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Karolis Urbonas
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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
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
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Apr 17
Karolis Urbonas's blog post was featured

How to Lie with Data

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 while…See More
Apr 17
Karolis Urbonas posted a blog post

How to Lie with Data

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 while…See More
Apr 17
Bernardo Gomes commented on Karolis Urbonas's blog post Top mistakes data scientists make
"Nice post. The communication skill is the most important thing in my opinion.  Keep up the good work.  Best regards, "
Apr 6
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Apr 5

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
Field of Expertise
Analytics, Data Integration, BI, Big Data, Data Science
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 2 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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