#data preparation in data science

[savantlabs]

Data Preprocessing Magic: Unveiling the Hidden Power and Benefits of Preparing Your Data for Success

In the realm of data analytics, there exists a transformative stage that acts as the sorcerer's apprentice, wielding magic to unveil the hidden power and benefits within datasets: Data Preprocessing. This essential step in the data preparation journey is akin to crafting a spell, weaving together precision and finesse to ensure that raw data is transformed into a potent source of insights, setting the stage for success.

Data Preprocessing involves a series of enchantments applied to raw data to cleanse, structure, and enhance its quality. Much like a wizard meticulously prepares magical ingredients for a potion, data analysts engage in the art of preprocessing to eliminate imperfections and set the foundation for accurate analyses. The magic lies not only in the process but in the myriad benefits it bestows upon the analytics journey.

One of the primary enchantments of Data Preprocessing is the handling of missing values. Like restoring missing pieces to a puzzle, analysts strategically decide whether to impute, discard, or interpolate missing data, ensuring that the final dataset is comprehensive and reliable. This magic trick prevents the distortion of insights, allowing for a clearer and more accurate understanding of the underlying patterns.

Another mystical aspect of Data Preprocessing involves handling outliers. These anomalies, if left unchecked, can cast shadows over analyses, leading to skewed results. Through the magic of preprocessing, analysts can detect and either remove or transform outliers, creating a dataset that reflects the true nature of the phenomenon under scrutiny. The result is an analytical journey that is not misled by extraneous influences.

Normalization and standardization are additional enchantments within the realm of Data Preprocessing. These techniques ensure that variables are on a level playing field, eliminating biases introduced by differing scales or units. The magic here lies in the ability to compare and contrast variables seamlessly, allowing for the identification of true relationships and patterns within the data.

The benefits of Data Preprocessing extend beyond these individual enchantments. A well-preprocessed dataset sets the stage for more accurate predictions, improved model performance, and ultimately, informed decision-making. The magic of Data Preprocessing, when wielded skillfully, empowers analysts to unlock the latent potential within raw data, transforming it from a mere collection of numbers into a source of strategic insights and success in the magical realm of data analytics.

[hangingslothcentral]

idk what the ettiquette is for liking posts people have made about the show. I wanna encourage you to yell about it!! i make the shows for this very purpose!! please yell!! but i worry that my tendency to go through the notes like 'yes good' may be slightly off putting.

[questacademy]

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[ari-ana-bel-la]

Hi lovely, I absolutely love your stories. I was wondering if you could write one for Lewis, he has a daughter who is 16-17 and is absolutely smart, like Einstein smart and it's her first time in the Ferrari garage since Lewis moved and she saw a fault in some engineering work and helped fixing it and shocked her father and the whole garage. Thank you

The Future of Ferrari

Ferrari’s Maranello garage was a symphony of whirring drills, clanking tools, and intense Italian chatter. The team was hard at work preparing for the weekend’s qualifying session, red and black suits moving in well-practiced rhythm. Amid the organized chaos, one presence stood out—not because of noise, but because of the absolute silence and awe she left in her wake.

A girl with thick curls pulled into a loose bun and wide, observant brown eyes stood at the edge of the garage. She wore an oversized red hoodie with the Ferrari emblem on the chest, and a lanyard hung from her neck, swinging gently with her movements. Her expression was sharp, analyzing every corner of the room like she was mentally dissecting the internal combustion engine of the SF-24 just by looking at it.

“Daaaad,” she called out, trying not to sound impatient. “Where do you keep the drinks around here? I’m thirsty.”

Lewis turned around, helmet under his arm, his eyes immediately softening at the sight of his daughter. “Over there, near the data screens. Just don’t unplug anything or they’ll have a meltdown,” he teased, pointing her toward the crew’s refreshment corner.

She smirked. “Please, I could rewire this place blindfolded.”

He chuckled and shook his head. “That’s the confidence of a teenager with three physics awards.”

“Five,” she corrected, walking off.

As she moved across the garage, a few of the engineers took notice, recognizing her as Lewis’s daughter. Most had heard rumors of her intellect. She had attended MIT lectures for fun while vacationing in the States and was known for winning national-level science competitions in Europe. But seeing her in the flesh, in their sacred garage? That was new.

She sipped a bottle of water and leaned casually against a pillar, eyes drifting over the open rear of the car. Something wasn’t sitting right. She tilted her head, stepped forward a bit, and squinted at the gearbox housing.

A technician walked past her, carrying a tablet. “Excuse me,” she said, stepping closer to the car. “Is that the final mount design for the differential casing?”

The man blinked at her. “Uh… yes?”

She pointed to a specific joint just behind the casing. “That’s going to cause micro-vibrations under torque load. The fastener's alignment is 1.3 degrees off. It’s subtle, but enough to affect the car's handling mid-corner. Especially if it's hot.”

The tech frowned, unsure if he should laugh or worry.

“Sorry, who are you again?”

“Just his daughter,” she replied, nodding toward Lewis, who was now talking with his race engineer.

“Do you want to… maybe sit down?” he asked awkwardly.

But she stepped past him, crouched slightly, and gestured at a younger engineer who was watching curiously.

“Can I borrow your torque data? Just real quick.”

The engineer hesitated, then handed her the tablet.

She began typing, pulling up schematics, calculations appearing rapidly on the screen. Her thumbs moved like lightning, her brow furrowed in concentration. A few other engineers were gathering now, whispering among themselves.

“I recalculated the stress vector. See?” she turned the tablet toward them. “It looks fine in theory, but under compound load—especially with the way the aero package is set up—it’ll shift. You’ll get slight inconsistencies in traction, which is bad news during qualifying laps.”

The older technician who’d first questioned her stepped forward again. “Are you saying we need to rework this section?”

“I’m saying you need to adjust the mounting bracket by 1.3 degrees, shift the load path just slightly to the left, and reinforce it with carbon-composite washers. If you do that, you’ll stabilize the torque vector and improve rear-end consistency in Sector 3.”

There was a beat of stunned silence.

Then—

“Where did you learn that?” one of the senior mechanics asked, blinking.

