#data wrangling tool

[helicalinsight]

Revolutionizing Data Wrangling with Ask On Data: The Future of AI-Driven Data Engineering

Data wrangling, the process of cleaning, transforming, and structuring raw data into a usable format, has always been a critical yet time-consuming task in data engineering. With the increasing complexity and volume of data, data wrangling tool have become indispensable in streamlining these processes. One tool that is revolutionizing the way data engineers approach this challenge is Ask On Data—an open-source, AI-powered, chat-based platform designed to simplify data wrangling for professionals across industries.

The Need for an Efficient Data Wrangling Tool

Data engineers often face a variety of challenges when working with large datasets. Raw data from different sources can be messy, incomplete, or inconsistent, requiring significant effort to clean and transform. Traditional data wrangling tools often involve complex coding and manual intervention, leading to long processing times and a higher risk of human error. With businesses relying more heavily on data-driven decisions, there's an increasing need for more efficient, automated, and user-friendly solutions.

Enter Ask On Data—a cutting-edge data wrangling tool that leverages the power of generative AI to make data cleaning, transformation, and integration seamless and faster than ever before. With Ask On Data, data engineers no longer need to manually write extensive code to prepare data for analysis. Instead, the platform uses AI-driven conversations to assist users in cleaning and transforming data, allowing for a more intuitive and efficient approach to data wrangling.

How Ask On Data Transforms Data Engineering

At its core, Ask On Data is designed to simplify the data wrangling process by using a chat-based interface, powered by advanced generative AI models. Here’s how the tool revolutionizes data engineering:

Intuitive Interface: Unlike traditional data wrangling tools that require specialized knowledge of coding languages like Python or SQL, Ask On Data allows users to interact with their data using natural language. Data engineers can ask questions, request data transformations, and specify the desired output, all through a simple chat interface. The AI understands these requests and performs the necessary actions, significantly reducing the learning curve for users.

Automated Data Cleaning: One of the most time-consuming aspects of data wrangling is identifying and fixing errors in raw data. Ask On Data leverages AI to automatically detect inconsistencies, missing values, and duplicates within datasets. The platform then offers suggestions or automatically applies the necessary transformations, drastically speeding up the data cleaning process.

Data Transformation: Ask On Data's AI is not just limited to data cleaning; it also assists in transforming and reshaping data according to the user's specifications. Whether it's aggregating data, pivoting tables, or merging multiple datasets, the tool can perform these tasks with a simple command. This not only saves time but also reduces the likelihood of errors that often arise during manual data manipulation.

Customizable Workflows: Every data project is different, and Ask On Data understands that. The platform allows users to define custom workflows, automating repetitive tasks, and ensuring consistency across different datasets. Data engineers can configure the tool to handle specific data requirements and transformations, making it an adaptable solution for a variety of data engineering challenges.

Seamless Collaboration: Ask On Data’s chat-based interface also fosters better collaboration between teams. Multiple users can interact with the tool simultaneously, sharing queries, suggestions, and results in real time. This collaborative approach enhances productivity and ensures that the team is always aligned in their data wrangling efforts.

Why Ask On Data is the Future of Data Engineering

The future of data engineering lies in automation and artificial intelligence, and Ask On Data is at the forefront of this revolution. By combining the power of generative AI with a user-friendly interface, it makes complex data wrangling tasks more accessible and efficient than ever before. As businesses continue to generate more data, the demand for tools like Ask On Data will only increase, enabling data engineers to spend less time wrangling data and more time analysing it.

Conclusion

Ask On Data is not just another data wrangling tool—it's a game-changer for data engineers. With its AI-powered features, natural language processing capabilities, and automation of repetitive tasks, Ask On Data is setting a new standard in data engineering. For organizations looking to harness the full potential of their data, Ask On Data is the key to unlocking faster, more accurate, and more efficient data wrangling processes.

[innovativeroutinesinternational]

Data Wrangling For Success: Streamlining Processes In Small And Large Enterprises

In the age of big data, organizations are continuously seeking ways to streamline their processes and seek knowledgeable insights from the vast amounts of data they collect. The need for efficiency and precision has never been higher, making data-wrangling tools an essential resource for businesses of all sizes. Data wrangling, or the process of cleaning and transforming raw data into a structured format, is the key to unlocking the potential of your data for business intelligence and analytics.

