#datascience

[soleminisanction]

I got a bee in my bonnet and spent last night crunching these numbers to confirm a long-held suspicion of mine, and now I'm going to do something with them even if it's only interesting to me. So.

I went through and tallied up all of the fics AO3 currently (as of 3/27/24) has under the tags "Trans Tim Drake," "Nonbinary Tim Drake," "Genderfluid Tim Drake" and "Genderqueer Tim Drake," since I figured that cast a wide enough net without committing myself to reading every fic vaguely tagged Trans Character to figure out which character they were talking about.

I then did the same for Dick, Jason, Damian and Bruce and, after comparing those numbers against each other and against the total number of fics each character has under their general tag, followed up with Duke, Babs, Cass, Steph and Kate, and then Kon, Cassie, and Bart for good measure.

The results confirm the suspicions I was going into check and are really interesting, to me at least:

Despite having far fewer stories overall than Jason, Bruce or Dick, Tim has by far the most stories tagging him under the trans umbrella (653 out of 58,395) and is the only member of the Bats for whom at least one full percent of his stories fall under that category (1.12% to be exact.) He actually has more total trans stories than Jason and Damian combined (308 out of 71,120 and 255 out of 42,607, equaling 0.43% and 0.59%, respectively) and outstretches the 2nd place ranker, Dick, by over a hundred (who clocks in at 438 out of 79,057 -- 0.55%). Bruce amusingly has by far the most stories overall (90,305) but the fewest trans stories (185) for the lowest percentage among the boys (0.2%).

The only one who comes anywhere close to matching Tim percentage-wise is Bart, who has far fewer stories to his name but a ratio of 62 out of 5,717 for 1.08%. I was thinking maybe Young Justice might have a higher percentage than the Bats due to their strong queer fandom but that only really proved true for Bart, with both Cassie and Kon coming in at only 0.2% and 0.28% trans umbrella percentage respectively (actual count 6 out of 2,874 and 39 out of 13,746).

Cassie's numbers correspond with the fact that women just, do not get a lot of these stories, at all, even compared to the general lack of attention they're paid by fanfiction spheres in general. Steph and Kate both clocked in at falling 0.17% under the trans umbrella (29 out of 16,638 for Steph, 5 out of 2,897 for Kate); Cass got 0.13% (21 out of 15,769) and Babs only 0.07%, the lowest percentage out of anyone I calculated for (11 out of 15,785). Duke's showing was a respectable 0.55% (34 out of 6,166) which puts him about even with the rest of the boys.

All of which I just went through to confirm a gut instinct I've had for a while: even in light of the noticeable trend in fandom towards increased visibility for trans and other queer-gendered people over the last decade and a half or so, it's a notable Thing for the DC comics fandom to explore with Tim Drake in specific.

And that doesn't even take into account things like the over 200 "Tim Drake is Catlad | Stray" fics, which almost always have some element of queered gender or at least femme'd sexuality to them, far outstripping any of the other Robin boys' spins in that AU (those counts stand at, respectively: Damian - 11, Dick - 33, Jason - 79, Tim - 242). Or the 11 fics logged under the "Tim Drake is Batgirl" tag, a category that doesn't even exist for any of the other male Robins.

(What makes that last one extra hilarious to me that most people don't know one canonical version of Tim has been a member of the Batgirls.) Part of me wants to use that parenthetic detail as a segway to ramble about the various canon snippets I think probably contributed to this, from Tim being presented as "the pretty one" who most often gets the "looks like his mother" comments to the fact that he is the only male Robin who's ever cross-dressed for an undercover mission and even though it only happened once the Internet will never forget Caroline Hill.

But this post is long enough as it is and I don't really have a point beyond I think this is interesting and cool so I'm going to leave off here for now and put my numbers under a cut so people have the raw data to look at if they'd like to.

TL;DR - Based on the numbers, the internet believes Tim Drake is more likely to be trans than any other member of the Bat-family or Young Justice, and I think that has interesting implications about his character and fandom. It's neat.

