Kamogawa Shokudou[é´¨ĺˇéŁĺ ]: Kamogawa Dininghall

Origami Around
trying on a metaphor
Sade Olutola
Alisa U Zemlji Chuda
Cosmic Funnies

â

⣠Chile in a Photography âŁ
sheepfilms
Cosimo Galluzzi
Show & Tell
DEAR READER
Claire Keane

Love Begins

pixel skylines

â
Lint Roller? I Barely Know Her

"I'm Dorothy Gale from Kansas"
todays bird

seen from Germany

seen from United States
seen from United Kingdom
seen from United Kingdom
seen from Italy

seen from Germany
seen from United States
seen from United States
seen from TĂźrkiye
seen from United States

seen from Malaysia
seen from Switzerland
seen from Singapore
seen from TĂźrkiye

seen from Germany
seen from United Kingdom

seen from United Kingdom
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seen from Malaysia

seen from Japan
@datagirlz
Kamogawa Shokudou[é´¨ĺˇéŁĺ ]: Kamogawa Dininghall

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Studying can be a daunting task, especially when we're not feeling motivated or don't know where to start. Luckily you are on Tumblr, where the Tumblr Studyblr community lives!
A group of individuals who share their study tips, techniques, and challenges to help motivate and inspire others.
As a member of this community, I've compiled a master post of study challenges created by Studyblr bloggers. These challenges aim to help students stay on track, improve their focus, and achieve their academic goals. So you can join in and start achieving your academic potential!
>> đ đ¨ đ đ
If you know any other challenges or you've created ones yourself and want to share them, do message me with the link to the post so I can update the list! I too will be creating some, more coding-related ones as I am a coding studyblr (codeblr) blog! That's all and hope you find a challenge you'd like to start!
@tranquilstudy's Studyblr Challenge - đ đ đ đÂ
@sub-at-omic-studies' Study Challenge - đ đ đ đÂ
@wecandoitâs Study Challenege - đ đ đ đÂ
@cheereader's The âBack To Collegeâ Study Challenge - đ đ đ đÂ
@myhoneststudyblr's The Studyblr Community Challenge - đ đ đ đÂ
@ddaengstudies' Wabi-Sabi Studyblr Challenge - đ đ đ đ
@hayley-studies' 30-Day Study Challenge - đ đ đ đÂ
@ddaengstudies' Zoomester Studyblr Challenge - đ đ đ đ
@cheereader's Summer Studying Challenge: Southern Hemisphere Edition -Â đ đ đ đ
@cheereader's Horrortober Challenge - đ đ đ đÂ
@caramelcuppaccino's Autumn Studying Challenge - đ đ đ đÂ
@myhoneststudyblr's Winter Studying Challenge - đ đ đ đ
@ddaengstudies' Winter Wonderland Studyblr Challenge - đ đ đ đ Â
@cheereader's South Hemisphere Autumn Challenge: 2023 Edition - đ đ đ đ Â
@stu-dna's January Study Challenge -Â đ đ đ đ
@planningforpatience's February Study Love Challenge - đ đ đ đÂ
@littlestudyblrblogâs March Study Challenge - đ đ đ đÂ
@wilstudies's April Studyblr Challenge - đ đ đ đÂ
@smallstudyblrsunite's The June Challenge - đ đ đ đÂ
@stu-dnaâs October Study Challenge - đ đ đ đÂ
@alfalfaaaryaâs 21-Day Productivity Challenge - đ đ đ đÂ
@work-before-glory's G's Productivity Challenge - đ đ đ đ
@moltre-se-s' 30 Day Langblr Challenge - đ đ đ đ
@drunkbloodyqueenâs The language challenge - đ đ đ đ
@caramelcuppaccino's 20 Language Learning Challenge - đ đ đ đÂ
@prepolygotâs Langblr Reactivation Challenge - đ đ đ đÂ
@onigiriforears's Target Language Reading Challenge - đ đ đ đ
@prepolyglot's Langblr Reactivation Challenge - đ đ đ đÂ
@xiacodes' 5in5weeks Coding Challenge - đ đ đ đ Â
@xiacodes' FreeCodeCamp Study Challenge - đ đ đ đ Â
@friend-crow's Tarot Study Challenge - đ đ đ đ  Â
Shocked programmer tries to use an API designed by a Data Scientist
dream âhabit-formingâ game app for me, an ADHD
level-ups stop getting further and further apart after like, level 10
and you always get the same amount of gold for doing a repeating task, forever
with like. periodic extra rewards for hitting a streak of a certain length
collectibles
the entire game is just eggs & animal food
maybe cross-breeding for cooler eggs? MAYBE?? TRADING??? idk
unlocking cooler/more complicated eggs/animals by reaching levels
option to limit how much time you spend in the app so you donât get stuck doing egg-collectible things
NO CHEATING BY PAYING EXTRA MONEYS
iâd pay literal money for this app but donât let me cheat by paying money
basically I just need an app that rewards tasks for long enough it actually becomes a subconscious habit. which takes like. a year. literally a whole year
& goals that donât grow so far apart I stop caring about reaching them
I want this to happen! check the notes!!!
Top 10 Data Science Project Ideas For Beginners - 2021
If you are an aspiring data scientist, then it is mandatory to involve in live projects to hone up your skills. These projects will help you to brush up your knowledge on knowledge and skills and boost up your career path. Now, if you write about those live projects on your resume, then there is a very good chance that you land up with your dream job on data science. But to be a top-notch data science engineer, it is essential to work on various projects. For this, it is important to know the best project ideas which you can leverage further on your CV.
Start Working on Live Projects to Build your Data Science Career
To get a sound idea for data science projects, you should be more concerned about it rather than itâs implementation. Because of this, we have come up with the best ideas for you. Here we have enlisted the top 10 project ideas that can shape your future in the world of data science. But to begin such programs or live projects, you need to have a good understanding of Python and R languages.