The Role of Data Wrangling in Today's Enterprises

Data wrangling plays a crucial role in helping businesses prepare their data for analysis. It involves gathering, filtering, and reformatting data from various sources so it can be effectively used by business intelligence (BI) tools. With the right data-wrangling software, enterprises can accelerate decision-making processes by providing clean and actionable data. Whether you are a small startup or a large multinational corporation, having organized data is the utmost step to gaining a competitive edge in your market.

Streamlining Small Enterprise Operations through Data Wrangling

Small businesses, with their limited resources, can benefit immensely from data-wrangling techniques. By using data wrangling tools, they can filter and aggregate data in a way that simplifies their operations. Instead of relying on manual processes, small enterprises can automate data preparation, ensuring that only the most relevant data is used for decision-making. This streamlined approach not only saves time but also helps small businesses stay agile in a competitive market, allowing them to respond to changes and opportunities more quickly.

How Large Enterprises Benefit from Advanced-Data Wrangling Techniques

For large enterprises, the volume of data collected can be overwhelming. Advanced data wrangling techniques allow large companies to manage big data more effectively. Using a platform like IRI Voracity, large enterprises can filter, scrub, and aggregate their data in a single job, reducing the time and effort required to prepare data for BI tools. This approach helps companies build data subsets that are easier to analyze, leading to faster insights and more informed business decisions.

Choosing the Right Provider for Data Wrangling

When selecting a provider for data wrangling, it's essential to choose one that offers robust and scalable solutions. Innovative Routines International (IRI), Inc. is a top provider in this space, known for its powerful data preparation tools like Voracity and CoSort. IRI's Voracity platform allows businesses to rapidly prepare big data for BI and analytics by filtering, transforming, and aggregating data in a single process. Built on the Eclipse framework and powered by CoSort or Hadoop engines, Voracity ensures that businesses can prepare data efficiently for multiple targets, whether it's for dashboards, scorecards, or scatter plots.

With the right provider, businesses can simplify the complexities of data wrangling and unlock the full potential of their data.

Conclusion

In today's data-driven world, businesses must embrace data wrangling to stay competitive. Whether you are a small enterprise looking to streamline operations or a large corporation managing vast amounts of data, the right data-wrangling tool can transform how you handle data. By choosing trusted providers like IRI, businesses can ensure their data is ready for insightful analysis, leading to better decision-making and overall success.

[oddlydescriptive]

Reset, Chapter 2

Series Masterlist

Full A/N below- please read previous A/N if you're just getting acquainted with the story! A bit of development for this slow burn, but I will be posting several chapters today that will bring us all the way up to things getting exciting!

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August 22, 2022- Findel, Luxembourg

The wheels hit the tarmac with a heavy thunk, the sudden shift in gravity making you instinctively press back into your seat as the plane slows down, rolling toward the gate. Your muscles are stiff, sore from the awful angles you contorted yourself into for the past twelve hours, but there’s no time to dwell on it. You barely hear the pilot’s announcement, barely register the sound of seatbelts clicking open around you, the shuffle of passengers stretching, retrieving bags, making groggy conversation.

You just breathe, long and steady, pressing your palm into your thigh to ground yourself.

It’s real now.

The last twelve hours have been a blur of data, race footage, and mind-numbing technical documents. You’d thrown yourself into studying, devouring every detail about Spa, about the AlphaTauri AT03, about anything that might give you a sliver of an advantage. At some point, exhaustion had forced you under, and you’d managed to sleep- not well, and not for long, but enough to keep yourself from completely burning out before you even landed. You don’t know if it’s enough, but it doesn’t matter. The only thing that does is the fact that you’re here.

You pull your duffel from the overhead compartment, the strap biting into your shoulder as you shuffle down the narrow aisle, down the jet bridge, through the airport corridors. The Luxembourg terminal is sleek, modern- glass walls, clean lines, an unbothered hush to the early-morning crowd. It’s almost enough to make you feel like this is just another trip, another airport, another connection to some middle-of-nowhere racetrack.

Almost.

