careers” at universities and industry conferences.

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: @chrisbishop on futurist’s lens on #JobsOfFuture

Ashok Srivastava(@aerotrekker) @Intuit on Winning the Art of #DataScience #FutureOfData #Podcast

[youtube https://www.youtube.com/watch?v=I5yZfhd-ZQY]

Ashok Srivastava(@aerotrekker) @Intuit on Winning the Art of #DataScience #FutureOfData

Youtube: https://youtu.be/I5yZfhd-ZQY
iTunes: https://apple.co/2FAZgz2

In this podcast Ashok Srivastava(@aerotrekker) talks about how the code of creating a great data science practice goes through #PeopleDataTech and he suggested how to handle unreasonable expectations from reasonable technologies. He shared his journey through culturally diverse organizations and how he successfully build data science practice. He shared his role in Intuit and some of the AI/Machine learning focus in his current role. This podcast is a must for all data driven leaders, strategists and wannabe technologists who are tasked to grow their organization and build a robust data science practice.

Ashok’s Recommended Read:
Guns, Germs, and Steel: The Fates of Human Societies – Jared Diamond Ph.D. http://amzn.to/2C4bLMT
Collapse: How Societies Choose to Fail or Succeed: Revised Edition – by Jared Diamond http://amzn.to/2C3Bu8f

Podcast Link:
iTunes: http://math.im/itunes
GooglePlay: http://math.im/gplay

Ashok’s BIO:
Ashok N. Srivastava, Ph.D. is the Senior Vice President and Chief Data Officer at Intuit. He is responsible for setting the vision and direction for large-scale machine learning and AI across the enterprise to help power prosperity across the world. He is hiring hundreds of people in machine learning, AI, and related areas at all levels.

Previously, he was Vice President of Big Data and Artificial Intelligence Systems and the Chief Data Scientist at Verizon. He is an Adjunct Professor at Stanford in the Electrical Engineering Department and is the Editor-in-Chief of the AIAA Journal of Aerospace Information Systems. Ashok is a Fellow of the IEEE, the American Association for the Advancement of Science (AAAS), and the American Institute of Aeronautics and Astronautics (AIAA).

Ashok has a range of business experience including serving as Senior Director at Blue Martini Software and Senior Consultant at IBM.

He has won numerous awards, including the Distinguished Engineering Alumni Award, the NASA Exceptional Achievement Medal, IBM Golden Circle Award, the Department of Education Merit Fellowship, and several fellowships from the University of Colorado. Ashok holds a Ph.D. in Electrical Engineering from the University of Colorado at Boulder.

About #Podcast:
#FutureOfData 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.

Wanna Join?
If you or any you know wants to join in,
Register your interest @ http://play.analyticsweek.com/guest/

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

Keywords:
#FutureOfData #DataAnalytics #Leadership #Podcast #BigData #Strategy

Source

May 10, 18: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[  COVER OF THE WEEK ]

image
Pacman  Source

[ LOCAL EVENTS & SESSIONS]

More WEB events? Click Here

[ AnalyticsWeek BYTES]

>> Dec 21, 17: #AnalyticsClub #Newsletter (Events, Tips, News & more..) by admin

>> â€œPutting Data Everywhere”: Leveraging Centralized Business Intelligence for Full-Blown Data Culture by jelaniharper

>> Finance Best Practices Are Changing—Is Your Organization Keeping Pace? by analyticsweekpick

Wanna write? Click Here

[ NEWS BYTES]

>>
 6 Tips for Keeping Iot Devices Safe – Security Sales & Integration Under  IOT

>>
 Software AG ramps up Australian push with IoT platform – IoT Hub Under  Streaming Analytics

>>
 Customer experience in a new dimension: 3D Augmented Reality App Mercedes cAR and Virtual Reality goggles … – Automotive World (press release) Under  Customer Experience

More NEWS ? Click Here

[ FEATURED COURSE]

Python for Beginners with Examples

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A practical Python course for beginners with examples and exercises…. 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]

Winter is coming, warm your Analytics Club
Yes and yes! As we are heading into winter what better way but to talk about our increasing dependence on data analytics to help with our decision making. Data and analytics driven decision making is rapidly sneaking its way into our core corporate DNA and we are not churning practice ground to test those models fast enough. Such snugly looking models have hidden nails which could induce unchartered pain if go unchecked. This is the right time to start thinking about putting Analytics Club[Data Analytics CoE] in your work place to help Lab out the best practices and provide test environment for those models.

[ DATA SCIENCE Q&A]

Q:What is star schema? Lookup tables?
A: The star schema is a traditional database schema with a central (fact) table (the “observations”, with database “keys” for joining with satellite tables, and with several fields encoded as ID’s). Satellite tables map ID’s to physical name or description and can be “joined” to the central fact table using the ID fields; these tables are known as lookup tables, and are particularly useful in real-time applications, as they save a lot of memory. Sometimes star schemas involve multiple layers of summarization (summary tables, from granular to less granular) to retrieve information faster.

Lookup tables:
– Array that replace runtime computations with a simpler array indexing operation

Source

[ VIDEO OF THE WEEK]

@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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[ QUOTE OF THE WEEK]

War is 90% information. – Napoleon Bonaparte

[ 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]

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

Sourced from: Analytics.CLUB #WEB Newsletter