1. Credit Card Fraud Detection Mechanism
This project requires knowledge of ML and R programming. This project mainly deals with various algorithms that you can get familiar with once you start doing your applied machine learning course. These algorithms mainly cover Logistic Regression, Artificial Neural Networks, Gradient Boosting Classifiers, etc. From the record of the Credit Card transactions, you can surely be able to differentiate between fraudulent and genuine data. After that, you can draw various models and use the performance curve to understand the behavior.
This project involves the Credit Card transaction datasets that give a pure blend of fraudulent as well as non-fraudulent transactions. It implements the machine learning algorithm using which you can easily detect the fraudulent transaction. Also, you will understand how to utilize the machine learning algorithm for classification.
2. Customer Segmentation :
It is another such intriguing data science project where you need to use your machine learning skills. This is basically an application of unsupervised learning where you need to use clustering to find out the targeted user base. Customers are segregated on the basis of various human traits such as age, gender, interests, and habit. Implementation of K-means clustering will help to visualize gender as well as different age distribution. Also, it helps to analyze annual income and spending ideas.
Here the companies deal with segregating various groups of people on the basis of the behavior. If you work on the project, you will understand K means clustering. It is one of the best methods to know the clustering of the unlabeled datasets. Through this platform, companies get a clear understanding of the customers and what are their basic requirements. In this project, you need to work with the data that correlates with the economic scenario, geographical boundaries, demographics, as well as behavioral aspects.
3. Movie Recommendation System :Â
This data science project can be rewarding since it uses R language to build a movie recommendation system with machine learning. The Recommendation system will help the user with suggestions and there will be a filtering process using which you can determine the preference of the user and the kind of thing they browse. Suppose there are two persons A and B and they both like C and D movies. This message will automatically get reflected. Also, this will engage the customers to a considerable extent.
It gives the user various suggestions on the basis of the browsing history and various preferences. There are basically two kinds of recommendation available-content based and collaborative recommendation. This project revolves around the collaborative filtering recommendation methodology. It tells you on the basis of the browsing history of various people.
4. Fake News :Â
It is very difficult to find out how an article might deceive you mostly for social media users. So, is it possible to build a prototype to find out the credibility of particular news? This is a major question but thanks to the data science professionals of some of the major universities to answer the problem. Â They begin with the major focus of the fake news of clickbait. In order to build a classifier, they extracted data from the news that is published on Opensource. It is used to preprocess articles for the content-based work with the help of national language processing. The team came up with a unique machine learning model to segregate news articles and build a web application to work as the front end.
The main objective is to set up a machine learning model that provides you with the correct news since there is much fake news available on social media. You can use TfidfVectorizer and Passive-Aggressive classifier to prepare a top-notch model. TF frequency tells the number of times a particular word is displayed in the document. Inverse Document Frequency tells you the significance of a word on the basis of which it is available on several contents. Therefore, it is important to know how it works.
A TfidfVectorizer helps in analyzing a gamut of documents.
After analyzing, it makes a TF-IDF matrix.
A passive-aggressive Classifier tells you whether the classification outcome is viable. However, it changes if the outcome swings in the opposite direction.
Now, you can build a machine learning model if you have such good project ideas.
5. Color Detection :
It might have happened that you donât remember the name of the color even after seeing a particular object. There is an ample number of colors that are totally based on the RGB color values but you can hardly remember any. Therefore, this data science project will deal with the building of an interactive app that will find the chosen color from the available options. In order to enable this, there should be a detailed level of data for all the available colors. This will help you to find out which color will work for the selected range of color values.