You exhale slowly, shoulders still tight from the flight, standing just a little too upright at baggage claim as the conveyor belt lurches to life with a mechanical groan. Around you, the other passengers shuffle forward in loose, disjointed clusters- bleary-eyed and half-present, tugging their carry-ons behind them, faces lit by the glow of phone screens. You barely notice them. Your focus is locked on the mouth of the belt, waiting for the first bag to appear.

The seconds stretch, and you can feel the flicker of unease curling in your stomach, the kind of unease that only comes when you’ve placed your entire fate in the hands of an airline’s baggage system. It would be inconvenient- spectacularly inconvenient- if your gear didn’t make it. Not just your clothes or your toiletries, but your helmet, your gloves, your boots- everything. The tools you need to do the only thing that matters this weekend.

You can handle a lot- jet lag, exhaustion, even the gnawing anxiety clawing at the edges of your composure- but showing up to the most important race of your life with nothing? That’s not a setback you have time to recover from.

Then, finally- there.

Your race bag drops onto the belt with a dull thud, and it’s impossible to miss. It’s enormous, practically the size of a small coffin, its navy fabric scuffed and faded from being tossed in and out of transporters, cargo holds, and garages across America. You muscle it off the belt, the weight familiar, grounding.

You sling your duffel over your shoulder, grip the handle of your race bag, and start toward the exit. No hesitation, no adjusting straps or rolling out sore shoulders- not yet. Every second counts. Every person standing around re-packing their duty-free bags or stretching out the stiffness from the flight is another body you can get in front of in the customs line. You can adjust in line.

The weight of your bags pulls at your arms as you weave through the terminal, stepping around half-asleep travelers and families trying to wrangle children, past the slow-moving group of businessmen already back on their phones as if they never left the ground. The overhead announcements blur together, voices in multiple languages calling out baggage claim numbers, security reminders, gate changes. None of it matters. The only thing that matters is putting one foot in front of the other, getting through this final checkpoint between you and some fresh-fucking-air.

Customs.

You slip into line, shifting your duffel to your other shoulder, adjusting your grip on your race bag. It’s moving, at least- steady, slow, but moving. You take the opportunity to pull out your passport, flipping it open, rolling your shoulders back as you force yourself to breathe.

The line inches forward. A woman ahead of you fumbles with her boarding pass, patting down her coat for something lost in a pocket. A man argues softly with an officer over the contents of his declaration form. The customs agents work through their endless queue of travelers with the same disinterested efficiency you’d expect.

When it’s your turn, you step forward, placing your passport on the counter. The officer barely glances at you at first, flipping it open, running his eyes over the photo page before thumbing through for an empty page. He’s got plenty of options- there aren’t many stamps. A handful from trips to Mexico, a couple from the occasional race in Canada. But there- right near the middle of the booklet, pressed between the folds of your life before now- is Japan.

The ink is slightly faded, but the memory is sharp.

A feeder series race under Puerta Performance. One of the biggest, most competitive wins of your junior career. A stream of races where everything clicked, where you’d finally felt like you belonged in the conversation. You had flown in alone, carried your own damn bags, worked on your own damn car- elbow to elbow with the one real mechanic the team had, and then, somehow, you had won.

It had been your first real, international win. And it had done nothing for you.

The officer glances up, his face still unreadable. "Business or pleasure?"

"Business," you answer automatically.

He nods, flipping back to the front, glancing from your photo to your face, making sure they match.

"And how long will your visit be?"

You hesitate- because you don’t actually know. "A week," you say, because it’s less likely to have you corralled in a plexiglass room than saying as long as they’ll let me stay.

The officer hums, pressing the stamp to the page with a firm thunk, sliding your passport back toward you. "Welcome to the EU."

You don’t waste another second.

Snatching the passport off the counter, you tuck it away and haul your bags back into motion. You’ll check the taxi company on your way- just move. Get outside, get in the car, point your feet somewhere closer to the track and figure out the rest as you go.

Snatching the passport off the counter, you tuck it away and haul your bags back into motion. You’ll check the taxi company on your way- just move. Get outside, get in the car, point your feet somewhere closer to the track and figure out the rest as you go.