Data Taken: 3/27/24

Tim Drake: 58,395 Trans Tim Drake: 513 Nonbinary Tim Drake: 46 Genderfluid Tim Drake: 89 Genderqueer Tim Drake: 5

Dick Grayson: 79,057 Trans Dick Grayson: 399 Nonbinary Dick Grayson: 15 Genderfluid Dick Grayson: 23 Genderqueer Dick Grayson: 1

Jason Todd: 71,120 Trans Jason Todd: 286 Nonbinary Jason Todd: 17 Genderqueer/Genderfluid Jason Todd: 5 (4 have both tags and are the only ones tagged Genderqueer Jason Todd)

Damian Wayne: 42,607 Trans Damian Wayne: 215  Nonbinary Damian Wayne: 37 Genderfluid Damian Wayne: 3 Genderqueer Damian Wayne: 0

Bruce Wayne: 90,305 Trans Bruce Wayne: 180 Nonbinary Bruce Wayne: 5 (2 also tagged Trans Bruce Wayne) Genderfluid Bruce Wayne: 1 Genderqueer Bruce Wayne: 1

-

Total Trans Umbrella Tim Drake: 653 Total Trans Umbrella Dick Grayson: 438 Total Trans Umbrella Jason Todd: 308 (313 if you count the GQ tag separately) Total Trans Umbrella Damian Wayne: 255 Total Trans Umbrella Bruce Wayne: 185 (187)

Percentage Trans Umbrella Tim Drake: 1.12% (1.11825) Percentage Trans Umbrella Dick Grayson: 0.55% (0.55403) Percentage Trans Umbrella Jason Todd: 0.43% (0.43307 or 0.44010) Percentage Trans Umbrella Damian Wayne: 0.59% (0.59849) Percentage Trans Umbrella Bruce Wayne: 0.2% (0.20466)

----

Duke Thomas: 6,166 Trans Duke Thomas: 20 Nonbinary Duke Thomas: 14 Genderfluid Duke Thomas: 0 Genderqueer Duke Thomas: 0

Barbara Gordon: 15,785 Trans Barbara Gordon: 11 Nonbinary Barbara Gordon: 0 Genderfluid Barbara Gordon: 0 Genderqueer Barbara Gordon: 0

Cassandra Cain: 15,769 Trans Cassandra Cain: 15 Nonbinary Cassandra Cain: 6 Genderfluid Cassandra Cain: 0 Genderqueer Cassandra Cain: 0

Stephanie Brown: 16,638 Trans Stephanie Brown: 27 Nonbinary Stephanie Brown: 2 Genderfluid Stephanie Brown: 0 Genderqueer Stephanie Brown: 0

Kate Kane (DCU): 2,897 Trans Kate Kane: 4 Nonbinary Kate Kane: 0 Genderfluid Kate Kane: 1 Genderqueer Kate Kane: 0

-

Total Trans Umbrella Duke Thomas: 34 Total Trans Umbrella Barbara Gordon: 11 Total Trans Umbrella Cassandra Cain: 21 Total Trans Umbrella Stephanie Brown: 29 Total Trans Umbrella Kate Kane: 5

Percentage Trans Umbrella Duke Thomas: 0.55% (0.55141) Percentage Trans Umbrella Barbara Gordon: 0.07% (0.06968) Percentage Trans Umbrella Cassandra Cain: 0.13% (0.13317) Percentage Trans Umbrella Stephanie Brown: 0.17% (0.17429) Percentage Trans Umbrella Kate Kane: 0.17% (0.17259)

----

Kon-El | Conner Kent: 13,746 Trans Kon-El | Conner Kent: 19 Nonbinary Kon-El | Conner Kent: 19 Genderfluid Kon-El | Conner Kent: 1 Genderqueer Kon-El | Conner Kent: 0

Bart Allen: 5,717 Trans Bart Allen: 40 Nonbinary Bart Allen: 20 Genderfluid Bart Allen: 1 Genderqueer Bart Allen: 1