In this project, you will require Python. You will utilize this language in creating an application that will tell you the name of the color. For this, there is a data file that comes with color names and values. Then it will be utilized to evaluate the distance from each color and find out the shortest one. Colors are segregated into red, green, and blue. Now the PC will analyze the range of the colors varying from 0 to 255. There are a plethora of colors available and in the dataset, you need to align each color value with the corresponding names. It requires a dataset that comprises RGB values as per the names. Â
6. Driver Drowsiness Detection :
In order to perform training and test data, researchers have come up with a Drowsiness Test which uses the Real Life Drowsiness dataset in order to detect the multi-stage drowsiness. The objective is to find out the extreme and discernible cases related to drowsiness using data science Skill. However, it permits the system to find out the softer signals of drowsiness. After that, comes the feature extraction which needs developing a classification model.
Since overnight driving is really a difficult task and leads to varied problems, the driver gets drowsy and feels quite sleepy while driving. This project helps to detect the time when the driver gets lazy and falls asleep. It produces an alarming sound as soon as it detects it. It implements a unique deep learning model to determine whether the driver is awake or not. This comes with a parameter to find out how long we stay awake. If the score is raised above the threshold value, then the alarm rings up. Now, you can easily be able to get the related dataset and Source Code.
7. Gender and Age Detection :Â
This is basically a computer vision and machine learning project that implements convolutional neural networks or CNN. The main objective is to find out the gender and age of a person using a single image of the face. In this data science project, you can segregate gender as male or female. After that, you can classify the age on the basis of various ranges like 0-2, 4-6, 15-20, and many more. Because of different factors such as makeup, lighting, etc, it is very difficult to recognize gender and age forms a particular image. Due to this, the project implements a classification model instead of regression.
For the purpose of face detection, you will require a .pb file since this is a protobuf file. It is capable of holding the graph definition and the trained weights of the model. A .pb file is used to hold the protobuf in a binary format. However, the .pbtxt extension is used to hold this in the text format. In order to detect the gender, the .prototxt file is used to find out the network configuration. The .caffemodel file is used here to denote the internal states of various parameters.
8. Prediction Of The Forest Fire :Â
Both forests, as well as the wildfire, ignites a state of emergency and health disasters in modern times. These disasters can hamper the ecosystem and this can cause too much money. Also, a huge infrastructure is required to deal with such issues. Therefore, using the K-means clustering you can easily be able to detect the forest fire hotspots and the disastrous effect of this natureâs fury. With this, it can cause faster resource allocation and the quick response. The meteorological data can be used to determine the seasons during the forest fires that are more frequent. Also, you can determine the weather conditions and climatic change that can reduce them and bring sustainable weather.
9. Effect of Climate Change on Global Food Supply :
Climatic change seems to affect various parts of the world. As a result, people residing in those areas are also under the wrath of such climatic change. The project mainly deals with the impact the climatic change is having and its effect on the entire food production. Main motive of the project is to determine the adverse effect of the climate on the production of crops. The project ideas mainly revolve around the impact of temperature and the rainfall along with the diversified cause of carbon dioxide on the growth of the plants. This project mainly focuses on the various data visualization techniques and different data comparisons will be drawn to find out the yield in various regions.
10. Chatbot-Best After the Data Science Online Training :
This is one of the famous projects done by the most aspiring data science professionals. It plays an important role in the business. They are used to give better services with very little manpower. In this project, you will see the deep learning techniques to talk with customers and can implement those using Python. There are basically two types of chatbots available. One deals with the domain which is used to solve a particular issue and the other one is an open domain chatbot. The second one you can use to ask various types of questions. Due to this, it requires a lot of data to store.
â Upskill Yourself Through Online Data Science Courses and Become a Professional â
The projects discussed in this technical article covers all the major Data Science projects which you need to do if you are a budding data science professional. But before that, you need to have a good grasp on various programming languages like Python and R. If you do the data science online tutorials, then these projects will be a cakewalk for you. Remember, one thing these small steps will make the large blocks so that you can rule the world of data science.. So, go ahead and participate in these live projects to gain relevant experience and confidence.