The wheels of your race bag clatter against the sleek tile floor as you push forward, dodging clusters of travelers, sidestepping a family stopped dead in the middle of the walkway, their kids wrestling over a stuffed animal. Someone’s wheeling a cart stacked with oversized luggage ahead of you, moving at a crawl, and you veer around them, your steps sharp, determined, relentless.

You're not rushed, not in the way that people sprinting to catch a flight are, but you're moving, too fast for someone who technically doesn't even have anywhere to be yet. But you do. The track. The garage. The sim. Work.

Your mind is running just as fast as your feet, the hum of the airport, the PA announcements, the scattered conversations in a dozen different languages all blurring together into static behind the sheer force of what comes next.

Four days.

Four days until FP1.

Four days to go from a long shot to something real.

Four days until you’re sitting in a Formula 1 car, in an actual race weekend, on one of the most legendary circuits in the world.

Your brain jumps tracks, recalibrating, running through everything you’ve learned, everything you still need to absorb. The AT03’s handling characteristics- where it struggles, where it thrives. The high-degradation nature of Spa’s tarmac. The elevation changes. The brutal forces through Eau Rouge and Raidillon. The moments in Yuki and Pierre’s footage where the car fought them, where the rear stepped out just enough to need a correction, where the chassis didn’t quite stick the way a Red Bull would- where it wouldn’t tolerate the lines of a more aggressive driver.

The air outside is going to be crisp, maybe damp, but you barely register the thought. You’re too busy calculating, adjusting, trying to fit yourself into the space you haven’t even stepped into yet. The exit is just ahead. You can see the doors, the hazy gray of the early morning sky beyond them, the promise of movement, of getting out.

Then- 

"Miss LeChriste?"

The voice cuts through the fog of your thoughts, smooth, precise. Not quite questioning, not quite commanding. It’s the tone of someone who already knows they have the right person. You blink, your mind needing an extra half-second to pull itself out of the high-speed loop it’s been running. You turn toward the sound. A man stands on the curb closest to the exit, holding a sign with your name on it.

Oh.

Your momentum stutters, feet slowing as your brain processes what you’re looking at. 

You’d expected a taxi. Maybe some impersonal email from a logistics coordinator telling you to grab a rental from the airport desk, something with a budget cap and a manual transmission. 

That’s what you’re used to- IndyCar, where teams cut costs at every possible turn, where travel arrangements were a patchwork of last-minute flights, hotel points, and the cheapest rental car they could justify expensing. Or, if you were really lucky, maybe one of the mechanics would swing by and pick you up in their own car, some beat-up old diesel with empty energy drink cans rattling around in the backseat, the heater stuck on max, a roll of duct tape on the dashboard because you never know.You’d piled into the passenger seat of sun-bleached hatchbacks, squeezed between spare parts and duffel bags, making small talk while rolling toward whatever motel your team had justified that weekend.

But this?

This man is wearing a suit. A pressed, properly fitted chauffeur’s suit, complete with a hat, standing in front of a sleek black car that definitely isn’t some bottom-tier economy rental.

"Uh, yeah. That’s me."

The driver nods once, crisp and efficient. "Right this way, Miss."

Miss.

You almost snort. Nobody calls you Miss anything. You barely get your name half the time.

You hesitate for the briefest second before stepping forward, gripping your race bag a little tighter. It’s ridiculous, but you feel out of place already, being ushered toward a private driver like you’re someone important.

There’s something about the way he says it that reminds you- this is Formula 1. This isn’t Indy, where you might be scrounging for a last-minute rental, squeezing into whatever compact car they gave you at the desk, hoping the hotel is decent enough to have a working coffee machine in the morning.

No.

This is Red Bull money. This is the first, quiet luxury of an operation that is so far beyond where you’ve been that you barely know how to process it. The kind of money where they send a driver- a chauffeur- to meet you at the airport before you’ve even turned a wheel for them.

The part that you’re really stuck on? This isn’t the top of Formula 1. This isn’t a private jet, a five-star concierge service, the kind of excess reserved for world champions. This is the bottom of the rung treatment. This is standard. This is what they do for anyone under their umbrella. This is expected.

