Jan 07, 21: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

image
Accuracy check  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]

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:Do you know a few “rules of thumb” used in statistical or computer science? Or in business analytics?

A: Pareto rule:
– 80% of the effects come from 20% of the causes
– 80% of the sales come from 20% of the customers

Computer science: “simple and inexpensive beats complicated and expensive” – Rod Elder

Finance, rule of 72:
– Estimate the time needed for a money investment to double
– 100$ at a rate of 9%: 72/9=8 years

Rule of three (Economics):
– There are always three major competitors in a free market within one industry

Source

[ VIDEO OF THE WEEK]

#BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

 #BigData @AnalyticsWeek #FutureOfData #Podcast with @DavidRose, @DittoLabs

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

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

[ PODCAST OF THE WEEK]

@SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

 @SidProbstein / @AIFoundry on Leading #DataDriven Technology Transformation #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

Poor data across businesses and the government costs the U.S. economy $3.1 trillion dollars a year.

Sourced from: Analytics.CLUB #WEB Newsletter

Top 5 Changes That AI Is Set To Have On The Education Industry

AI is everywhere. Whether you are conscious of it or not, the presence of automated tech is overwhelming, with applications in the average individual’s life that won’t even occur to them until it is pointed out. From online shopping, to financial trends, the data revolution is fueling huge amounts of AI technology that is shaping the future of all sorts of different industries. With an almost unlimited potential influence, it’s useful looking at the more unusual areas that AI can have an impact on. One such area is education. The importance of education is so great that it is always worth keeping up to date with how it is changing, so let’s look at 5 ways AI is changing the education industry.

Cutting Down On Admin

One of the biggest hampering forces in education is all the ‘other stuff’. It’s not as simple as sitting in a classroom with a teacher and learning, modern education is a bureaucratic nightmare at times. From medical forms, to safeguarding to insurance, there is a huge amount to worry about beyond the education of students. Artificial Intelligence can be used to automate these sorts of duties with ease and allow teachers to focus on the teaching and spend less time grading tests and calculating scores on a curve. AI will let the education come first in education.

Chatbots In The Classroom

This is an area of AI tech that Is seeing its first outings in a classroom scenario. “After their success in customer service, alternative applications for AI driven chatbots are being explored. One such area is education, where the load of a teacher with a class of 30 or 40 students is being lessened with the help of pre-programmed chatbots who can help answer the more straight forward, binary questions that kids will likely have”, explains Mac Johnson, IT writer at StateOfWriting and Essayroo. This might seem a bit ‘sci-fi’ but it’s actually a pretty simple response to the issue of the increasing burden on teachers, particularly in a high school setting.

Personalize The Learning Path

Personalization is a major benefit of AI. The more data that can be collected on an individual, the more it can be fed to a piece of AI-driven tech which can then decipher the most tailored paths through things like shopping, streaming recommendations and, now, education. “Everyone is different when it comes to education, in what they want to achieve and how they will most effectively achieve it. So using AI to tailor the experience is a no-brainer. Traditional methods of education will leave great swathes of people on either end feeling like they’re not getting what they need. This stops that from occurring”, explains Laura Washington, tech journalist at Academized and Boomessays. Personalization is one of those really great benefits from AI that should be take advantage of whenever possible.

An Education In Technology

The presence of AI in the process of education gives a wonderful opportunity for the woefully under-explored experience of technology education. Aside from those people who actively pursue computer-science degrees, the average individual is actually noticeably ignorant about technology, especially when you consider how important technology is to everyone these days. Introducing AI into standard education will encourage much needed conversations about how things like AI work, which will better prepare people for a world absolutely dominated by technology.

Smart Content

AI presents in many different forms, as we can see. In a lot of instances we barely even know it is there, in other cases it’s in the form of a physical robot that has AI written all over it. AI can help you to digitalize content in a way that feels futuristic, which can help to boost engagement and give an alternative approach to education. Digital content in the curriculum is here to stay and AI can help make it even more efficient.

Conclusion

Artificial Intelligence is such a rich and varied field with such a large range of applications that its integration into technology is as exciting as it is pre-ordained. Users will find their lives made easier and more engaging as they navigate the difficult task of receiving an education.

