#Data Science Coding

[arabellasdoingthework]

13/100 days of productivity

i am slowly getting my head above water, not only by getting things done but by realizing people don't secretly hate me (i know but be patient i only realized this yesterday)

academically speaking: python python python different types of regressions different models python python data tables APIs python python pyt*loses her mind*

[millenniallust4death]

Here’s the third exciting installment in my series about backing up one Tumblr post that absolutely no one asked for. The previous updates are linked here.

Previously on Tumblr API Hell

Some blogs returned 404 errors. After investigating with Allie's help, it turns out it’s not a sideblog issue — it’s a privacy setting. It pleases me that Tumblr's rickety API respects the word no.

Also, shoutout to the one line of code in my loop that always broke when someone reblogged without tags. Fixed it.

What I got working:

Tags added during reblogs of the post

Any added commentary (what the blog actually wrote)

Full post metadata so I can extract other information later (ie. outside the loop)

New questions I’m trying to answer:

While flailing around in the JSON trying to figure out which blog added which text (because obviously Tumblr’s rickety API doesn’t just tell you), I found that all the good stuff lives in a deeply nested structure called trail. It splits content into HTML chunks — but there’s no guarantee about order, and you have to reconstruct it yourself.

Here’s a stylized diagram of what trail looks like in the JSON list (which gets parsed as a data frame in R):

I started wondering:

Can I use the trail to reconstruct a version tree to see which path through the reblog chain was the most influential for the post?

This would let me ask:

Which version of the post are people reblogging?

Does added commentary increase the chance it gets reblogged again?

Are some blogs “amplifiers” — their version spreads more than others?

It’s worth thinking about these questions now — so I can make sure I’m collecting the right information from Tumblr’s rickety API before I run my R code on a 272K-note post.

Summary

Still backing up one post. Just me, 600+ lines of R code, and Tumblr’s API fighting it out at a Waffle House parking lot. The code’s nearly ready — I’m almost finished testing it on an 800-note post before trying it on my 272K-note Blaze post. Stay tuned… Zero fucks given?

If you give zero fucks about my rickety API series, you can block my data science tag, #a rare data science post, or #tumblr's rickety API. But if we're mutuals then you know how it works here - you get what you get. It's up to you to curate your online experience. XD

[scipunk]

Person of Interest (2011-2016)

[hi-im-nova]

Check up!

She isnt listening to a word he is saying

[moruteacademia]

Hi! I'm ally, this is my studyblr blog.

INTJ 5w4 584 mel-chol sx/so LVEF

Im currently studying Data Science, but besides coding I also enjoy cybersecurity, poetry, literature, universal history, philosophy, psychiatry, neurology, sociology, astronomy, feminism, music, typology, jung, enneagram languages and anarchism.

Gonna use this blog for rant, motivation, quotes, pretty pictures of my study sessions and pretty pictures and info that I found comforting here.

If I have any mistake on english let me know, because my mother language is spanish. So if u r spanish speaker let's be friends pls!!

I'm 18 years old. She-her from Lima, Peru. And + I also enjoy rock and alt music, goth and punk subculture, aesthetics, fashion and lolita fashion, writing, comics, anime and memes.

Girlblog @girlpresidentt and personal acc @allinson @dollpparts, blog that I don't use but my @ is really cool @cryptidacademia

Dividers made by @dollywons

[siph-by-induction]

wanted to learn some r so i started doing the cs50 r course. i was on a godawful school chromebook, and they've made it so you can't install linux, so i can't use vs code. rstudio cloud to the rescue, i sign up to rstudio cloud with my school email, write my first little program and save it in rstudio cloud.

i go back today. i want to carry on with my thing. i try to sign in to rstudio cloud.

"Your administrator has not approved access for this app."

WHAT THE FUCK DO YOU MEAN!!!!!! I WAS USING IT ON WEDNESDAY!!!!!!!!! LET ME SEE MY TINY PROGRAM PLEASEEE OH MY FUCKING GOD

[sage-thephdstudent]

I’ve just enrolled in an undergrad class at my university in data science / coding. It is the first time I’ve done any kind coding. It’s also been almost four years since I was in undergrad myself, so I’m pretty nervous for the exam.