She shrugged. “I read a paper about torque distribution in high-speed cornering last week. Got bored on the flight here.”

Someone stifled a laugh. Another said under his breath, “Bloody hell…”

“Oi!” Lewis called, finally noticing the growing crowd. “What’s going on?”

The head of engineering, a stern Italian named Matteo, stepped forward and gestured for Lewis to come over.

“Your daughter,” he began slowly, still sounding amazed, “just found a design flaw we didn’t catch. One that would’ve possibly cost you two-tenths per lap. Maybe more.”

Lewis stared. “Wait. What?”

Matteo pointed at her. “She’s… she’s like a walking CFD simulator. She even pulled up our own torque data.”

Lewis turned to her, his face a mixture of disbelief and fatherly pride. “Sweetheart, what did you do?”

She looked up innocently. “I fixed your car. You’re welcome.”

A round of laughter broke out, but it was warm, appreciative. The crew clapped her on the back, some shaking their heads in awe.

“She’s incredible,” Matteo said to Lewis. “You sure she’s not secretly part of Red Bull’s spy program?”

Lewis laughed. “Trust me, if she were, we’d all be in trouble. She’s probably smarter than half the grid already.”

“I’m smarter than you,” she teased.

“Absolutely no doubt about that,” he replied with a grin, ruffling her hair.

She smoothed it down with a roll of her eyes. “So dramatic.”

The engineers quickly got to work implementing her suggestions. Matteo kept glancing back at her like she was some kind of wizard. Lewis watched with arms folded, his heart swelling.

After a while, she stood beside him, watching the updated component go onto the car.

“So… what did you think?” he asked gently.

She tilted her head. “It’s loud. Smells like oil. Half the men here don’t know how to hold a tablet properly.”

Lewis laughed. “Welcome to Formula One.”

She smiled. “It’s cool, though. I like it.”

He nudged her shoulder. “You ever think about working in this world someday? Engineering, maybe?”

She glanced at him, then back at the car. “Maybe. If they can keep up.”

He chuckled again. “No pressure, but… you made me proud today.”

She looked at him seriously. “You’re always proud.”

“True. But today, I’m blown away. You just walked into one of the most elite garages on the planet and made a critical engineering correction before lunch.”

She gave a shy smile, shrugging. “Just saw something wrong and fixed it.”

He wrapped an arm around her shoulders. “You’ve always done that. In your own way.”

As the car roared to life for testing, the modified part holding firm, Lewis and his daughter stood side by side, two Hamiltons—one a living legend of the track, the other a rising genius who might just change the sport in her own quiet, brilliant way.

And somewhere behind them, Matteo whispered to a fellow engineer, “Keep an eye on her. She’s the future.”

♡♡♡♡♡♡♡♡♡♡♡♡♥︎♡♡♡♡♡♡♡♡♡♡♡♡

Authors Note: Hey loves. I hope you enjoyed reading this story. My requests are always open for you!

-♡○♡

[roanofarcc]

A MISJUDGMENT

pairing. tyler owens x fem!reader

summary. when kate drags you back to the home for a one-week stint to help out one of her old friends, you meet tyler owens. the uncouth cowboy and his reckless actions when dealing with something as dangerous as tornados almost instantly prick your nerves until you realize maybe there's more to the cowboy than meets the eye.

warnings. description of tornados, a curse word or two, slightly inaccurate meteorological info, reader is from the midwest.

word count. 2k || masterlist

a.n. did not expect my other fic to get so much love!! sending kisses to everyone who sent me such nice words <3 and I am having a ball with all of the wonderful requests I'm getting!!

The difference between the Oklahoma and New York was more jarring than you remembered. The wide-open skies and fields that stretched for miles were a distantly familiar sight as you stepped out of the truck. You had grown up in the Midwest, smack dab in the middle of tornado alley, which meant your youth was spent listening to your cautious mother warn you every tornado season of the dangers the storms posed so you’d always be prepared when worst came to worst. You’d hunkered down more time than you could count in your storm cellar, listening to doors rattling and the radio speak. Your father was less cautious; he enjoyed watching the storms roll in on the front porch as he listened to the distant hum of sirens. 

You’d never been a fan of storms, not like your father. They made you nervous; the unpredictably and devastating destruction wasn’t something you found fascinating enough to chase.

Moving to New York was a culture shock but you were lucky enough to score to a job working in tandem with someone who also grew up in tornado alley. You and Kate quickly became friends, bonding over your upbringing and knowledge of the weather. She had opened up to you about her storm-chasing days, all ending with the tragedy that took the lives of three people she loved. Her story only cemented your opinion of storm chasing; it was too risky. But she had suckered you in with your love for the science behind weather, and the next thing you know you were in Oklahoma with Kate and a friend of hers on a one-week mission.

You stuck back with the team in charge of reading the data the chasers collected. Your apprehension wasn’t thwarted by Kate’s reassurance, but you’d always known her to be smart and she knew those storms better than anyone. Your distaste for storm chasers was not because of those there for the science of it all, but rather those who did it for the thrill. 

Tyler Owens was exactly the kind of person you expected to drive into tornados with no regard for the danger. What he was doing, from what you gathered from Javi’s brief explanation, was for entertainment and the excitement of facing down peril, laughing in the face of it. 

You stretched in the nighttime air as Kate closed the truck door behind her and turned to you with the same unsure smile she’d been carrying around since you arrived in Oklahoma. You could tell her feelings were mixed about being back there, but you also saw the spark of enjoyment she was slowly relighting. 

“I’ll go check us in,” Kate said, gesturing to the front office of the motel before she took off. You leaned against the side of Javi’s truck, yawning and taking in the scene of more storm chasers lounging around the motel’s lot, enjoying each other’s company as you all waited for another storm to pop up amidst the outbreak. 

The sound of boots under gravel approached you, belonging to none other than Tyler Owens himself. “How ‘ya holding up, city girl?” he said. 

He introduced himself to you and Kate when you first arrived with Javi, meeting his team and the other groups of chasers who were all gunning after the same storm. She had told him the two of you were in from New York for the week, and he assumed that meant you both were born and raised there. Maybe you had lost your Midwest twang during your stay, but no matter how far you moved away, a piece of you would always remain there. 

