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group_work in Data Science, Data Analytics, and Data Professionals in General and in 1 more group

Post from Zacharias 🐝 Voulgaris

How to confuse
machine learning:
Found this gem on LinkedIn. Intriguing how something so simple as a puppy-detection machine learning system can be so easily confused…

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group_work in Data Science, Data Analytics, and Data Professionals in General and in 1 more group

A Modern Data Pipeline

Source: Semantix Brasil   · I generally don't opt for fancy animations and such, but sometimes this is the only way to convey a process' complexity and sophistication. In this case, it's the data science process, often referred to as a data pipeline (it's not the only one, while ...

timer 2 min. reading time · thumb_up 11 relevants · comment 2 comments

group_work in Data Science, Data Analytics, and Data Professionals in General

In Praise of the Data Engineer

Source: LibreOffice Draw & pixabay.com This article is partly inspired by Ken Boddie's article about the various types of engineers and partly from one of the great quotes in Fay's latest article. ·   · The data engineer is the unsung hero of data science. He (that role is usuall ...

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group_work in Data Science, Data Analytics, and Data Professionals in General

Post from Zacharias 🐝 Voulgaris

In the beginning, there was chaos (probably due to some politician). Then data came about. Afterward, data scientists came to be, so that some sense can be made out of all this…

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group_work in Data Science, Data Analytics, and Data Professionals in General

Summing Up the Data at Hand (Sampling Data Optimally)

Source: pixabay.com Summarizing information is one of the most fundamental processes of a sentient being. You'd think that we'd have figured out how to automate this process in the data world by now, creating a reliable summary of a dataset for further analysis. After all, with t ...

timer 2 min. reading time · thumb_up 1 relevant · comment 0 comments

group_work in Data Science, Data Analytics, and Data Professionals in General

Key Characteristics of a Data Science Ready Organization

Source: pixabay.com · Throughout my career, I've worked with various organizations, from one that I'm not at liberty to talk about (it was via Georgia Tech) to others that I'd rather not talk about since their presence is an insult to data science itself! Of course, many of the o ...

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group_work in Data Science, Data Analytics, and Data Professionals in General

Post from Zacharias 🐝 Voulgaris

Wqnyu.jpg
Quick question: what's you biggest pain point in your line of work, related to data?

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Engineer vs. scientist: what is the difference?

Further to @Ken Boddie 's article on engineers : · Some people say there is no difference between a scientist and an engineer, while other people think the two careers are totally separate from each other. Scientists and engineers often have strong opinions about what they do, wh ...

timer 3 min. reading time · thumb_up 5 relevants · comment 6 comments

group_work in Information Technology Professionals and in 2 more groups

Modern Abaci for Modern Times

Source: pixabay.com This article is inspired by Ken's interesting article on the various kinds of Engineers (sens data engineers, who are covered in one of the comments). This article spawned a great discussion which at one point veered towards the abacus as an instrument for the ...

timer 2 min. reading time · thumb_up 4 relevants · comment 3 comments

group_work in Data Science, Data Analytics, and Data Professionals in General

Data Literacy in the Age of Data Analytics

Source: pixabay.com It was about thirty years ago when an uncle of mine, who was around 70 at the time, asked my older nephew (who was one year older than me), to explain to him computers. His rationale was that in the year 2000, anyone who doesn’t understand computers would be c ...

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group_work in Data Science, Data Analytics, and Data Professionals in General

Testing a Hypothesis with BROOM

Source: pixabay.com In data analytics and data science, we often need to test a hypothesis. This is usually a tiresome and confusing process, particularly to those new to the craft. Although formulating a hypothesis (and the corresponding null hypothesis) isn't that hard, testing ...

timer 2 min. reading time · thumb_up 1 relevant · comment 0 comments

group_work in Data Science, Data Analytics, and Data Professionals in General

Towards a New Kind of Statistics for Data Science and Beyond

It's not as fancy as a vacuum cleaner, but it does the job! · Source: pixabay.com · In a previous article, I talked about the limitations of Statistics and how they hinder data science work. Naturally, all these shortcomings also make the whole framework of Stats vulnerable to an ...

timer 4 min. reading time · thumb_up 2 relevants · comment 1 comment

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