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Karolis Urbonas
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Braulio A Martinez Maureira liked Karolis Urbonas's blog post How to Lie with Data
Jun 19
Ante Bilic liked Karolis Urbonas's blog post How to Lie with Data
Mar 21
Frederick T Williams liked Karolis Urbonas's blog post How to stay out of analytic rabbit holes: avoiding investigation loops and their traps
Mar 14
Dennis Gonzalez liked Karolis Urbonas's blog post How to think like a data scientist to become one
Mar 4
Sanford Lefkowitz commented on Karolis Urbonas's blog post How to Lie with Data
"The mean can be more robust than the median. Suppose your data look like : 1 2 3 4 5 76 77 78 79 The mean is 36,1, the median 5 Add one data point 80 The new mean is 40.5, a relatively small change The new median is 40.5, a huge change, suggesting…"
Dec 19, 2017
Vivek Yadav liked Karolis Urbonas's blog post How to think like a data scientist to become one
Dec 17, 2017
Peter Bruce liked Karolis Urbonas's profile
Dec 4, 2017
Danielh Mendez Carranza liked Karolis Urbonas's blog post What makes a great data scientist?
Oct 28, 2017
Danielh Mendez Carranza liked Karolis Urbonas's blog post What makes a great data scientist?
Oct 28, 2017
Martin Dion Benes commented on Karolis Urbonas's blog post How to think like a data scientist to become one
"Thank you for your insightful and motivating article that simply describes a Data Scientist. I have previously read numerous articles about the topic and was somewhat confused by all the specific abilities and skills stated by the articles. …"
Sep 2, 2017
Martin Dion Benes liked Karolis Urbonas's blog post How to think like a data scientist to become one
Sep 2, 2017
Andrea Valente commented on Karolis Urbonas's blog post How to Lie with Data
"Interesting what you say about the central tendency indicator. Unfortunately, attemps at being more rigorous are not always appreciated. I remember trying to convince a business guy that, at the presence of extreme values in a distribution, the…"
Apr 26, 2017
John L. Ries commented on Karolis Urbonas's blog post How to Lie with Data
"Unless one is deliberately trying to deceive someone else, any false statements made do not constitute lying, but are merely wrong.  On the other hand, technically correct statements made with the intent to mislead are lies (as demonstrated by…"
Apr 24, 2017
John L. Ries liked Karolis Urbonas's blog post How to Lie with Data
Apr 24, 2017
Sandrine FAVIER liked Karolis Urbonas's blog post How to Lie with Data
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Gerald Lucey liked Karolis Urbonas's blog post How to Lie with Data
Apr 20, 2017

Profile Information

Short Bio
Energetic data executive with a demonstrated history of building high-performing data science teams and delivering strategic analytic projects.

Proven track record of influencing and disrupting business strategy for large international organizations with strategic insights through disruption.
My Web Site Or LinkedIn Profile
http://www.cyborgus.com
Years of Experience:
10
Your Company:
Amazon
Your Job Title:
Head of Business Intelligence
Interests:
Finding a new position, Networking, New venture, Recruiting, Other

Karolis Urbonas's Blog

How to Lie with Data

Posted on April 17, 2017 at 10:30am 3 Comments

We expect that data scientists and analysts should be objective and base their conclusions on data. Now while the name of the job implies that “data” is the fundamental material that is used to do their jobs, it is not impossible to lie with it. Quite the opposite – the data scientist is affected by unconscious biases, peer pressure, urgency, and if that’s not enough – there are inherent risks in the process of data analysis and interpretation that lead to lying. It happens all the time…

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Top mistakes data scientists make

Posted on April 3, 2017 at 11:30pm 1 Comment

The rise of the data scientists continues and the social media is filled with success stories – but what about those who fail? There are no cover articles praising the fails of the many data scientists that don’t live up to the hype and don’t meet the needs of their stakeholders.

The job of the data scientist is solving problems. And some data scientists can’t solve them. They either don’t know how to, or are obsessed about the technology part of the craft and forget what the job is…

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How to stay out of analytic rabbit holes: avoiding investigation loops and their traps

Posted on March 28, 2017 at 2:30am 0 Comments

What if we add these variables?..” is a deadly type of a question that can ruin your analytic project. Now, while curiosity is the best friend of a data scientist, there’s a curse that comes with it – some call it analysis paralysis, others – just over-analysis, but I call these situations “analytic rabbit holes”. As you start any data science project – be it an in-depth statistical research, machine learning model, or a simple business analysis – there…

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What makes a great data scientist?

Posted on March 20, 2017 at 12:00am 0 Comments

A data scientist is an umbrella term that describes people whose main responsibility is leveraging data to help other people (or machines) making more informed decisions. The spectrum of data scientist roles is so broad that I will keep this discussion for my next post. What I really want to focus is on what are the distinctive characteristics of a great data scientist.

Over the years that I have worked with data and analytics I have found that this has almost nothing to do with…

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