Aimee Laurence has worked in tech journalism and marketing for the past 3 years at Cheap Assignment and OXEssays. She works mainly on future tech and making technology consumer ready. She also works as a freelance editor at the PaperFellows portal.




Source: Top 5 Changes That AI Is Set To Have On The Education Industry

The What and Where of Big Data: A Data Definition Framework

I recently read a good article on the difference between structured and unstructured data. The author defines structured data as data that can be easily organized. As a result these type of data are easily analyzable. Unstructured data refers to information that either does not have a pre-defined data model and/or is not organized in a predefined manner. Unstructured data are not easy to analyze. A primary goal of a data scientist is to extract structure from unstructured data. Natural language processing is a process of extracting something useful (e.g., sentiment, topics) from something that is essentially useless (e.g., text).

While I like these definitions she offers, she included an infographic that is confusing. It equates the structural nature of the data with the source of the data, suggesting that structured data are generated solely from internal/enterprise systems while unstructured data are generated solely from social media sources. I think it would be useful to separate the format (structure vs. unstructured) of the data from source (internal vs. external) of data.

Sources of Data: Internal and External

Generally speaking, business data can come from either internal sources or from external sources. Internal sources of data reflect those data that are under the control of the business. These data are housed in financial reporting system, operational systems, HR systems and CRM systems, to name a few. Business leaders have a large say in the quality of internal data; they are essentially a byproduct of the processes and systems the leaders use to run the business and generate/store the data.

External sources of data, on the other hand, are any data generated outside the walls of the business. These data sources include social media, online communities, open data sources and more. Due to the nature of source of data, external sources of data are under less control by the business than are internal sources of data. These data are collected by other companies, each using their unique systems and processes.

Data Definition Framework

Data Definition Framework
Figure 1. Data Definition Framework

This 2×2 data framework is a way to think about your business data (See Figure 1). This model distinguishes the format of data from the source of data. The 2 columns represent the format of the data, either structured or unstructured. The 2 rows represent the source of the data, either internal or external. Data can fall into one of the four quadrants.

Using this framework, we see that unstructured data can come from both internal sources (e.g., open-ended survey questions, call center transcripts) and external sources (e.g., Twitter comments, Pinterest images). Unstructured data is primarily human-generated. Human-generated data are those that are input by people.

Structured data also can come from both inside (e.g., survey ratings, Web logs, process control measures) and outside (e.g., GPS for tweets, Yelp ratings) the business. Structured data includes both human-generated and machine-generated data. Machine-generated data are those that are calculated/collected automatically and without human intervention (e.g., metadata).

The quality of any analysis is dependent on the quality of the data. You are more likely to uncover something useful in your analysis if your data are reliable and valid. When measuring customers’ attitudes, we can use customer ratings or customer comments as our data source. Customer satisfaction ratings, due to the nature of the data (structured / internal), might be more reliable and valid than customer sentiment metrics from social media content (unstructured / external); as a result, the use of structured data might lead to a better understanding of your data.

Data format is not the same as data source. I offer this data framework as a way for businesses to organize and understand their data assets. Identify strengths and gaps in your own data collection efforts. Organize your data to help you assess your Big Data analytic needs. Understanding the data you have is a good first step in knowing what you can do with it.

What kind of data do you have?

 

Source: The What and Where of Big Data: A Data Definition Framework

Dec 31, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Conditional Risk  Source

[ AnalyticsWeek BYTES]

>> Why Is Big Data an Advantage for Your Business by thomassujain

>> Conducting Post-COVID19 Technology Gap Analysis in Hotels & Resorts by analyticsweekpick

>> How to Use XGBoost for Time Series Forecasting by administrator

Wanna write? Click Here

[ FEATURED COURSE]

Introduction to Apache Spark

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Learn the fundamentals and architecture of Apache Spark, the leading cluster-computing framework among professionals…. more

[ FEATURED READ]

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]

Data Have Meaning
We live in a Big Data world in which everything is quantified. While the emphasis of Big Data has been focused on distinguishing the three characteristics of data (the infamous three Vs), we need to be cognizant of the fact that data have meaning. That is, the numbers in your data represent something of interest, an outcome that is important to your business. The meaning of those numbers is about the veracity of your data.