The thought buzzes through you as you follow him toward the car, your feet moving before your brain has even finished catching up. The air outside is crisp, damp from last night’s rain, and the sky is the washed-out gray of early morning. The exhaustion is there, creeping at the edges of your mind, but it doesn’t matter. You’re still running on adrenaline, on the sheer force of need, but none of that really registers because- 

What the fuck is this?

This isn’t your world.

The driver reaches for your race bag, and for a moment, your immediate instinct is to pull it back, to haul it into the car yourself, because that’s what you’ve always done. You carry your own gear. You load your own luggage. You do it yourself, because no one else is going to do it for you.

But his hands are already on it, lifting it into the trunk with the ease of someone who expects to be doing this. Like it’s normal. Like it’s his job.

You exhale through your nose, shaking off the instinct to tell him you’ve got it. Instead, you climb into the backseat, sinking into the plush leather, the scent of clean upholstery hitting you as the door shuts with a quiet thunk.

Outside, the sky is gray, a thick European morning pressing against the glass as the driver pulls away from the curb, the urban sprawl of Luxemborg slipping into something quieter, something greener. You know, logically, that the scenery outside is incredible- lush countryside rolling into the Ardennes, sweeping hills, dense forests- but you don’t spare it a second glance. You don’t have the time for it.

You haven’t looked out the window once.

Instead, your mind is still on the flight, still running through every second of the last twelve hours, every bit of information you devoured somewhere over the Atlantic.

Spa.

You’d watched every inch of Spa.

Every braking point, every apex, every trick of the circuit that separated the competent from the champions. The Red Bull driver portal had given you access to all the film you could ask for- every onboard lap, every telemetry breakdown, every millisecond of data available. You’d watched the best of it, the ones who had conquered this place.

Max, Checo- their onboard film from this very track last year. The big boys. The cleanest, fastest lines that Spa had to offer. The best-case scenario. The way Max bullied his way through the wet, the way Sergio managed his tires on a track that could go from soaked to bone-dry in minutes. They were aggressive, clinical, perfect.

Yuki and Pierre’s onboards- this season, especially.  A different perspective. Your perspective. The same car you’d be driving. The AT03 wasn’t the RB18, not by a long shot. It lacked the raw dominance, the brutal efficiency, but it was the best AlphaTauri had managed in years. You studied how it moved, where it suffered, where it thrived. The way Pierre fought understeer through S-turns. The way Yuki handled the tricky mid-sector when the tires started to go. The places where they struggled, where you might struggle.

You absorbed it all.

You should be intimidated. You should be honored, overwhelmed by the fact that in just four days, you’ll be on the same track as the real legends, racing on one of the most historic circuits in the world.

But you don’t have time for intimidation.

You don’t have time to sit here and marvel at the fact that you’re about to put a Formula 1 car through Eau Rouge, that you’re about to barrel down the Kemmel Straight at 300 kilometers an hour.

You have four days. Four days to be good enough to make someone, anyone, just… notice. 

You shift in the backseat, adjusting your posture, rolling your shoulders back to shake out the stiffness. You’d finally shucked off your race suit after landing, stripping out of it in an airport bathroom, standing at the sink and taking a long, long look at yourself in the mirror before forcing yourself into something that wouldn’t get you laughed out of the boardroom when you arrived at the track. A fitted jacket, dark jeans, your best attempt at looking like you belonged.

The racesuit had been a reminder, a necessary weight of shame on the flight. But now? Now, you needed to look like someone they’d take seriously. There’s no room for shame, no room for weakness where you’re going.

You take a breath, steadying yourself as you glance down at your phone, skimming through the notes you made mid-flight.

Tire degradation. DRS zones. Elevation change data. Sector time comparisons.

The car isn’t even close to the track yet, and still, your brain is there.

The driver barely says a word, but you can feel his occasional glances in the rearview mirror, maybe wondering what exactly he’s transporting. Maybe wondering if the girl sitting stiffly in his backseat, scrolling through race data at seven in the morning, is actually human.

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August 22, 2022- Spa-Francorchamps Circuit, Belgium

The paddock is in pieces when you arrive, barely recognizable as the polished, high-functioning heart of a Grand Prix weekend. Temporary flooring is being laid down. Trucks are still reversing into position. Forklifts beep relentlessly as they maneuver crates full of equipment and spare parts into the skeletons of hospitality units. Crew members are swarming everywhere, setting up gantries, rigging screens, connecting endless tangles of cables that will power the broadcast feeds and telemetry systems by the time Friday rolls around.