Cassie Sandsmark: 2,874 Trans Cassie Sandsmark: 4 Nonbinary Cassie Sandsmark: 2 Genderfluid Cassie Sandsmark: 0 Genderqueer Cassie Sandsmark: 0

-

Total Trans Umbrella Kon-El: 39 Total Trans Umbrella Bart Allen: 62 Total Trans Umbrella Cassie Sandsmark: 6

Percentage Trans Umbrella Kon-El: 0.28% (0.28371)  Percentage Trans Umbrella Bart Allen: 1.08% (1.08448) Percentage Trans Umbrella Cassie Sandsmark: 0.2% (0.20876)

[turns-out-its-adhd]

AI exists and there's nothing any of us can do to change that.

If you have concerns about how AI is being/will be used the solution is not to abstain - it's to get involved.

Learn about it, practice utilising AI tools, understand it. Ignorance will not protect you, and putting your fingers in your ears going 'lalalala AI doesn't exist I don't acknowledge it' won't stop it from affecting your life.

The more the general population fears and misunderstands this technology, the less equipped they will be to resist its influence.

[herpersonafire]

Hey everyone! enjoying my (two) week break of uni, so I've been lazy and playing games. Today, working on Python, I'm just doing repetition of learning the basics; Variables, Data types, Logic statements, etc. Hope everyone has a good week!

[altin-studies]

Econ notes on notion~

10-02-2024, Saturday

It's been 41 days of 2024, and it is already hectic beyond measure. I started reading extensively with a 52-book challenge. So far it has been going swimmingly. My studies, however, not so good. I am far behind everything, procrastinating and it is not leaving a good impression on my grades.

And so, FEBRUARY is the month of penance. I have started learning python and practicing SQL. I also am going to start on my academic progress soon enough. However, February barged into my life with a ton of events with mandatory participation. I don't know how I will cope with 18-hour weeks with 2 cultural events jammed in it. But, we will see. Wish me luck.

tag me --> #altinstudies

[mapsontheweb]

Italy population density.

by @milos_agathon

[d0nutzgg]

Predicting Alzheimer's With Machine Learning

Alzheimer's disease is a progressive neurodegenerative disorder that affects millions of people worldwide. Early diagnosis is crucial for managing the disease and potentially slowing its progression. My interest in this area is deeply personal. My great grandmother, Bonnie, passed away from Alzheimer's in 2000, and my grandmother, Jonette, who is Bonnie's daughter, is currently exhibiting symptoms of the disease. This personal connection has motivated me to apply my skills as a data scientist to contribute to the ongoing research in Alzheimer's disease.

Model Creation

The first step in creating the model was to identify relevant features that could potentially influence the onset of Alzheimer's disease. After careful consideration, I chose the following features: Mini-Mental State Examination (MMSE), Clinical Dementia Rating (CDR), Socioeconomic Status (SES), and Normalized Whole Brain Volume (nWBV).

MMSE: This is a commonly used test for cognitive function and mental status. Lower scores on the MMSE can indicate severe cognitive impairment, a common symptom of Alzheimer's.

CDR: This is a numeric scale used to quantify the severity of symptoms of dementia. A higher CDR score can indicate more severe dementia.

SES: Socioeconomic status has been found to influence health outcomes, including cognitive function and dementia.

nWBV: This represents the volume of the brain, adjusted for head size. A decrease in nWBV can be indicative of brain atrophy, a common symptom of Alzheimer's.

After selecting these features, I used a combination of Logistic Regression and Random Forest Classifier models in a Stacking Classifier to predict the onset of Alzheimer's disease. The model was trained on a dataset with these selected features and then tested on a separate dataset to evaluate its performance.

Model Performance

To validate the model's performance, I used a ROC curve plot (below), as well as a cross-validation accuracy scoring mechanism.

The ROC curve (Receiver Operating Characteristic curve) is a plot that illustrates the diagnostic ability of a model as its discrimination threshold is varied. It is great for visualizing the accuracy of binary classification models. The curve is created by plotting the true positive rate (TPR) against the false positive rate (FPR) at various threshold settings.