Does anyone have any study tips, particularly for data science / coding, which they would be willing to share? This topic is so different from anything I’ve ever done before I’m feeling pretty out of my depth.

[a-fox-studies]

August 7, 2023

Uni 'A' has begun! Today we were introduced to data structures, and the teacher's accent was so heavy I zoned out too many times T-T

Planning on doing a pre-read before her class so it's more of a recall session rather than learning it new.

Things I did today:

Recalled basics of C programming on a sheet of paper

Light reading on introduction to data structures

Created a new template for my digital planner

@studaxy and I had a very productive 40 minutes :D

The days ahead look bright :)

🎧I Know Places - Taylor Swift

[webthreecorp]

Mastering Linked Lists: Beginner's Guide

Hey Tumblr friends 👋

After learning about Arrays, it's time to level up! Today we’re diving into Linked Lists — another fundamental building block of coding! 🧱���

So... What is a Linked List? 🤔

Imagine a treasure hunt 🗺️:

You find a clue ➡️ it points you to the next clue ➡️ and so on.

That's how a Linked List works!

🔗 Each element (Node) holds data and a pointer to the next Node.

It looks something like this: [data | next] -> [data | next] -> [data | next] -> NULL

Why Use a Linked List? 🌈

✅ Dynamic size (no need to pre-define size like arrays!) ✅ Easy insertions and deletions ✨ ✅ Great for building stacks, queues, and graphs later!

❌ Slower to access elements (you can't jump straight to an item like arrays).

Basic Structure of a Linked List Node 🛠️

data -> stores the actual value

next -> points to the next node

📚 CRUD Operations on Linked Lists

Let’s build simple CRUD functions for a singly linked list in C++! (🚀 CRUD = Create, Read, Update, Delete)

Create (Insert Nodes)

Read (Display the list)

Update (Change a Node’s Value)

Delete (Remove a Node)

🌟 Final Thoughts

🔗 Linked Lists may look tricky at first, but once you master them, you’ll be ready to understand more powerful structures like Stacks, Queues, and even Graphs! 🚀

🌱 Mini Challenge:

Build your own linked list of your favorite songs 🎶

Practice inserting, updating, and deleting songs!

If you loved this explainer, give a follow and let's keep leveling up together! 💬✨ Happy coding, coder fam! 💻🌈 For more resources and help join our discord server

[studentbyday]

so help me god i will brave the boredom of (re-)learning R syntax through codecademy with my forest timer 🙏🏻

plan for the last few days of 2023:

physio ✅

laundry ✅

read moral philosophy article ✅

set up vscode for local work ✅

learn git and save portfolio to my own repo ✅ (btw, does anyone know whether you get to have access to the me50 repo years after you've finished cs50? would be nice to get to keep my work...)

forest stopwatch w/ learn R course ✅ (there's still quite a ways to go 😪 but at least i'm learning...i think... i'll probably still have to look up a bunch of things in the docs whenever i finally do my first project 😅)

slowly read 1 journal article on said lab's research ✅

write cover letter for a lab i want to work in ✅

🏁 do the quicker, high-priority tasks as fast as you can to get 'em outta the way!

[nikjag]

plotting

plot random mundane stuff its fun

heres a plot of my mood rated 1-10 over time for the past half a year (rolling average with a window of 7 days)

always good to practice processing data and visualizing it

[sweetanyways]

(practicing dsa) is somebody gonna match my leet

[d0nutzgg]

Tonight I am hunting down venomous and nonvenomous snake pictures that are under the creative commons of specific breeds in order to create one of the most advanced, in depth datasets of different venomous and nonvenomous snakes as well as a test set that will include snakes from both sides of all species. I love snakes a lot and really, all reptiles. It is definitely tedious work, as I have to make sure each picture is cleared before I can use it (ethically), but I am making a lot of progress! I have species such as the King Cobra, Inland Taipan, and Eyelash Pit Viper among just a few! Wikimedia Commons has been a huge help!

I'm super excited.

Hope your nights are going good. I am still not feeling good but jamming + virtual snake hunting is keeping me busy!