[ DATA SCIENCE Q&A]

Q:What is the difference between supervised learning and unsupervised learning? Give concrete examples
?

A: * Supervised learning: inferring a function from labeled training data
* Supervised learning: predictor measurements associated with a response measurement; we wish to fit a model that relates both for better understanding the relation between them (inference) or with the aim to accurately predicting the response for future observations (prediction)
* Supervised learning: support vector machines, neural networks, linear regression, logistic regression, extreme gradient boosting
* Supervised learning examples: predict the price of a house based on the are, size.; churn prediction; predict the relevance of search engine results.
* Unsupervised learning: inferring a function to describe hidden structure of unlabeled data
* Unsupervised learning: we lack a response variable that can supervise our analysis
* Unsupervised learning: clustering, principal component analysis, singular value decomposition; identify group of customers
* Unsupervised learning examples: find customer segments; image segmentation; classify US senators by their voting.

Source

[ VIDEO OF THE WEEK]

Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

 Nick Howe (@Area9Nick @Area9Learning) talks about fabric of learning organization to bring #JobsOfFuture #Podcast

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

You can use all the quantitative data you can get, but you still have to distrust it and use your own intelligence and judgment. – Alvin Tof

[ PODCAST OF THE WEEK]

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

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

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

For a typical Fortune 1000 company, just a 10% increase in data accessibility will result in more than $65 million additional net income.

Sourced from: Analytics.CLUB #WEB Newsletter

Dec 24, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[  COVER OF THE WEEK ]

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Human resource  Source

[ AnalyticsWeek BYTES]

>> 6 Operational Reporting Capabilities to Consider by analyticsweek

>> Discussing #Jobs #Data and #WhatsTheFuture with @TimOReilly #JobsOfFuture #Podcast by v1shal

>> Big Data: The Management Revolution – Harvard Business Review by v1shal

Wanna write? Click Here

[ FEATURED COURSE]

CS109 Data Science

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Learning from data in order to gain useful predictions and insights. This course introduces methods for five key facets of an investigation: data wrangling, cleaning, and sampling to get a suitable data set; data managem… more

[ FEATURED READ]

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

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

[ TIPS & TRICKS OF THE WEEK]

Grow at the speed of collaboration
A research by Cornerstone On Demand pointed out the need for better collaboration within workforce, and data analytics domain is no different. A rapidly changing and growing industry like data analytics is very difficult to catchup by isolated workforce. A good collaborative work-environment facilitate better flow of ideas, improved team dynamics, rapid learning, and increasing ability to cut through the noise. So, embrace collaborative team dynamics.

[ DATA SCIENCE Q&A]

Q:Is it better to have 100 small hash tables or one big hash table, in memory, in terms of access speed (assuming both fit within RAM)? What do you think about in-database analytics?
A: Hash tables:
– Average case O(1)O(1) lookup time
– Lookup time doesn’t depend on size

Even in terms of memory:
– O(n)O(n) memory
– Space scales linearly with number of elements
– Lots of dictionaries won’t take up significantly less space than a larger one

In-database analytics:
– Integration of data analytics in data warehousing functionality
– Much faster and corporate information is more secure, it doesn’t leave the enterprise data warehouse
Good for real-time analytics: fraud detection, credit scoring, transaction processing, pricing and margin analysis, behavioral ad targeting and recommendation engines

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

 @AnalyticsWeek #FutureOfData with Robin Thottungal(@rathottungal), Chief Data Scientist at @EPA

Subscribe to  Youtube

[ QUOTE OF THE WEEK]

For every two degrees the temperature goes up, check-ins at ice cream shops go up by 2%. – Andrew Hogue, Foursquare

[ PODCAST OF THE WEEK]

@CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

 @CRGutowski from @GE_Digital on Using #Analytics to #Transform Sales #FutureOfData #Podcast

Subscribe 

iTunes  GooglePlay

[ FACT OF THE WEEK]

In that same survey, by a small but noticeable margin, executives at small companies (fewer than 1,000 employees) are nearly 10 percent more likely to view data as a strategic differentiator than their counterparts at large enterprises.