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Are you not sure about the key data science concepts? Don't worry have a look at some of the key data concepts you should know. Follow for more tech and programming stuff @techbiason
Have a look at the top 5 types of analytics in data science. Follow for more tech and programming stuff @techbiason
10.10.2021
Yesterday Tina Huang (a person i admire) posted a new video on her YouTube channel about how to make your study routine work and how to actually be productive (the link https://www.youtube.com/watch?v=INymz5VwLmk).Â
She gave a list of things everyone should figure for themselves to understand what they need to change in their life or attitude to be productive and not feel like shit. Hereâs her formula:
1) Have a goal (and sub-goals)
2) What pushes your buttons? (as in what really makes you do the work?)
3) Adapting your mindset (actually adapting to âshowing upâ mindset. This item is not for figuring out - itâs the same for everybody BUT itâs very important and i love how Tina illusrates it *no spoilers)
4) Finding the right support system
5) Measure your progress (find the way to measure your progress)
Bellow is how i used this formula and what i plan to doÂ
Keep reading
"My Track of Success" Tracker
Hello! I made a quick and easy tracker in which, if it is not already self-explanatory, tracks your successes!
What do you mean by 'successes'? Well, any achievements e.g. study goals, art commission goals, completing programming projects - anything good happening in your life, you track it here!
The whole point is to focus on the positives of life, have a record of all your achievements in which any time you feel down in the dumps, you can look at this and motivate you more to continue your hard work. The addition of how you felt on that day will make you think back and think "Wow, I want to feel like that again!!"
How the sheet works are that you add the date, the actual success and how you felt when it happened!
I hope this helps other people as it helped me! I will show an example of my own tracker:
Here I did change the colours a bit hehe >..<
Anyways here is the link to the sheet: â My track of success â by @XiaCodes
âĄ~âĄ~âĄ
Well, thank you for reading and have a good day coding! đ