[irisbellemoon]

one of my favourite things about art is that EVERY line or stroke means something. the artist may not have created it with any meaning, but there's still some meaning in it in context of the full work and/or story and the author's other works and life. this is what makes art so great.

And it doesn't exist in AI 'art', because it's all random. there's no meaning in it, no more than there's a meaning in a random string of numbers. it's just code.

[tech-insides]

What are the skills needed for a data scientist job?

It’s one of those careers that’s been getting a lot of buzz lately, and for good reason. But what exactly do you need to become a data scientist? Let’s break it down.

Technical Skills

First off, let's talk about the technical skills. These are the nuts and bolts of what you'll be doing every day.

Programming Skills: At the top of the list is programming. You’ll need to be proficient in languages like Python and R. These are the go-to tools for data manipulation, analysis, and visualization. If you’re comfortable writing scripts and solving problems with code, you’re on the right track.

Statistical Knowledge: Next up, you’ve got to have a solid grasp of statistics. This isn’t just about knowing the theory; it’s about applying statistical techniques to real-world data. You’ll need to understand concepts like regression, hypothesis testing, and probability.

Machine Learning: Machine learning is another biggie. You should know how to build and deploy machine learning models. This includes everything from simple linear regressions to complex neural networks. Familiarity with libraries like scikit-learn, TensorFlow, and PyTorch will be a huge plus.

Data Wrangling: Data isn’t always clean and tidy when you get it. Often, it’s messy and requires a lot of preprocessing. Skills in data wrangling, which means cleaning and organizing data, are essential. Tools like Pandas in Python can help a lot here.

Data Visualization: Being able to visualize data is key. It’s not enough to just analyze data; you need to present it in a way that makes sense to others. Tools like Matplotlib, Seaborn, and Tableau can help you create clear and compelling visuals.

Analytical Skills

Now, let’s talk about the analytical skills. These are just as important as the technical skills, if not more so.

Problem-Solving: At its core, data science is about solving problems. You need to be curious and have a knack for figuring out why something isn’t working and how to fix it. This means thinking critically and logically.

Domain Knowledge: Understanding the industry you’re working in is crucial. Whether it’s healthcare, finance, marketing, or any other field, knowing the specifics of the industry will help you make better decisions and provide more valuable insights.

Communication Skills: You might be working with complex data, but if you can’t explain your findings to others, it’s all for nothing. Being able to communicate clearly and effectively with both technical and non-technical stakeholders is a must.

Soft Skills

Don’t underestimate the importance of soft skills. These might not be as obvious, but they’re just as critical.

Collaboration: Data scientists often work in teams, so being able to collaborate with others is essential. This means being open to feedback, sharing your ideas, and working well with colleagues from different backgrounds.

Time Management: You’ll likely be juggling multiple projects at once, so good time management skills are crucial. Knowing how to prioritize tasks and manage your time effectively can make a big difference.

Adaptability: The field of data science is always evolving. New tools, techniques, and technologies are constantly emerging. Being adaptable and willing to learn new things is key to staying current and relevant in the field.

Conclusion

So, there you have it. Becoming a data scientist requires a mix of technical prowess, analytical thinking, and soft skills. It’s a challenging but incredibly rewarding career path. If you’re passionate about data and love solving problems, it might just be the perfect fit for you.

Good luck to all of you aspiring data scientists out there!

[algorithmquartz]

First Post!

Or at least, this is my first post on Tumblr since high school lol. I used to use Tumblr often a long time ago, but I left. Now I start fresh with a new account!

My name is AlgorithmQuartz. I'm 22, an INFJ, and a Capricorn Sun/Leo Moon/Libra Rising. I'm into gaming, singing, coding, witchcraft, tarot/oracle, astrology, and exploring esoteric ideas. I'm currently in school for computer science with a minor in data analytics. I'm also a practicing witch.

If any of that sounds interesting, feel free to give me a follow! I may also follow back. I've recently started making YouTube content as well, and I'm shortly going to post the couple videos I have so I can put them on the featured section of my blog. If you wanna check out my YouTube channel, then here it is!

Y e e t u s