You weave through it all, your race bag rattling behind you on uneven asphalt, escorted by an AlphaTauri staffer who barely introduces himself -Ignacio?- before setting off at a brisk pace. You don’t mind. The chaos feels oddly comforting- this kind of frantic, half-formed scene is something you know well. Setup days at Indy weren’t so different, at least in terms of sheer logistical madness.

What’s different is the scale.

Even in its unfinished state, this place radiates money. The equipment, the infrastructure, the sheer size of it all- everything is dialed up to a level you’ve never touched before. You pass Red Bull’s hospitality build, where scaffolding and tarps still cover half the façade, and for a split second, you think maybe that’s where you’re headed.

It’s not.

You’re led into the actual racetrack offices instead- concrete hallways and plain glass doors, a far cry from the polished luxury the public sees when the paddock is camera-ready. This is the backstage, the practical side of the circus, where decisions happen before anyone ever hears an engine fire up.

Your escort leaves you at the door of a conference room, gesturing for you to go in. You smooth your jacket, square your shoulders, and step inside.

They’re all waiting. You register them, of course, briefly as they all look up.. A set of suits that look like they may have slept even less than you in the last twenty-four hours, two bright eyed, pleasant looking professionals decked out in team kits. But they’re not who earn your attention first. It’s not Mattia Spini that gets it, either. It’s not even Franz Tost- to most, you’d be crazy not to defer to him first- he is the man that this entire opportunity rides on, after all.��

But that’s not the truth. Not entirely. Because the Godfather is here. 

Helmut Marko.

He’s not seated at the table with the others. Instead, he stands off to the side, leaning against the windowsill like he’s still trying to decide if this meeting is even worth the energy of taking a proper seat. His arms are crossed, head tilted slightly, expression settled somewhere between bored and mildly inconvenienced. He looks at you the way a banker looks at a loan applicant with no credit history- no malice, no warmth, just a quiet, clinical assessment of risk versus reward. It’s not dismissive, but it’s not encouraging, either. It’s the exact amount of respect you’ve earned from him so far, which is to say- none. Not yet.

It’s not a surprise. If anything, you’d expected worse.

Helmut Marko isn’t just some team advisor who drops in for the important meetings. He’s the architect of the entire Red Bull driver development program- the gatekeeper of every seat that exists within this brand. Every junior driver with a Red Bull patch on their chest lives under his thumb, or the thumb of someone who does. He decides who gets opportunities, who gets second chances, and who gets left to rot in feeder series obscurity.

And if you’re not his, if you didn’t come up through his system- if you weren’t plucked from karting at age 12 and molded in the image of what Helmut Marko believes a Red Bull driver should be- you’re already starting with a strike against you.

You’re twenty-two. By Helmut’s standards, that’s practically geriatric for a driver who still needs to prove themselves. Most of his prospects would have either succeeded or washed out entirely by your age. They would have either earned a seat, or been shuffled off to sports cars, endurance racing, somewhere that didn’t matter to him anymore.

But you’re here.

And that’s the part that matters.

Because Helmut Marko doesn’t suffer charity cases. He doesn’t tolerate time-wasters. The fact that you’re standing in this room at all means that, somewhere along the line, something about you caught his attention. Maybe it was your handful of substitute drives this season and last. Maybe it was something Christian Horner said. Maybe it was sheer desperation on AlphaTauri’s part to find anyone who could possibly hold the line in Yuki’s absence.

It doesn’t matter why.

All that matters is that Helmut Marko allowed this meeting to happen. He doesn’t have to like you. He doesn’t have to be impressed. He just has to leave the door open exactly this much. It’s your job to kick it the rest of the way in.

You move like you belong here. Like this is normal- being thrown into a meeting with a room full of people who hold your future in their hands. Like you weren’t on the other side of the world less than twenty-four hours ago, driving a shitbox for a team that treated you like nothing.