“Just fine, thank you,” you replied. His team had set up not far from where you two stood; they all seemed busy working on their equipment, but their work was often cut by howls of laughter. They seemed to be enjoying themselves more than Javi’s team was. They’d all split up into separate rooms for the night, so they’d be ready to leave first thing in the morning. 

He rested his arm against the bed of the truck, making himself comfortable as he too looked out across the lot at the people. “I’ve always wanted to visit New York City,” he said, surprising you. That seemed like the last place someone like him wanted to go. “What’s it like?” 

You shrugged. “A lot different than this.” You looked upwards at the sky, seeing stars blinking back at you. The skies were never that dark in New York City, but the towering buildings made for a cool scene too. “I haven’t lived there too long, though. I’m still figuring it out.” You were still trying to gauge if you liked it more than home. You liked the hustle and bustle most of the time, but being back under starry skies and open plains, you had to admit you missed it a little. 

“Really?” he furrowed his brows. “Where’d you move from?” 

“Kansas.” 

He smiled in disbelief. “Well, I’ll be damned. City girl’s not actually a city girl after all.” 

“I’m full of surprises.” 

“I’m seein’ that.” Tyler was quiet for a moment before he asked, “Do you miss it?” 

You weren’t sure why he asked or why he seemed to care, but you answered regardless. “Sometimes. Not so much the storms though.” 

He laughed. “Yet, you’re out here storm chasing anyway?” 

“I’m just here to help my friend; their business is to help people. That kind of storm chasing I can get behind, I guess. Yours on the other hand…” You trailed off, and he scoffed in mock offense. 

“My kind of business is to face my fears.” 

It was your turn to scoff. “By putting yourself and your friends in danger for…what, exactly? Your internet audience? I know plenty of people like you from back home. You’re reckless and irresponsible.” You saw Kate waving you down by the stairs of the motel, flashing a set of room keys in the air. You said nothing more to Tyler, didn’t even give him a chance to defend himself, before you walked off and into your room for the night

You’d seen devastation before following a tornado, but it was still a harrowing sight. Homes flattened, family belongings flung miles away, and people left hurt in the ruins of their town. You, Kate, and all of Javi’s team arrived just as the storm subsided and the damage was fresh as wounds many of the townspeople bared. You wasted no time going around to help people; Kate did the same. 

An old woman sat in her front yard, carefully cradling windchimes in her arms. “Are you all right?” you asked, kneeling down in the wet grass in front of her. She looked up slightly startled but smiled kindly as she shook her head. “Oh, no. I’m just fine, dear, thank you.” 

“Here you go, Ms. Riley,” a familiar voice sounded from behind you. You turned your head just as Tyler appeared, holding a small box in one hand and a little kitten in the other. The woman, Ms. Riley, gasped and sat her windchimes back on the grass. She took the kitten, teary-eyed, as it purred. “There’s food there too. Make sure you eat, and if you need more my team’s got a table set up just down the road, all right?” 

“Thank you,” she said. 

Tyler said nothing to you as he began to walk away, but you followed him, not catching up with him until he was at a little table surrounded by his team. They had a stack of brown boxes they were handing out, filled with sandwiches one of the members was making quickly. They also handed out bottles of water to the line of people who had just been affected by the storm. 

One of his team members smiled at you, holding out a box of food. “You hungry?” they asked, but you shook your head. 

“No. These people need it, but thanks.” 

You weren’t sure for a moment that Tyler was going to say a word to you. You hadn’t left your last conversation on the nicest note, only to find him and his team working hard to help the ravaged neighborhood. 

But he turned toward you for a moment, looking a little conflicted. “At least take a water,” he said before looking at another member of his team. “Lily, can you take some boxes up the road? There’re  some people who can make it all the way down here.” She nodded, filling her arms with the boxes before she took off.

You were quiet for a moment, staring at Tyler as he and his team came up with a plan to help and feed as many people as they could before night fell. You felt a complicated set of feelings topple over you. And as Tyler started to walk away, you surged forward and grabbed his arm, forcing him to turn around. 

“What can I do to help?” 

Together, you and Tyler spent the rest of the afternoon helping members of the neighborhood find their lost belongings and connected anyone with injuries to the EMTs working overtime. It wasn’t until the sun started to set that you took a break, finding a blown-away lawn chair that was still usable to sit on. All day you had eaten your judgment and first impression of Tyler and his team. Maybe they all were reckless and a little irresponsible in their storm-chasing, but they were doing just as Kate was, helping people, just differently. He and his team apparently did that often and were some of the first responders to the damage the tornados they chased caused. You had overheard Lily tell Kate they used the money from their t-shirt sales to buy food for victims of the storm. 

“Hey,” Tyler greeted, approaching you with two boxes of food. “Here.” He handed onto to you before he found a seat and pulled it up beside you. 

You thanked him before the two of you ate in silence for a little while. Some of the debris had been picked up, but the wrecked houses haunted the street. You’d been lucky enough to never lose your home turning a storm, but you knew too many people who had. It was terrible. That was why you had gotten a metrology degree. You had witnessed the devastation storms brought and even though you were trapped behind a computer most days, your goal was to help improve warning systems for all kinds of disasters and ensure that people knew the best way to prepare for them, but it wasn’t foolproof. Sometimes all there was to do was help pick up the pieces in the wake. 

“I think I misjudged you,” you said, breaking the silence. 

“Yeah?” He smiled slightly, his face warmly illuminated by the ironically beautiful sunset. “Are you taking back the reckless and irresponsible comment?” 

“No.” You smiled too. “But maybe that’s not such a bad thing. You guys did a good thing here, helping these people.” 

Maybe there was more to him than you had originally believed. 

“It’s all a part of the job,” he said, a bit too casually for all of the work they actually did to help; one could say he was humble about it, which confused you even more. From the second he climbed out of his truck the first time you saw him, you were so sure you knew exactly the kind of guy he was. 

“You aren’t exactly how I expected you to do,” you said, honestly. 

He seemed to take that in stride, smirking at you bright enough to bring heat to your face. “Well, if you stick around, you might even get to like me.” 