Sourced from: Analytics.CLUB #WEB Newsletter

Michael Canic(@MichaelCanic) on Leading with ruthless consistency. Work 2.0 Podcast #FutureofWork #Work2dot0 #Podcast

Michael Canic(@MichaelCanic) on Leading with ruthless consistency. Work 2.0 Podcast #FutureofWork #Work2dot0 #Podcast

In this podcast, Michael Canic discussed his book Ruthless Consistency, the insights it carries, and shared how his journey has helped him craft a strategy that could work on the testing times. He sheds light on the importance of ruthless consistency and how any leader could adopt it in their day to day activities and succeed in leading.

[youtube https://www.youtube.com/watch?v=gVpuMHWyOpQ?feature=oembed&w=850&h=478]

Michael’s Recommended Read:
Lincoln on Leadership: Executive Strategies for Tough Times by Donald T. Phillips amzn.to/2K0h18Q

Michael’s Book:
Ruthless Consistency: How Committed Leaders Execute Strategy, Implement Change, and Build Organizations That Win by Michael Canic amzn.to/3luMF1u

Podcast Link:
iTunes: math.im/jofitunes
Youtube: math.im/jofyoutube

Some questions we covered:
1. Explain your journey to your current role?
2. Could you share something about your current role?
3. What does your company do?
4. Your book is titled, Ruthless Consistency. What does it mean and why is it important?
5. Be consistent. It sounds simple; why isn’t it?
6. What does it look like when leaders are inconsistent?
7. How important is ruthless consistency for leaders during today’s crisis?
8. You suggest that to develop and sustain the right focus, leaders stop strategic planning. Why?
9. To create the right environment, why must leaders be coaches, not just managers?
10. Can you explain why you emphasize holding people constructively accountable?
11. Why is it essential that leaders need to value people?
12. So, the key to success is when a leader acts with ruthless consistency?
13. You write about how commitment is what drives everything. Wouldn’t every leader say they’re committed?
14. Isn’t consistency limiting? Shouldn’t there be room for creativity and innovation?
15. Why ruthless? That sounds harsh.
16. You have a PhD in the psychology of human performance and you helped coach a college football team to a national championship. How did those experiences help shape your views?
17. Who will benefit most from the book?
18. What is the first thing that a leader can do to become ruthlessly consistent?
19. What are 1-3 best practices that you think is the key to success in your journey?
20. Do you have any favorite read?
21. As a closing remark, what would you like to tell our audience?

Michael’s BIO:
Michael Canic, Ph.D., is the author of RUTHLESS CONSISTENCY: How Committed Leaders Execute Strategy, Implement Change and Build Organizations That Win (September 1, 2020; McGraw Hill). He is also the president of Making Strategy Happen, a consultancy which helps committed leaders turn ambition into strategy, and strategy into reality. Previously, he managed the consulting division at The Atlanta Consulting Group and held a leadership role at FedEx. Michael earned a Ph.D. in the psychology of human performance from the University of British Columbia. Currently, Michael leads strategic change initiatives in the corporate world and spent the past 25 years consulting with CEOs and top management teams across North America. A former national championship-winning coach, Michael is also a member of Marshall Goldsmith’s global 100 Coaches project. He lives between Denver and Vancouver and has written over 400 posts for his blog.

About #Podcast:
Work 2.0 Podcast is created to spark the conversation around the future of work, worker, and workplace. This podcast invite movers and shakers in the industry who are shaping or helping us understand the transformation in work.

Wanna Join?
If you or any you know wants to join in,
Register your interest by emailing: info@analyticsweek.com

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

Keywords:
Work 2.0 Podcast,

#FutureOfWork,

#FutureOfWorker,

#FutureOfWorkplace,

#Work,

#Worker,

#Workplace,

Originally posted at: https://work2.org/content/michael-canicmichaelcanic-on-leading-with-ruthless-consistency-work-2-0-podcast-futureofwork-work2dot0-podcast/

The post Michael Canic(@MichaelCanic) on Leading with ruthless consistency. Work 2.0 Podcast #FutureofWork #Work2dot0 #Podcast appeared first on Work2.0 Podcast.

Source by v1shal