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Learning Python: Week 1
Happy New Year! Frohes Neues Jahr! The New Year is here and a lot of people want to learn coding as one of their New Years' Resolution, so I am here to give my very own Python self-taught curriculum (which I made a couple of months ago) but thought it would be helpful even now! Anyways, let me help you with your very first week learning Python!
Python Self-Taught Curriculum
đ - đ - đ
I made a self-taught curriculum for myself but I wanted to share it. It's very basic and pink instead of frog green sorry. It's obviously not all you need to learn to be a pro in Python but it's a start! It is what I will be using to guide me to learn the programming language.
What I have done was I looked at 5 python bootcamps and just merged the curriculums together!
Here it is: print("Python Self-Taught Curriculum") on Google Docs
Hope it helps some people! Have a nice day! đ¸
The Vibrant Bot
If I could create a bot that changes the world, I would make a color bot.
In a Harvard Mental Health study, researchers found that participants who experience depression see colorless vividly than those who do not. According to the journal, â The researchers also found a significant association between severity of depression (as measured by standard clinical instruments) and perception of contrasts,â (Harvard Health Publishing). It can also be said, that the use of colors can dramatically alter a viewerâs understanding of the piece they are viewing. Regardless; color matters and it impacts the way we experience the world. However, losing your sense of color is devastating.
A long time ago, I used to live for colors. It was the colors I saw that defined the feeling I experienced and the words that I could not describe. An outing with my close friends could be described as a warm red that hummed from the hot ramen we would enjoy, and the time I spent alone in my room on a cool summer day breathed in and out from the tranquil aquamarine wallpaper to the subtle rays of yellow that would bounce through my window ceil. By being aware of colors things become more vivid and bearable. But, after I experience depression, the only colors I could see were muted tones. What once was warm red is now just the dull red of the ramen broth and what I once embraced as tranquil blue, can be described as the blue-gray that defines my loneliness.
When colors lose feeling, such as warmth and calm, you are just left with a dull picture of the world. Despite this tale of woes, the feeling that color can give should not be limited to your eyesight. A color-blind person will never experience red the same way a person without does. Colors do not have to be seen. I think there is another way to view them and this is what my bot does.
My bot is called the Vibrant Bot, but it does not show colors- it describes them. Using a large collection of colors, quotes, excerpts, and poems my bot displays color descriptions that use adjectives and nouns to describe colors, descriptive words that invoke feelings, or a quote about color and then posts them to Twitter every morning. The objective is for the viewer is to imagine colorful emotions that match the description or appearance of colors being described. Even if a person loses their sense of seeing color, this bot will ensure that it will not change their experience with color.
To make this bot, it would require two resources and some tenacity. This bot uses a color collection database (aka. a color dictionary) that can be found online. It uses only the names of colors from the list of Crayola names, as well as a few hex code titles. An example of some names this data set generates is, âash grayâ and âbanana blue,â collected from a Kaggle Dataset (Tatman) and a GitHub color repository (codebrainz). I selected these resources because they provide colors as well as adjectives to describe them. This pairing of nouns and colors gives a vivid description of what the color would look like with only two words. Compared that to simply calling a color âyellowâ or âgrayâ especially since color is a spectrum and individual understanding of colors can vary widely.
Another element of this bot is its use of literature and quotes that are highly descriptive to paint an image in the viewerâs head. While some quotes may not use color in them, the language creates a self-designed color associated with the mood and objects being described. For example, âdragonflies circled me, the sun knifing off the brilliant blues and yellows of their bodies,â (âQuoteLyfeâ) and âAs romantic as a Monday Morning,â. Unlike the last dataset, the inputs of these quotes are collected across several websites and books. The results are then put into python anywhere manually. The goal of each quote is to invoke emotions that paint a vivid picture of the thing being described, such as a Monday Morning or a dragonfly. The more vivid the quote, poem, or excerpt the more likely the viewer is to experience the underlying colors that may not be expressly written out. It gives the viewer the chance to create their own color associate through the words rather than being told the color to see. For example, each person has their own understanding of the colors on a Monday morning for some, it may be a romantic gray-blue that falls with the rain, for others it may be a pure black and white spiral that can be worn like a cloak. Regardless, the colors are seen and that is what the bot is going for.
