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How to practice user empathy in UX design and make product more accessible

Level of difficultyEasy
Reading time6 min
Views132

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.

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Total votes 1: ↑1 and ↓0+3
Comments0

Top 10 Google Sheets Features to Enhance Your Productivity

Level of difficultyEasy
Reading time3 min
Views776

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!

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Total votes 3: ↑3 and ↓0+4
Comments1

Unveiling the Power of Matplotlib: A Visual Odyssey

Level of difficultyEasy
Reading time3 min
Views434

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.

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Total votes 2: ↑1 and ↓1+2
Comments0

Unveiling the Power of Data Science with Python

Level of difficultyEasy
Reading time3 min
Views376

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.

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Total votes 3: ↑3 and ↓0+3
Comments0

Master Data Analysis with ChatGPT — How to Analyze Anything (Beginners Guide)

Level of difficultyEasy
Reading time3 min
Views1.5K

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.

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Total votes 2: ↑2 and ↓0+2
Comments4

Rich text editors from backend perspective

Reading time7 min
Views4.5K
Welcome everyone, in this article I’m going to overview the most popular types of rich text editors, tradeoffs of their use from a backend perspective. By that I mean:

  • Streaming of content from the rich text editor to other infrastructure tools like full-text search, warehouses, etc.
  • Retrieving of content to clients: mobile, web, desktop.
  • Storing of content in some kind of storage (SQL database in my case)
  • Analyzing of content, which includes point 1, but also analyzing it from the perspective of our application
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Total votes 1: ↑0 and ↓1-1
Comments2

How to find an English teacher. Part 2

Reading time4 min
Views890
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This is a continuation of story about using Data Science for finding an English teacher. If you have not read it yet - there is an opportunity to become familiar with it

Briefly  -  we had information about language teachers and tried to apply some basic ideas using pandas and our expectations. Unfortunately we got stuck on the third step, because there is not enough information for resolving our the last requirements  -  we need not more 3 candidates at the end.

Disclaimer
It is an approach based on my own experience and can be unsuitable to your point of view, ideas, or principles.
Rating0
Comments0

How to find an English teacher. Part 1

Reading time5 min
Views1.6K


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?


Disclaimer

It is an approach based on my own experience and can be unsuitable to your point of view, ideas, or principles.

Total votes 2: ↑1 and ↓10
Comments0

Kibana Tips & Tricks: How to view events in Discover mode

Reading time3 min
Views6K
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Hi Habrausers!

As you may know Kibana is a visualization instrument, part of ELK (Elastic, Logstash, Kibana) stack. With the help of Kibana you may analyze and visualize your data, build different charts and combine them on the dashboard to present data in the most beautiful way.
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.
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Total votes 7: ↑7 and ↓0+7
Comments0

COVID YAAA! or Yet Another Analyze Attempt

Reading time11 min
Views1.2K

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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!

What did you do?
Total votes 1: ↑0 and ↓1-1
Comments0

Habr — best articles, authors and statistics 2019

Reading time6 min
Views2.8K
2019 is coming to an end, and it's Christmas soon. It is also the time to grab all data and collect statistics and a rating of the most interesting Habr's articles for this period.



In this post the best articles and best Habr authors 2019 will be presented, I also will show some statistical graphs that I find interesting or unusual.

Let's get started.
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Total votes 13: ↑12 and ↓1+21
Comments11

Machine Learning for your flat hunt. Part 2

Reading time9 min
Views1.6K


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?


The world provides so much information…



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Total votes 4: ↑4 and ↓0+4
Comments0

Keyword Tree: graph analysis for semantic extraction

Reading time3 min
Views1.7K

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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.

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Total votes 8: ↑7 and ↓1+6
Comments0

Automatic respiratory organ segmentation

Reading time8 min
Views2.1K

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.


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Total votes 11: ↑10 and ↓1+9
Comments1

Google News and Leo Tolstoy: visualizing Word2Vec word embeddings using t-SNE

Reading time7 min
Views13K

Everyone uniquely perceives texts, regardless of whether this person reads news on the Internet or world-known classic novels. This also applies to a variety of algorithms and machine learning techniques, which understand texts in a more mathematical way, namely, using high-dimensional vector space.

This article is devoted to visualizing high-dimensional Word2Vec word embeddings using t-SNE. The visualization can be useful to understand how Word2Vec works and how to interpret relations between vectors captured from your texts before using them in neural networks or other machine learning algorithms. As training data, we will use articles from Google News and classical literary works by Leo Tolstoy, the Russian writer who is regarded as one of the greatest authors of all time.

We go through the brief overview of t-SNE algorithm, then move to word embeddings calculation using Word2Vec, and finally, proceed to word vectors visualization with t-SNE in 2D and 3D space. We will write our scripts in Python using Jupyter Notebook.

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Total votes 28: ↑28 and ↓0+28
Comments0

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