The area under the ROC curve, often referred to as the AUC (Area Under the Curve), provides a measure of the model's ability to distinguish between positive and negative classes. The AUC can be interpreted as the probability that the model will rank a randomly chosen positive instance higher than a randomly chosen negative one.

The AUC value ranges from 0 to 1. An AUC of 0.5 suggests no discrimination (i.e., the model has no ability to distinguish between positive and negative classes), 1 represents perfect discrimination (i.e., the model has perfect ability to distinguish between positive and negative classes), and 0 represents total misclassification.

The model's score of an AUC of 0.98 is excellent. It suggests that the model has a very high ability to distinguish between positive and negative classes.

The model also performed extremely well in another test, which showed the model has a final cross-validation score of 0.953. This high score indicates that the model was able to accurately predict the onset of Alzheimer's disease based on the selected features.

However, it's important to note that while this model can be a useful tool for predicting Alzheimer's disease, it should not be the sole basis for a diagnosis. Doctors should consider all aspects of diagnostic information when making a diagnosis.

Conclusion

The development and application of machine learning models like this one are revolutionizing the medical field. They offer the potential for early diagnosis of neurodegenerative diseases like Alzheimer's, which can significantly improve patient outcomes. However, these models are tools to assist healthcare professionals, not replace them. The human element in medicine, including a comprehensive understanding of the patient's health history and symptoms, remains crucial.

Despite the challenges, the potential of machine learning models in improving early diagnosis leaves me and my family hopeful. As we continue to advance in technology and research, we move closer to a world where diseases like Alzheimer's can be effectively managed, and hopefully, one day, cured.

[catndboots]

Things are finally getting serious: Today is the day I will start my journey learning Python from scratch! I really want to improve my coding skills and get more confident. By now, I still often rely on the idea of others, looking at their code and often simply adapt code snippets which fit the present issue to „my solution“ but then couldn‘t replicate a single line without looking at it. I am very excited to change that now, starting to get a deeper understanding and hopefully earn more confidence as a future data scientist!

[codingquill]

What is Cloud Computing ?

Cloud computing has become a widely discussed topic in recent years, but explaining it in simple terms to someone without a background in computer science can be challenging. Allow me to break it down for you.

Cloud computing is a method of storing and accessing data and programs over the internet, rather than keeping them on your personal computer or mobile device. To illustrate this, let's consider online email services like Gmail or Outlook. When you use these services, you can access your emails from anywhere because they are stored in the cloud. This means you don't need to install any special software or save your messages on your hard drive. Instead, your emails are stored on remote servers owned by companies like Google or Microsoft. You can access them from any device connected to the internet, regardless of your location.

Understanding Servers in the Cloud

Now, let's delve into the concept of servers in the cloud.

The data stored in the cloud is saved on physical servers, which are powerful computers capable of storing and processing vast amounts of information. These servers are typically housed in data centers, which are specialized facilities that accommodate thousands of servers and other equipment. Data centers require significant power, cooling, security, and connectivity to operate efficiently and reliably.

Microsoft and Google are two of the largest cloud providers globally, and they have data centers located in various regions and continents. Here are some examples of where their data centers are located, according to search results:

Microsoft has data centers in North America, South America, Europe, Asia, Africa, and Australia.

Google has data centers in North America, South America, Europe, and Asia.

[openprogrammer]

Follow for more & Save this post @openprogrammer @openprogrammer @openprogrammer #python #programming #coding #java #javascript #programmer #developer #html #snake #coder #code #computerscience #technology #css #machinelearning #pythonprogramming #linux #ballpython #php #datascience #reptile #snakes #reptiles #snakesofinstagram #software #reptilesofinstagram #webdevelopment #webdeveloper #tech #codinglife (at India) https://www.instagram.com/p/Ci0VXtgPFKN/?igshid=NGJjMDIxMWI=

[autismserenity]

Okay, I'm a data nerd. But this is so interesting?!

She starts out by looking at whether YouTube intense promotion of short-form content is harming long-form content, and ends up looking at how AI models amplify cultural biases.