Every Morning, this bot will post one of the colors, quotes, poems, or excerpts entered into the database. This entire process is done through pythonanywhere.com. While it may take time to completely collect the input data, the results will change the way individuals, literally, see the world. In this case, showing is worse than telling, because a person struggling to see does not need to be reminded of that fact. Also, seeing color being depicted with words rather than through a digital image is novel and can spark a personâs passion for site seeing and random gazing. To show that color can be described and is not limited by eyesight may change the way depression, color blindness, and other ailments are understood.
Predicting the Stock Market
*This project was never completed. It was a group Project for my Data Visualizations class, but my teammate and I were not able to see eye to eye. This is the work I contributed to the project. *
For almost a century, economists have tried to theorize the stock market trend. They could not find any relevant, predictable pattern and concluded that the stock market follows a random walk pattern. The 2013 Nobel Prized Fama developed the most famous theory in the 1970s. This theory is called the efficient market hypothesis (EMH), which asserts that information is fully incorporated into the price. In other words, stocks do not display any relevant information that can be used for predictions. However, some investors have made a fortune from the stock market, contradicting Famaâs theory.
In this project, we would like to verify the validity of the EMH. Under such theory, financial ratios could not be used for prediction. Yet, many financial advisors recommend the use of some. In this project, we will use the most widely used financial ratio to see if they impact the market trend.
To answer our questions, we will use a data set collected from Kaggle. The data set contains 4,000 listed stocks with their financial ratios. In total, they have 200 financial ratios, which is all the financial ratios that are out there. The time span of the data ranges from 2014 to 2018. It can be accessed using the following link:
Try to predict stocks' future performance leveraging 200+ financial indicators
Dr. Morrison Data Visualization April 14th, 2021 Data Visualization Proposal For almost a century, economists have tried to theorize the sto
Can you beat the stock market? Completed by Yazid & Sarah
The Importance of Aesthetics in Data Visualization
What if you could make data immersive in a way that people could feel or empathize with the information presented?
Hyoyong Kim and Jin Wan Park, explain the answer to this question. By using aesthetics with data visualization, rather than the conventional method of displaying information, data can become immersive. In their piece, Topics on Aesthetic Data Visualization, they look at the artwork that utilizes data and classify it into three topics. Each topic explains how artists use viewpoints, interpretations, and senses to enhance data and the impact it has on the viewer.
To begin, Hyoyong Kim and Jin Wan Park first make it clear that there is a distinction between Conventional Data Visualization, as we know it today, and Aesthetic Data Visualization. Conventional Data Visualization has a single goal, to deliver clear and concise information to the viewer. The data visualization is only practical when the viewer can recognize patterns from the in the once overcrowded Data. Aesthetic Data visualization, on the other hand, cares less about the practicality of the data. The goal of adding aesthetics to data is to make room for artistic expression. By removing the practical parts, the artist can add a voice to the data that would be missing otherwise.
By using aesthetics the information delivered takes a back seat to its appearance. By combining art, philosophy, and iconography with conventional data, the field of Aesthetic Data Visualization emerges. Hyoyong Kim and Jin Wan Park explain that there are ways to describe this practice of information visualization. Some words they use are: Ambient - abstract depictions of data, Social - the data that uses social experiences, and Artistic - displaying data as a visual representation or interactive method, describing how the viewers explore parts of data that would not fit into the goal of data visualization. Kin and Wan Park also suggest that the use of these methods could allow viewers to see problems that conventional data may miss.
Aesthetic Data Visualization appeals to the viewer in ways that conventional data visualization can not. Hyoyong Kim and Jin Wan Park explore and classify their argument into three categories. These categories are titled: Viewpoint, Interpretation, and Enhancing Sense. Each topic, when added to conventional Data Visualization creates works that enhance the viewers' experience and understanding of numbers and quantities.