You laughed. “Don’t push your luck, cowboy.” But you had a feeling he right be right. The week wasn’t over yet; you still had time to figure out exactly who Tyler Owens was. 

[zineobiology]

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The Zine O’Biology is a unique Star Trek Zine boldly going where no one has gone before!

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Also check out the Ao3 collection

[savantlabs]

Elevate Your Data Game: Unleashing Potential with AI-Powered Data Preparation Software

In the era of rapid digital transformation, organizations are turning to AI-powered data preparation software to elevate their data game and unlock unprecedented insights. Traditional data preparation methods often fall short in handling the complexities of today's vast and varied datasets. Enter AI-powered data preparation, a revolutionary approach that harnesses the capabilities of artificial intelligence to streamline and enhance the entire data preparation process.

One of the key advantages of AI-powered data preparation is its ability to automate mundane and time-consuming tasks. Machine learning algorithms embedded in these tools learn from patterns in data, automating tasks such as cleaning, structuring, and transforming data with remarkable precision. This not only accelerates the data preparation timeline but also significantly reduces the risk of human error, ensuring the integrity and accuracy of the prepared datasets.

These advanced tools are designed to adapt to the evolving nature of data. They can handle diverse data sources, whether structured or unstructured and navigate through the intricacies of real-world data scenarios. This adaptability is crucial in today's data landscape, where information is generated at an unprecedented pace and in various formats.

AI-powered data preparation software goes beyond automation—it leverages predictive analytics to suggest transformations, imputations, and enrichment strategies. By understanding the context and relationships within the data, these tools intelligently recommend the most effective steps for optimal data preparation. This not only empowers data professionals but also democratizes the data preparation process, enabling users with varying levels of technical expertise to contribute meaningfully to the organization's data goals.

Moreover, these tools foster collaboration between data teams and business stakeholders. The intuitive interfaces of AI-powered data preparation software facilitate seamless communication, allowing business users to actively participate in the data preparation process. This collaboration bridges the gap between raw data and actionable insights, ensuring that decision-makers have access to high-quality, prepared data for informed decision-making.

AI-powered data preparation software is a game-changer in the data analytics landscape. By automating, adapting, and intelligently guiding the data preparation process, these tools empower organizations to unleash the full potential of their data. As businesses strive to stay ahead in a data-driven world, embracing AI-powered data preparation is not just a choice—it's a strategic imperative to thrive in the realm of data analytics.

[jambalaya-enthusiast]

okay.....inhales...... UNIVERSITY AU!!!!!! Crew members as university professors x student! Reader who has a massive, explosive, crush on them!!!!

💃🏻💃🏻💃🏻

TEACHERS PET— Professor! Crew Members x Student! Reader who has a massive crush on them!

warnings: college! AU, student/teacher relationship dynamics, power dynamics, reader is 18. No NSFW in this one,jimmy being manipulative, the characters being a little inappropriate.

note: y'all want a part 2? I can make one where the reader proposes, and then one with relationship dynamics and how it would play out. angst maybe? Lemme know.

PROF. GRANT CURLY

He would be the professor of data science/and or aerospace engineering, with astrophysics.

Extremely well liked at the University, students and teachers alike look up to him due to his approachable demeanor and calm and respectable nature.

Very responsible as an authoritative figure, in his years of expertise, he's never had a single complaint ever come in from the board or from any student/guardian.

Which is why he basically fell into a dilemma when you one of his top students, started looking at him in a way that he was sure wasn't platonic.

Curly is very strict on keeping his personal and professional life apart.

He tried to convince himself, that it was just a puppy crush and it would soon go away.

But boy oh boy, nothing had prepared him for the day that you snuck into his office in the pretense of asking subject related questions.

He could practically feel your eyes being fixated on him, it didn't help how you were so devastatingly attractive either.

He doesn't wanna risk his career, and your educational prowess.

But God, He doesn't know how he's going to handle this predicament he's gotten himself into.

PROF. JIMMY ZARE

Oh boy. Oh dear.

He's a Psychology Professor fasho.

You've chosen the wrong older man to fall in love, this guy is literally the man LDR sings about.

Sure, he was probably the strictest and sternest professor on campus grounds, but he was also... The sexiest one, and he didn't even hide the fact that he didn't know how many young adults in the institution were practically drooling over him. His rough stud persona.

He never paid them any attention, sure he dropped in flirty smiles and winks every now and then to get the girls weak in their knees, but that all stopped when he laid his eyes on you.

you were this young, bright, cheery and oh so beautiful student, who happened to look at him in a way which he wasn't unfamiliar with.

He knew you were falling, hard.

Normally, he would of just ignored your desperate attempts at getting to know him, telling you off or extreme case just sleep with you to fulfill your desires of extra credits.

But no, you weren't looking at him entirely for lust, oh no no no.

He recognised that you were madly, deeply, in love.

And to him, you just seemed so tempting.

Like an angel whose wings he had to rip apart, a beautiful doll whose innocence he had to taint.

And he isn't quite sure if he's going to pass out on the opportunity.

PROF. ANYA MARINOVA

Psychology/Biology professor.

Extremely sweet, yet extremely stern, like curly, she prefers to keep her work and professional life separate.

She understands body language really well, so she was quick to catch onto your lingering eyes on her.

She acknowledges the fact that having romantic feelings for an authoritative figure is completely normal and part of a normal human psyche.

What she doesn't,is to act upon those feelings.

When she noticed you staying back during classes

She tries to play it coy, not giving you any attention.

But at one point, she begins to question even her morals.

Maybe it's not too bad? Time will tell.

PROF. SWANSEA HAROLD

Definitely the professor of Mechanical Engineering/Computer Engineering.

The typical no-nonsense straight to the point type professor.

Is extremely stern, hence is quite unpopular with the students.

It's a good day if he even acknowledged your existence, let alone engaging in a conversation.

He is quick to call out mistakes, hence if you wanna stand out you have to be a very good student who doesn't make any trouble or aren't inattentive.

He appreciates the students who actually take interest in the material rather than just mug everything up for passing.

