A quick guide on the differences between quantitative and qualitative data

There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

Defining the type of variable you are working with is always the first step in…


A quick guide on the basic concepts used to describe data

“Facts are stubborn things, but statistics are pliable”

― Mark Twain

The job of a data analyst is not to come up with a lot of fancy reports containing tons of data as it may first seem. He needs to understand what the data can tell the business or help it solve existing problems.

Okay, we have data what’s next?

The next step is to get insights from them.

What are insights?

Insights are valuable knowledge obtained with the help of data analytics. It’s a very general definition because under the category of insides can fall a lot of things…


A quick guide on the basic concepts used to describe data

“Facts are stubborn things, but statistics are pliable”

― Mark Twain

The job of a data analyst is not to come up with a lot of fancy reports containing tons of data as it may first seem. He needs to understand what the data can tell the business or help it solve existing problems.

Okay, we have data what’s next?

The next step is to get insights from them.

What are insights?

Insights are valuable knowledge obtained with the help of data analytics. It’s a very general definition because under the category of insides can fall a lot of things…


A quick guide on the differences between quantitative and qualitative data

There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

The whole series:

Defining the type of variable you are working with is always the…


There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

The whole series:

Defining the type of variable you are working with is always the…


There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

The whole series:

Defining the type of variable you are working with is always the…


There are a lot of engineers who have never been involved in the field of statistics or data science. But in order to build data science pipelines or rewrite produced code by data scientists to an adequate, easily maintained code many nuances and misunderstandings arise from the engineering side. For those Data/ML engineers and novice data scientists, I make this series of posts. I’ll try to explain some basic approaches in plain English and, based on it, explain some of the Data Science basic concepts.

The whole series:


In the process, developers often have to update their services and deploy them. When the team and the number of services are small, this is not a problem because releases and deployments are rare. Tests, release management, publishing artifacts, and deployments can be run manually. But over time, the number of services and tasks increases, the cognitive load increases even more, and the release cycle starts to fail if you don’t do it often and get bogged down in running its individual steps.

Let’s look at the typical process of feature implementation/bug fixing for the majority of projects:

  • Create a…


My friend asked me an interesting question about what skills are worth learning for Data Management specialists and how to build a grow roadmap.

In fact, the question made me think because I haven’t had a clear picture in my head. It’s just my thoughts on the topic and for the most part, I’m just speculating about the current state and the future of Data Management.

Prerequisites

In the beginning, as in any other area, there are basic things that any Software Engineer should know.

In short, I assume that the person who came to the Big Data already knows some…


My friend asked me an interesting question about what skills are worth learning for Data Management specialists and how to build a grow roadmap.

In fact, the question made me think because I haven’t had a clear picture in my head. It’s just my thoughts on the topic and for the most part, I’m just speculating about the current state and the future of Data Management.

Prerequisites

In the beginning, as in any other area, there are basic things that any Software Engineer should know.

In short, I assume that the person who came to the Big Data already knows some…

Kirill Bobrov

helping robots conquer the earth and trying not to increase entropy using Python, Big Data, ML. Linkedin @luminousmen. Check out my blog — luminousmen.com

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