December 2020 – Analytics51

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

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[ COVER OF THE WEEK ]

Conditional Risk Source

[ 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

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[ 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

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[ 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

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Originally Posted at:

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

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[ COVER OF THE WEEK ]

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

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

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 doesnt depend on size

Even in terms of memory:
– O(n)O(n) memory
– Space scales linearly with number of elements
– Lots of dictionaries wont 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 doesnt 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

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[ 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

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 PodcastÂ #FutureofWorkÂ #Work2dot0Â #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]

Michaelâs Recommended Read:
Lincoln on Leadership: Executive Strategies for Tough Times by Donald T. PhillipsÂ amzn.to/2K0h18Q

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

Podcast Link:
iTunes:Â math.im/jofitunes
Youtube:Â 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 isnât it?
6. What does it look like when leaders are inconsistent?
7. How important is ruthless consistency for leaders during todayâs 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. Wouldnât every leader say theyâre committed?
14. Isnât consistency limiting? Shouldnât 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?

Michaelâs 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 Goldsmithâs global 100 Coaches project. He lives between Denver and Vancouver and has written over 400 posts for his blog.

AboutÂ #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:Â info@analyticsweek.com

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

Keywords:
Work 2.0 Podcast,

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.

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Dec 17, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

[ COVER OF THE WEEK ]

Weak data Source

[ AnalyticsWeek BYTES]

>> Professional Data Recovery Services and Its Amazing Benefits by thomassujain

>> Technology, People, and Process: 3 Pillars of Construction Digital Transformation by analyticsweekpick

>> March 13, 2017 Health and Biotech analytics news roundup by pstein

Wanna write? Click Here

[ FEATURED COURSE]

Tackle Real Data Challenges

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

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

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]

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: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

Source

[ VIDEO OF THE WEEK]

@AnalyticsWeek: Big Data at Work: Paul Sonderegger

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[ QUOTE OF THE WEEK]

The data fabric is the next middleware. Todd Papaioannou

[ PODCAST OF THE WEEK]

Solving #FutureOfOrgs with #Detonate mindset (by @steven_goldbach & @geofftuff) #FutureOfData #Podcast

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[ FACT OF THE WEEK]

571 new websites are created every minute of the day.

Sourced from: Analytics.CLUB #WEB Newsletter

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Dec 10, 20: #AnalyticsClub #Newsletter (Events, Tips, News & more..)

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[ COVER OF THE WEEK ]

Accuracy check Source

[ FEATURED COURSE]

Data Mining

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

[ FEATURED READ]

The Black Swan: The Impact of the Highly Improbable

[ TIPS & TRICKS OF THE WEEK]

Analytics Strategy that is Startup Compliant
With right tools, capturing data is easy but not being able to handle data could lead to chaos. One of the most reliable startup strategy for adopting data analytics is TUM or The Ultimate Metric. This is the metric that matters the most to your startup. Some advantages of TUM: It answers the most important business question, it cleans up your goals, it inspires innovation and helps you understand the entire quantified business.

[ DATA SCIENCE Q&A]

Q: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

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