But he isn't dumb, he's had his fair share of students crushing on him during his first years on the job.

He knows when a student starts paying too much attention to the guy teaching a boring ass physics formula.

He knows better than to indulge in their fantasies, to give them the delusional idea that something might be possible between the two of you.

But you caught his attention not just for your looks, okay maybe a little bit for your looks. but he'd also noticed how diligent you were as a student.

He's starting to question maybe it won't be too bad, it's been a while since he had some game. and you're both adults, so it should be fine right?

GUEST PROF. DAISUKE JUANEZ

Bro stormed through school and managed to bag the position as a guest lecturer at your college for a mechanical engineering course.

Since he's literally so unbelievably young, he has fangirls and fanboys left and right, and he knows it, he isn't dense.

But he promised himself he won't date any students since he thought they would only see him as a fantasy not someone to genuinely love.

But that was until you came into the picture.

He felt something the moment you locked eyes with him, he saw something in those eyes of yours, staring at him so dreamily.

He knows he's young, and he knows that you're as well, but he also knows that anything inappropriate between the two of you could result in him losing his job and risking even your future.

But he knows how to keep a secret, maybe you can as well?

[nasa]

Of course Saturn brought its ring light.

On June 25, 2023, our James Webb Space Telescope made its first near-infrared observations of Saturn. The planet itself appears extremely dark at this infrared wavelength, since methane gas absorbs almost all the sunlight falling on the atmosphere. The icy rings, however, stay relatively bright, leading to Saturn’s unusual appearance in this image.

This new image of Saturn clearly shows details within the planet’s ring system, several of the planet’s moons (Dione, Enceladus, and Tethys), and even Saturn’s atmosphere in surprising and unexpected detail.

These observations from Webb are just a hint at what this observatory will add to Saturn’s story in the coming years as the science team delves deep into the data to prepare peer-reviewed results.

Download the full-resolution image, both labeled and unlabeled, from the Space Telescope Science Institute.

Make sure to follow us on Tumblr for your regular dose of space!

[jcmarchi]

How Microsoft’s TorchGeo Streamlines Geospatial Data for Machine Learning Experts

New Post has been published on https://thedigitalinsider.com/how-microsofts-torchgeo-streamlines-geospatial-data-for-machine-learning-experts/

How Microsoft’s TorchGeo Streamlines Geospatial Data for Machine Learning Experts

In today’s data-driven world, geospatial information is essential for gaining insights into climate change, urban growth, disaster management, and global security. Despite its vast potential, working with geospatial data presents significant challenges due to its size, complexity, and lack of standardization. Machine learning can analyze these datasets yet preparing them for analysis can be time-consuming and cumbersome. This article examines how Microsoft’s TorchGeo facilitates the processing of geospatial data, enhancing accessibility for machine learning experts. We will discuss its key features and showcase real-world applications. By exploring how TorchGeo addresses these complexities, readers will gain insight into its potential for working with geospatial data.

The Growing Importance of Machine Learning for Geospatial Data Analysis

Geospatial data combines location-specific information with time, creating a complex network of data points. This complexity has made it challenging for researchers and data scientists to analyze and extract insights. One of the biggest hurdles is the sheer amount of data coming from sources like satellite imagery, GPS devices, and even social media. It’s not just the size, though — the data comes in different formats and requires a lot of preprocessing to make it usable. Factors such as differing resolutions, sensor types, and geographic diversity further complicate the analysis, often requiring specialized tools and significant preparation.

As the complexity and volume of geospatial data surpasses human processing capabilities, machine learning has become a valuable tool. It enables quicker and more insightful analysis, revealing patterns and trends that might otherwise be missed. But getting this data ready for machine learning is a complex task. It often means employing different software, converting incompatible file formats, and spending a lot of time cleaning up the data. This can slow down progress and make things more complicated for data scientists trying to benefit from the potential of geospatial analysis.

What is TorchGeo?

Addressing these challenges, Microsoft developed TorchGeo, a PyTorch extension designed to simplify geospatial data processing for machine learning experts.  TorchGeo offers pre-built datasets, data loaders, and preprocessing tools, allowing users to streamline the data preparation process. This way, machine learning practitioners can focus on model development rather than getting trapped by the complexities of geospatial data. The platform supports a wide range of datasets, including satellite imagery, land cover, and environmental data. Its seamless integration with PyTorch allows users to utilize features like GPU acceleration and custom model building, while keeping workflows straightforward.

Key Features of TorchGeo

Access to Diverse Geospatial Datasets

One of TorchGeo’s primary advantages is its built-in access to a wide range of geospatial datasets. The library comes pre-configured with several popular datasets, such as NASA’s MODIS data, Landsat satellite imagery, and datasets from the European Space Agency. Users can easily load and work with these datasets using TorchGeo’s API, removing the need for tedious downloading, formatting, and pre-processing. This access is particularly useful for researchers working in fields like climate science, agriculture, and urban planning. It accelerates the development process, allowing experts to focus on model training and experimentation rather than data wrangling.

Data Loaders and Transformers

Working with geospatial data often involves specific challenges, such as dealing with different coordinate reference systems or handling large raster images. TorchGeo addresses these issues by providing data loaders and transformers specifically designed for geospatial data.

For example, the library includes utilities for handling multi-resolution imagery, which is common in satellite data. It also provides transformations that allow users to crop, rescale, and augment geospatial data on-the-fly during model training. These tools help ensure that the data is in the correct format and shape for use in machine learning models, reducing the need for manual preprocessing.

Preprocessing and Augmentation

Data preprocessing and augmentation are crucial steps in any machine learning pipeline, and this is especially true for geospatial data. TorchGeo offers several built-in methods for preprocessing geospatial data, including normalization, clipping, and resampling. These tools help users clean and prepare their data before feeding it into a machine learning model.

PyTorch Integration

TorchGeo is built directly on PyTorch, allowing users to seamlessly integrate it into their existing workflows. This offers a key advantage, as machine learning experts can continue using familiar tools like PyTorch’s autograd for automatic differentiation and its wide range of pre-trained models.