The viewpoint of the creator determines the perspective of the data. Aesthetic Data Visualizers can choose a viewpoint for their data but, when conventional visualizers do this, there is often a bias. With conventional data, the motive of the project is often determined by the usefulness to the viewer. However, for aesthetic data visualizers, the use of data is determined by only the goals and motives of the artist. The data selected acts as inspiration rather than the focus of the work. An example of this is in graphs of neighborhood crime. Often with conventional data, the viewer is limited to the neighborhood and the type of crime. The goal of these charts is to show a buyer the crime in an area the data visualized is limited to information that helps the viewer make an informed decision when buying. Aesthetic Data Visualizers have the flexibility to choose their goal. Yet, in the piece Out of Statistics: Beyond Legal, by Rebecca Ruige Xu and Sean Hongsheng Zhai, their work uses the same data as conventional visualizers, neighborhood crime statistics, but the perspective is different. Instead of the focus being on the residential areas it looks at the data across several states. Their visualization dramatizes the data and leaves an impact on the viewer about crime in each state, in a way that conventional data is not able to do.
Interpretation explains how a data source can become Art. Conventional data can capture information at a single moment in time, but its ability to capture information is limited by technology. Additionally, data cannot capture emotions or abstractions with visualizations. A way around this is to use art with data. By making this combination, aesthetics can take on deeper meanings and can draw on individuals' experiences and create expressions that simple data visuals can not capture. This can be seen in the project, "Mapping time", by Lev Manovich. This piece collects decades of Time Magazine covers and places them in chronological order. By using images rather than qualities or charts, the work documents public perceptions on race, and wars by the lack, or inclusion, of diversity on the covers. Aesthetics can also show multiple opinions in data and give a new appreciation of the art, and the information presented. The journal uses the example, A digitally generated paternal family tree of Mr. Park, by Jin Wan Park. The data was pulled from a traditional Korean family "Jokbo" (family book of ancestors). When Jin Wan Park visualized this information artistically, he found gaps that documented the lost lives of young family members and clusters of spaces that represent times of war when several family members pass away. This piece gives emotion to otherwise static data.
Seeing data as facts and figures is not the only way to understand it. Hyoyong Kim and Jin Wan Park propose that aesthetic visualization can enhance how the viewer understands the data through their senses. Typically, visualizations are limiting in their ability to pull in our intrigue and deliver information. Adding aesthetics to the visualization, allows the artist to draw out sensations and emotions by placing information in a way that makes it impactful. An example of this is in the piece, The News Knitter, by Ebru Kurbak. The work collects information from public news and places it onto knit shirts. Ebru Kurbak amplifies the data by making it interactive rather than a simple chart. This delivery method of choice is an object from popular culture, allowing the data to send a message that the public can understand and relate too. The sight of a T-shirt touches the sight and kinesthetic sense in a way that a chart or graph can not.
To conclude, Hyoyong Kim and Jin Wan Park suggest that Aesthetic visualization is more impactful than typical visualization methods. They claim that by using the viewpoint, interpretation, and sense of the artist, the visualization takes on dimensions that go farther than graphs and charts can explain. By adding aesthetics to data visualization, the viewers of data can understand the works in a way that goes beyond numbers. As the field of Data Science expands, data will have a much larger role to play in the shaping of society and the decisions made in it. Hopefully, the importance of aesthetics will be taken into considerations, as visualization is not just for one group but rather the public eye. It would be best for data to appeal to the publics' senses, perspectives, and understandings of the world around them, as it is not just one person whom the data impacts but rather several voices.

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ICU Admissions Rate
Research shows that ICUs can put people with chronic illnesses in worse conditions than when they entered. Though it may not be immediately apparent, some studies have shown that the ICU can have a negative effect on patients later. We want to see if this applies to the different conditions in this dataset.
We want to answer the question: Does gender impact ICUs admission rates and the likelihood of re-admittance for individuals with different Chronic Illnesses?