In one of her examples, Amazon had to stop using AI recruitment software because it was filtering out women. They had to tell it to stop removing resumes with the word "women's" in them.

But even after they did that, the software was filtering out tons of women by doing things like selecting for more "aggressive" language like "executed on" instead of, idk, "helped."

Which is exactly how humans act. In my experience, when people are trying to unlearn bias, the first thing they do is go, "okay, so when I see that someone was president of the women's hockey team or something, I tend to dismiss them, but they could be good! I should try to look at them, instead of immediately dismissing candidates who are women! That makes sense, I can do that!"

And then they don't realize that they can also look at identical resumes, one with a "man's" name and one with a "woman's" name, and come away being more impressed by the "man's" resume.

So then they start having HR remove the names from all resumes. But they don't extrapolate from all this and think about whether the interviewer might also be biased. They don't think about how many different ways you can describe the same exact tasks at the same exact job, and how some of them sound way better.

They don't think about how, the more marginalized someone is, the less access they have to information about what language to use. And the more likely they are to have been "trained," by the way people treat them, to minimize their own skills and achievements.

They don't think about why certain words sound polished to them, and whether that's actually reflecting how good the person using that language will be at their job.

In this How To Cook That video, she talks about the fact that they're training that type of software on, say, ten years of hiring data, and that inherently means it's going to learn the biases reflected in that data... and that what AI models do is EXAGGERATE what they've learned.

Her example is that if you do a Google image search for "doctor," 90% of them will be men, even though in real life only 63% are men.

This is all fascinating to me because this is why representation matters. This is such an extreme, obvious example of why representation matters. OUR brains look at everything around us and learn who the world says is good at what.

We learn what a construction worker looks like, what a general practitioner doctor looks like, what a pediatrician looks like, what a teacher looks like.

We look at the people in our lives and in the media we consume and the ambient media we live through. And we learn what people who matter in our particular society look like.

We learn what a believable, trustworthy person looks like. The kind of person who can be the faux-generic-human talking to you about or illustrating a product.

Unless we also actively learn that other kinds of people matter equally, are equally trustworthy and believable... we don't.

And that affects EVERYTHING.

Also, this seems very easy to undo -- for AI, at least.

Like, instead of giving it a dataset of all the pictures of doctors humans have put out there, they could find people who actively prefer diverse, interesting groups of examples. And give the dataset to a bunch of them, first, to produce something for AI to learn from.

Harvard has a whole slew of really good tests for bias, although I would love to see more. (Note: they say things like "gay - straight, " but it's not testing how you feel about straight people. It's testing whether you have negative associations about gay people, and it uses straight people as a kind of baseline.)

There must be a way for image-recognition AI software to take these tests and reveal how biased a given model is, so it can be tweaked.

A lot of people would probably object that you're biasing the model intentionally if you do that. But we know the models are biased. We know we all have cultural biases. (I mean. Most people know that, I think.)

Anyway, this is already known in the field. There are plenty of studies about the biases in different AI programs, and the biases humans have.

That means we're already choosing to bias models intentionally. Both by knowingly giving them our biases, and by knowing they'll make our biases even bigger. And we already know this has a negative impact on people's lives.

[mlearningai]

The idea of designing with AI is both fascinating and inspiring.

A must-read!

#MLsoGOOD

[scrabble-scribbles]

Research Project

Hey guys! My friend reached out to me for some help with their research project. I'll paste their message to me below, if you have a chance, please go help them out! Its a google survey i think

Pasted from my friend:

Research Survey Hey dude! Im writing a research paper for my linguistics class on rich point words (words that have evolved different meanings within their own languages that vary from the "standard" definitions and may have their own dialects or sub-languages based on them). If you’re able, would you be willing to fill this form out? https://docs.google.com/forms/d/e/1FAIpQLSfdG1DUajrluz98IySIlpGeiB83JFKFrwNDQt5yLGi5hCDgnw/viewform?usp=sf_link Also, if you would be willing to share this survey around with friends/family members (if you’re comfortable doing so), that would be a huge help! Thank you!