By treating geospatial data as a core part of the PyTorch ecosystem, TorchGeo makes it easier to move from data loading to model building and training. With PyTorch’s features like GPU acceleration and distributed training, even large geospatial datasets can be handled efficiently, making the entire process smoother and more accessible.

Support for Custom Models

Many geospatial machine learning tasks necessitate the development of custom models designed for specific challenges, such as identifying agricultural patterns or detecting urban sprawl. In these cases, off-the-shelf models are inadequate for meeting the specific needs. TorchGeo provides the flexibility for machine learning experts to design and train custom models suited to geospatial tasks. Beyond data handling, it supports complex model architectures like convolutional neural networks (CNNs), recurrent neural networks (RNNs), and transformers, offering a robust foundation for addressing specialized problems.

Real-World Applications of TorchGeo

TorchGeo is already making a significant impact in various industries that rely heavily on geospatial data and machine learning. Here are a few examples:

Agriculture: Agricultural researchers are using TorchGeo to predict crop yields, monitor soil health, and identify patterns of water usage. By processing satellite images and weather data, models can be built to assess the health of crops, enabling early detection of issues like drought or disease. These insights can drive decisions about resource allocation and even government policy on food security.

Urban Planning: Urbanization is rapidly changing landscapes, and planners need accurate data to design sustainable cities. TorchGeo enables urban planners to analyze satellite imagery and geographic information to model urban growth patterns, optimize infrastructure, and forecast how cities might expand over time.

Environmental Monitoring: With the growing threat of climate change, environmental scientists rely on data from various geospatial sources, including satellite imagery and weather sensors, to monitor changes in forests, oceans, and the atmosphere. TorchGeo allows them to streamline the analysis of these datasets, providing actionable insights on deforestation rates, glacial melting, and greenhouse gas emissions. This can help both governments and private organizations make data-driven decisions about conservation efforts.

Disaster Management: In disaster-prone areas, machine learning models that utilize geospatial data are crucial for predicting natural disasters such as floods, hurricanes, and wildfires. TorchGeo simplifies the integration of datasets from various sources, like weather forecasts and historical satellite imagery, enabling the development of predictive models. These models enhance response times, optimize resource allocation, and ultimately have the potential to save lives.

The Bottom Line

As geospatial data continues to expand, tools like TorchGeo will become increasingly vital for helping machine learning experts extract insights from this information. By offering user-friendly access to standardized geospatial datasets, streamlining the data processing pipeline, and integrating seamlessly with PyTorch, TorchGeo eliminates many traditional barriers associated with working in this domain. This not only simplifies the task for experts addressing real-world challenges but also paves the way for new innovations in areas such as climate science, urban planning, and disaster response.

[envyi5envious]

RESEARCH.. JUST RESEARCH.

࿐ — 𝙋𝘼𝙄𝙍𝙄𝙉𝙂 : YANDERE (Red Robin) Tim Drake x GN Reader. 𝙎𝙔𝙉𝙊𝙋𝙎𝙄𝙎 : He was scribbling in a notebook, and you wondered what he was writing. 𝙒𝙊𝙍𝘿𝘾𝙊𝙐𝙉𝙏 : 1.7k. 𝙒𝘼𝙍𝙉𝙄𝙉𝙂𝙎 : Dark. Obsessive tendencies and stalking. 𝙉𝙊𝙏𝙀𝙎 : English isn’t my first language. I don't know why this took so long. Enjoy ♡

Class had just begun, and the familiar sound of shuffling papers and low murmurs filled the air. You had recently been transferred to AP Computer Science by your mother’s request. The teacher was discussing data analysis. They turned to the whiteboard, where they had written several bullet points. “First, we need to understand data collection.”

“This is where we gather information from various sources. It’s essential to choose reliable methods. Can anyone provide an example?” A young man raised his hand, mainly focused on the notebook on his desk.

“Yes, Drake.” The teacher replied as they leaned their backside against their desk. “We could use sensors or databases.”, “Correct. Well done.” After a few minutes, you tuned out the sound of their voice. Mainly focused on taking down the notes written on the board. Your ears perked up at the mention of an assignment. The teacher’s gaze swept across the room, lingering on a few students. “Next week, you’ll begin to work on a project analyzing a dataset of your choice. You will be required to pick your own partners this week so you have the weekend to prepare.”

The students responded with a few quiet hums and the teacher ended the class like that. The room was mainly silent besides the few people speaking to ask other students to be their partners. Assuming since you were new you wouldn’t get picked, you stood up to talk to one of your random classmates only to be met by a chest slamming into your nose.

“Shit-”

You heard a familiar voice say, their hands reaching out to secure you before you fell. “Are you alright?” They asked. Once your vision cleared, you realized why it was familiar. It was the same guy that answered the teacher. “Drake?” Your mutter came out before you could stop it, he let out a dry chuckle. “Tim, actually. Drake’s my family name.” He corrected. “Sorry about that. I was just coming to ask you if you wanted to be partners since I noticed you were new.” What a coincidence, you were about to do the same thing. “Oh, well I’m lucky then. We can meet at the Gotham library later, like 5PM-ish?” You weren’t sure if he’d be okay with giving his number off to a complete stranger.

He hummed for a second, thinking if he was busy around that time. Then he nodded his head as confirmation. “It’s a date. Talk to you later, (L/N).” He said before leaving the class, phone in his hands as he typed away like crazy. You could literally hear the sound of his thumbs touching the screen from that far away. Sighing, you sat back into your desk. You decide to try finishing your homework early today so you could focus on planning for the project. You even texted your mom not to pick you up since you would be meeting with Tim later. When you were done, you stood up to go for a walk to the cafeteria. Maybe you could get some coffee to stay awake. All AP classes were no joke, you were a little annoyed at your mom for forcing you to go to them so suddenly. While you were smart, you weren’t exactly a fan of school. You just did what you had to do to pass and that’s all. So when you found out you would have to be learning more because of your ‘potential’ you got rightfully pissed. It didn’t matter though. Once you were in AP, you can’t get out of it unless your parents signed for it (which your mother clearly isn’t budging on) or you flunk. And you weren’t about to fail Senior year just to get out of harder classes. Once you reached it, the room was mainly empty as most people went home. But the worker was still there until school closing time. There were groups still there, most likely waiting for their rides. You decided to order a croissant with ice coffee, making your way to an empty table to eat. You pulled out one of your notebooks to get to planning ideas.

