Complex systems often appear chaotic or incomprehensible, yet closer examination reveals that such complexity can frequently be reduced to a simple underlying mechanism. By systematically removing layers of emergent behavior, one can uncover a fundamental rule or equation from which the entire system originates.
In this article, I’m going to discuss something really important. If you’re a data analyst or you want to learn data analysis, please watch this video till the end because it’s really important.
According to the Oxford Dictionary, empathy is “the ability to understand and share the feelings of another”. In UX, there is a special term “user empathy”. It refers to the ability of UX designers to fully understand what users need from a particular software product. Having user empathy and basing design solutions around users” comfort is one of the most true indicators of a designer”s professionalism. Without that, any product a designer works on has a high chance of turning out to be pointless. Apart from having empathy as a soft skill in general, there are several ways a designer can practice user empathy through different UX methods and techniques. In this article we would like to talk on how a UX designer can treat users with empathy and make the product more accessible for different groups of target audience.
It's been a while since my last appearance, but I'm excited to be back and to share something truly special with you. In this article, we'll explore my top 10 Google Sheets features that are guaranteed to boost your productivity, speed up your workflow, and make your data handling more efficient. So, without further ado, let's dive into these game-changing tools!
In the realm of data visualization, where insight meets aesthetics, Matplotlib stands as a towering beacon of versatility and creativity. As one of the most popular plotting libraries in Python, Matplotlib empowers data scientists, analysts, and enthusiasts alike to transform raw data into captivating visual narratives. Let us embark on a journey through the vibrant landscapes of Matplotlib, exploring its features, capabilities, and the artistry it inspires.
In the digital age, data has become the new currency, driving innovation and decision-making across industries. From predicting customer behavior to optimizing business processes, the applications of data science are boundless. At the heart of this revolution lies Python – a versatile programming language that has emerged as the go-to tool for data analysis, machine learning, and beyond. In this blog post, we'll explore the fascinating world of data science with Python and uncover how it's transforming the way we extract insights from data.
Today we’re diving into an exciting feature within ChatGPT that has the potential to enhance your productivity by 10, 20, 30, or even 40%. If you’re keen on learning how to leverage this feature to your advantage, make sure to read this article until the end. This feature stands out because it allows you to analyze almost anything by uploading your data and posing various questions to ChatGPT. Whether it's business data, your resume, or any other information you wish to explore, ChatGPT is here to deliver answers based on your specific dataset.
In the modern world, here and there ideas are arising about using data science for an extra benefit. For instance, Google can use a history of watched videos for providing recommendations about new ones. Online shops are using a recommendation system for increasing your receipt. However… if companies use the data for their benefit, could we do the same for own needs such as looking an online English teacher?
It is an approach based on my own experience and can be unsuitable to your point of view, ideas, or principles.
People who use Kibana in our company have different background — some of them are technical who process data, some are managers who simply want to monitor some KPIs. And all have various questions. In spite of Kibana is rather popular in IT companies, there are not many articles or courses about it. To fill the gap I have created Kibana Tips & Tricks — weekly letters with frequently asked questions or themes. Such letters help our users to become more familiar with Kibana. There are no secrets — just detailed description of how you may work with your data.I would like to share the first part of 'Kibana Tips & Tricks' with you — series of simple how-to articles for people who would like to know more about data analysis and visualization in Kibana. Today we will see how to view events in Kibana.
Hello, Habr!
About a month ago, I had a feeling of constant anxiety. I began to eat poorly, sleep even worse, and constantly read to a ton of news about the pandemic. Based on them, the coronavirus either captured, or liberated our planet, was either a conspiracy of world governments, or the vengeance of the pangolin, the virus either threatened everyone at once, or personally me and my sleeping cat…
Hundreds of articles, social media posts, youtube-telegram-instagram-tik-tok (yes, I sin) content of varying degrees of content quality did not lead me to anything but an even greater sense of anxiety.
But one day I bought buckwheat decided to end it all. As soon as possible!
Have you thought about the influence of the nearest metro to the price of your flat?
What about several kindergartens around your apartment? Are you ready to plunge in the world of geo-spatial data?
This post is a small abstract of full-scaled research focused on keyword recognition. Technique of semantics extraction was initially applied in field of social media research of depressive patterns. Here I focus on NLP and math aspects without psychological interpretation. It is clear that analysis of single word frequencies is not enough. Multiple random mixing of collection does not affect the relative frequency but destroys information totally — bag of words effect. We need more accurate approach for the mining of semantics attractors.
Have you ever looked for a flat? Would you like to add some machine learning and make a process more interesting?
Manual lung segmentation takes about 10 minutes and it requires a certain skill to get the same high-quality result as with automatic segmentation. Automatic segmentation takes about 15 seconds.
I assumed that without a neural network it would be possible to get an accuracy of no more than 70%. I also assumed, that morphological operations are only the preparation of an image for more complex algorithms. But as a result of processing of those, although few, 40 samples of tomographic data on hand, the algorithm segmented the lungs without errors. Moreover, after testing in the first five cases, the algorithm didn’t change significantly and correctly worked on the other 35 studies without changing the settings.
Also, neural networks have a disadvantage — for their training we need hundreds of training samples of lungs, which need to be marked up manually.
I came up with idea, that it would be cool to edit cell tags with help of IPython magic instead of mouse clicking and interacting with tags or metadata toolbars. So, now I can do it by typing this code directly into the cell input area:
%tags foo bar baz