Link pasted here as well for convinience:

thx for helping them out yall

[kochivamarketing]

Why Study Data Science?

Data science is an exciting and rewarding field that offers numerous compelling reasons to pursue it as a career path. Here are five key reasons why you should consider studying data science:

High Demand and Lucrative Salaries: With companies across industries recognizing the value of data-driven decision-making, the demand for skilled data scientists is skyrocketing, leading to abundant job opportunities and attractive compensation packages.

Work on Cutting-Edge Technologies: Data science lies at the intersection of emerging technologies like AI, machine learning, big data, and cloud computing, allowing you to work with cutting-edge tools and drive innovation.

Solve Complex Problems and Make an Impact: As a data scientist, you'll tackle real-world challenges across diverse domains, using data to develop solutions that can improve lives, drive social change, and shape the future.

Develop Highly Transferable Skills: Data science requires a unique combination of technical and analytical skills, such as programming, statistics, machine learning, and data visualization, which are highly transferable across industries and job roles.

Join a Vibrant Community: The data science community is diverse, dynamic, and collaborative, offering opportunities for continuous learning, networking, and professional growth through conferences, hackathons, and engagement with like-minded individuals.

With its high demand, potential for innovation, and the ability to make a meaningful impact, data science offers a rewarding and future-proof career path for those driven by intellectual curiosity and a passion for solving complex problems.

If you're in Delhi and looking to kickstart your data science journey, there are several excellent courses to consider, check out - Top 6 Best Data Science Courses in Delhi – The Complete Information

[anchaal]

Is Business Analytics Hard

Here’s the breakdown:

The Basics Aren’t Scary: Business analytics uses tools you might already be familiar with, like Excel and data visualization software. Plus, the core concepts involve analyzing data to answer business questions — something we do intuitively in everyday life.

The Challenge Lies in the Details: As you delve deeper, you’ll encounter statistics, programming languages like SQL or Python, and data wrangling (cleaning and organizing messy data). These skills require dedication, but there are plenty of beginner-friendly resources and courses available.

Experience is Your Best Teacher: The real challenge lies in applying your knowledge to solve real-world business problems. Understanding your specific industry and translating data insights into actionable solutions is where the magic happens.

So, is Business Analytics Hard?

Not inherently. It requires a blend of skills: some technical know-how, analytical thinking, and a good dose of business acumen. But with the right resources and a willingness to learn, anyone can develop these skills.

Here are some tips to make your business analytics journey smoother:

Start with the fundamentals: Learn basic data analysis techniques, get comfortable with data visualization tools, and brush up on your Excel skills.

Embrace online learning: There are countless online courses, tutorials, and even boot camps dedicated to teaching business analytics.

Find a mentor. Connect with experienced business analysts who can guide you and answer your questions.

Practice makes perfect: Look for opportunities to apply your learnings to real-world data sets, even if it’s a personal project.

The Takeaway:

Business analytics is a rewarding field with excellent career prospects. Don’t be intimidated by the initial learning curve. With dedication and a passion for data, you can unlock the power of business analytics and make a real impact in today’s data-driven world.

[emmaformuladata]

[06/03/2024 Day 3/100]

I forgot to post this morning, but better late than never✌🏼

Today my brain has been melted with maths. I feel I might have to just accept that this stuff I’m studying is horrid and it’s going to take much longer to understand than I originally thought 🙃

I managed to get some more reading done for another module which I didn’t plan to do today but I had to just change up what I was doing because I was losing motivation (and the will to live💀).

Also got back in to better habits of drinking my greens and drinking water before I have caffeine so, proud of me for that. Still keeping up with the Skin + Me routine too so hopefully I’ll start seeing a difference relatively soon.

But yea, all in all, relatively productive. I’m going to read some more of my book. Currently reading More Perfect by Temi Oh. It’s a dystopian fiction and so far I’m loving it, but it is a lengthy one so will update at *some point* in the future.

In terms of plans for tomorrow, I just want to continue being productive with regards to uni, I would also like to add in some movement tomorrow because that’s an area I have been severely lacking in recently.