—

The Sun had already set in Gotham due to the amount of buildings surrounding the city causing the car Tim was in to be fully dark, the only source of light was that of the laptop on his lap. The image broadcasted was that of the cafeteria’s cameras directed at you. You were writing notes with one hand and eating a pastry with the other. He couldn’t take his eyes off you. He had one of his notebooks beside him, taking notes when he noticed any quirks of yours. Like how you would subconsciously bite your nails or pick at your skin when you were stressed and the food you ordered. Then he took a look at what you were writing. At first he thought you were still working on ideas for the project. But as he kept reading, he realized that it seemed to be more of a fantasy novel. “Hm.. If I can just.. There we go.” He mutters to himself as he managed to zoom close enough to the book’s cover to see that it was a novel. ‘The Whispers of the Assassin.’ Quite the title. He searches the book online to have it delivered to the manor as soon as possible. “The Whispers of the Assassin follows Elara, a skilled assassin haunted by her past. Tasked with eliminating a crime lord responsible for her family's down.. Okay, I’ll read it later.” Tim thought to himself that he could suggest using this novel as a dataset, might help you be more interested to work with him on the project.

He’ll decide once he reads the book himself, for now, it’s best not to bring it up. When he realized the time was close to 5PM, Tim moved to the driver’s seat of his car to reach the library before you did. He would be a cover story that he was there the whole time.

—

When you finally reached the library, you found Tim scribbling notes in the same notebook he was using during class.When he heard your footsteps, he closed the book before you could get too close. Placing it back into his bag, he pulled out a tablet. “Hey.” He gave you a small smile. “Hey back.” You sat on the other side of the table, pulling out your own notes. “I wrote a few ideas on what we could use as a dataset and the methods. You can tell me which ones you find interesting.” You slid the papers to him, letting him read everything. “Hmm.. Good. The ideas, I mean. Here, we could use a novel. What novels do you like?”

“Well, I was reading a novel recently about a book called ‘The Whispers of the Assassin.’ It’s really good, you should read it. But I thought maybe we could use that.” Great minds think alike. You saw him typing away at his comically large tablet, he skimmed through the summary. He didn’t answer right away, almost like he was absorbed in the story.

But eventually he directed his face back to you. “Interesting. I’ll buy it later.” He tapped his index finger, eyes slightly unfocused. Before he stopped abruptly. “Since we’re basically done planning, there’s not much to do here.” He chuckles, turning to face his attention to one of the windows. “What do you like about the book?” His gaze wasn’t on you but he was still talking to you. “Well.. I like the main character, Elara. She’s a total badass. Her family died because of this mob boss and she goes after him to avenge her family. She honestly reminds me of Batman.” You could see him try to stop himself from cracking a smile from that. “Yeah, now I have to read it. I’ve had an obsession with Batman since I was a kid.” That explains the huge bat logo on his shirt. “Oh, so you’re a superhero nerd?” He nodded his head, smiling.

“Oh, shit. I completely forgot to tell you my name. It’s (Y/N).” You instinctively reached your hand out for him to shake and he surprisingly shook it as soon as you held it out. “That’s a pretty name.” He mused on it for a second before freeing your hand from his grip. “What else do you like to do?” The single sentence led to a conversation for a few hours before you left for your respective homes.

—

“Young master Tim, a delivery has arrived in your name.” Alfred’s voice could be heard through the door as he insisted on repeatedly knocking till Tim answered. “Thank you, Alfred.” He was about to close the door but the older man blocked the way with the tip of his foot. “I’m sorry to be a bother but Master Bruce has been concerned with your amount of screen time.”

Tim sighed slightly, he couldn’t help but be annoyed at the fact that they were taking time out of his busy schedule just to worry over nothing. “I can guarantee you both that I am fine. Just been busy with projects. AP classes are kind of kicking my ass right now. Thanks again.” He took the package from him without another word, pushing the man’s foot with his own. He quickly closed the door before he could be berated with even more of their concerns.

His room was clean but definitely not organized. Wires and computers were everywhere, books filled to the brim with the most minute of details about you. He made his way back to his bed, closing his laptop and pulling out his phone and earphones. He put the small buds in his ears, playing ‘8 HOURS OF BROWN NOISE’ as he began reading the novel. Four hours later, he had already finished it. Though, he had trained his mind to be able to handle large amounts of information in short periods. While the book most definitely had its flaws, it wasn’t bad. Now, just to finish the project so he can spend more time with you.

☆ 𝙢𝙖𝙨𝙩𝙚𝙧𝙡𝙞𝙨𝙩. ©◞✶ envyi5envious

[thewinastudyblog]

some advice i have for future computer science students

as soon as you learn data structures & complexity, run, don’t just walk, RUN to leetcode while the knowledge is still fresh in your mind. your entire career and whether you’ll get a well-paying job vs an average paying job depends on how good you are at leetcode.

build as many projects as you can, and i’m not talking tutorial projects that take a few hours, i’m talking big projects. working on a project for a month or two will get you really far.

if you don’t have an internship, do not waste your summers, learn new technologies, languages, concepts and build projects you can put in your cv.

try to participate in hackathons and coding competitions. it’s okay if you fail, but you’ll learn a lot.

learn how to read documentation. most tutorials don’t even cover a quarter of what a language, framework or software has to offer. the sooner you make reading documentation a habit, the better it is. and yes i know, documentation is long and hard to read. my advice is only read the sections that are relevant to you in the moment. something i also personally do is look at the code examples at the same time as i am reading the paragraphs, it really helps easily absorb the information.

try not to use chatgpt. and if you do, then at least use it for stuff you know you can do yourself and will be able to correct if the bot gets it wrong. using chatgpt is a very slippery slope and the more you use it the less you learn.

the math is important. math teaches you how to reason and how to develop better logical thinking. just because you don’t see yourself using the xyz theorem you’ve learnt anytime in the future doesn’t mean the math is useless.

be prepared to get comfortable with erros, issues, bugs and just problems in general. you’ll be coding 30% of the time and debugging 70% of the time (i’m exaggerating but sometimes it feels like this is the case lol), and that’s okay, it’s how we learn and the sooner you embrace it the better. if you’re someone who easily gets frustrated, then this is a heads up.

learn as you go. there is no such thing as waiting until you know everything before you start on a project. the only way and the best way to learn in this field is practice, so build, build, and build.

these are all the ones i could think of for now. feel free to comment your thoughts and questions <3

[lactoseintolerentswag]

Rise Characterizations Pt. 3!!!

Now that Leo and Raph are done, it's Donnie's turn for character analysis as a writing reference. So without further ado,

Donnie Character Notes

Language Habits:

Straight up talks like a redditor who hasn't touched enough grass (affectionate)

Oscillates between very scientific paper polished, sometimes adding a dazzle of shakespearean for dramatics, or abbreviations/a shorter version of a word with a more fun connotation (i.e. "brekkie" instead of breakfast)

Uses food as surprised exclamations or curses, "oh my peaches and cream", "banana pancakes!"

Emphasizes each syllable of a long word when he's excited or trying to make a point. Conquered becomes con-qu-ered

Either exaggerates his speech or speaks in deadpan

The science terms he uses as battle cries aren't chosen at random, but rather are related to the action/subject at hand, i.e. yelling "fibonacci" when throwing his spinning tech-bo

Will overly describe an item or a situation, and often gets caught up in these observations before processing what just happened

Will repeatedly yell "help!" when he's distressed and/or outnumbered

Refers to Mikey as "Michael"

Refers to his brothers as "brethren" or "gentlemen"

Refers to splinter as either "father", "papa", or "dad" depending on the weight of the situation

Refers to his tech as his "babies"

Answers the phone with, "You're conversing with Donatello"

Uses "gesundheit" instead of bless you

Personality:

The fixer, he supplies the family with tech and resources. He always has a trinket made for the situation at hand and/or offers his knowledge/data collected. He's always prepared to help. Even with outside resources, he likes to feel useful in solving their problems (i.e., building Todd that dog park)

The theater kid, in a similar vein to leo, Donnie has his own style of dramatics. He often uses shakespeare-like language, is mentioned to regularly recite the jupiter jim musical soundtrack, and has a music mode for his battle shell. He belongs on a stage, or at least thinks he does

Not good at lying, despite the glamour he can put on in the spotlight. This may be due to the side of himself that over explains his thoughts

An over-thinker, who really tends to over-complicate things. His first theory or idea will always be the most extreme buck-wild concept. After some filtering, he still word vomits

A dreamer/big idea guy. He does have big ideas and goals. A lot of these he's able to put into place, although some go a little haywire (see Albearto). He doesn't do things in halves, and puts everything into a project

Meticulous, someone who's very detail oriented. As mentioned before he tends to over-complicates things. This may be impacted by his love for data and collecting information (he does record Everything for a reason)

Always on the edge of violence, which is surprising. Donnie's not known as being the angry archetype of tmnt, but he can get a little violent in his fighting style and does often cite his desire to use lethal force

Low empathy, which is mainly due to his issues processing and recognizing emotions. He's been pegged as unemotional, but in canon he's rather emotional and expressionate, just lacking the skills to process such emotion (he's just like me fr)

Praise motivated, as seen with his interactions with Splinter. Also desires the praise of his brothers, who he doesn't feel understand him with all the teasing that's sent towards his direction. This also pushes him to seek validation and acceptance in other groups (i.e. the purple dragons), to feel a sense of security or belonging

Ignores his own mistakes, and will often pretend like they didn't exist or ever happen. This most likely has to do with his desire for praise, so he feels bad when he fails. If he never made a mistake, he never has to feel bad

Miscellaneous:

Fourth to unlock mystic powers

Uses "Bootyyyshaker9000" as most of his usernames and passwords, with his alt. username being "Alpha-Bootyyyshaker9000"

Has a fear of bees, spiders, and of course beach balls

Breaks the fourth wall the most

Loves the smell of pineapple, hates the texture

Has a hobby of rooting around in the junkyard and dumpster diving

Uses cheat codes in video games

Mikey's next of course :)

[chase-solidago]

NOW HIRING - Special Projects Assistant

Chicago Residency at time of job start required

Join the Rethinking Lawns Research Team (http://rethinkinglawns.com) and study the impacts of lawns and their potential alternatives in Chicago! Lead field experiments, manage experimental sites, and collect and manage data. Ideal candidate will have experience in project coordination and ecological field work.

Full description under the cut!

The Chicago Park District is seeking an experienced candidate to join our Natural Areas team, a part of the Park District’s Department of Cultural and Natural Resources. The Special Projects Assistant will be a member of the Rethinking Lawns project, a multi-disciplinary team exploring the ecological and ecosystem services benefits of traditional lawns, natural areas, and native, short-statured lawn replacement plantings. More information about the project can be found at rethinkinglawns.com. The ideal candidate will have experience in project coordination and ecological field work, preferably in a leadership capacity.

Key responsibilities include leading a team of seasonal research assistants in collecting data on plant cover, pollinator visitation, water infiltration, temperature, and soils at sites throughout Chicago and at the Chicago Botanic Garden. The Special Projects Assistant will participate in regular project coordination meetings with the research team, and oversee scheduling, coordination, communication, and data entry. They will also regularly interact with landscaping contractors, colleagues and the project team regarding installation and maintenance of plantings. Exceptional candidates will have some experience in data management, and analysis using R, and/or GIS. There are opportunities to present at local and national conferences. Local travel to field sites is required.

Desired Qualifications:

Bachelor's degree in Biology, Environmental Science, or a related field, or a combination of education and experience.

Knowledge of ecological principles and practices.

Ability to research information and prepare clear written or oral reports.

Ability to relate to field personnel and community groups, particularly in an urban setting.

Data entry, writing, computer skills.

Knowledge of contemporary research and communication practices. 

City of Chicago residency is required at time of job start.

This position is based out of North Park Village (5801 N. Pulaski Rd.) and includes frequent citywide travel.