The first few minutes are pure formalities. Introductions, pleasantries, nods exchanged. You shake hands with everyone, making sure your grip is firm, your eye contact direct. You sit where they gesture, hands folded in front of you, posture perfect. Professional, measured. No jokes, no awkwardness, no nerves.

Franz Tost sits at the head of the table, his posture composed but his expression unreadable. Franz starts with the basics- introductions, a brief overview of what they’re hoping to achieve this weekend. You keep your tone perfectly professional, measured, micromanaging every aspect of yourself to project exactly what they need to see. Capable. Likable. Smart enough to understand the stakes. Hungry enough to take whatever they give you. You ask exactly the right questions at exactly the right moments- about the car, about expectations, about media requirements, about everything that will determine whether or not you make it to the weekend.

To his left is Mattia Spini, the man who will be your race engineer this weekend- if you earn the car. He’s quiet, thumbing through the small stack RedBull’s assembled that you can assume is all your career -your life’s work- mounts to, on paper.

The legal team- the two suits- sit with carefully neutral expressions. When they slide over a stack of documents that might as well be a brick, and you pick up the pen without hesitation, signing where they point, asking the occasional smart, concise question to show you’re paying attention.

Media relations is here too- the kitted-out pair you had noted before. You nod along to their every ask, perfectly agreeable. You’ll do every interview they want, every promo shot, every press availability. You don’t care. You’ll stand in front of cameras all day if that’s what it takes to earn the seat.

"I’m happy to do whatever the team needs."

It’s not a lie. It’s not even an exaggeration. You will do anything.

And then, it’s your turn. You pull your own packet from your bag- a meticulously prepared file containing every piece of critical data they could possibly need about you. The Holy Bible. This is your life’s work- not the measly six or seven pages they had scraped together and set in front of each seat before you arrived. Mattia takes the folder without much thought at first, flipping it open with the kind of casual disinterest of someone who has sat through way too many meetings just like this one. But the second his eyes land on the first page, the shift is almost imperceptible- almost.

You see it, though.

It’s in the way his fingers slow against the edge of the paper, in the way his posture changes just slightly. His gaze sharpens, scanning the structured layout, taking in the color-coded tabs along the side, the neatly labeled sections that break everything down into digestible, categorized data points.

His brow creases just slightly, his fingers smoothing over the paper as he scans the biometric data. Stress tests, reaction times, endurance tracking. He turns another page, and another. Height, weight, exact body measurements for suit fittings, seating position requirements. Flip. Car history, setup preferences, personal notes on what has worked for you and what hasn’t. Flip. On-track strengths, biggest flaws, areas you’ve personally identified as weaknesses and your own methods of mitigating them.

You keep your expression even, but you know exactly what’s happening here.

Mattia is a data guy. That’s how he got this job in the first place. Numbers, telemetry, analysis- it’s what he does. He’s used to drivers walking in with an opinion on how a car should feel, sure, but not with this. 

Because this? This is what he does. This is his job. Synthesize the data, break it down, make it digestible, work on it with the driver. Not the other way around. And that’s interesting.

Tost glances at him briefly, but Mattia doesn’t look up, doesn’t acknowledge the way the room has subtly shifted. He keeps flipping through, fingers moving slightly faster now, like he’s searching for something, like he needs to confirm that this is actually what he thinks it is.

“Did Dale Coyne’s engineers put this together for you?” Mattia’s voice is casual, but the surprise isn’t hidden. It bleeds through the edges, slipping into the slight lift of his brow, the way his fingers hesitate for half a second before flipping to the next page.

You almost laugh- almost. Because the idea of those half-competent, half-bored bastards at Dale Coyne assembling something this polished, this comprehensive? It’s ridiculous. Those men wouldn’t waste the paper to print you a fucking data readout, much less do you the courtesy of organizing your career data into something usable. And if they had? It wouldn’t look like this. It wouldn’t be color-coded within an inch of its life, wouldn’t have cross-references or a table of contents, wouldn’t read like a military dossier written by someone who knows exactly how much weight every ounce of detail could carry.

“No,” you say smoothly, keeping your face as neutral as your tone. “I keep all my data myself.”

There’s a reaction. A small one, but you catch it- Mattia’s head tips just slightly, the folder resting heavier in his hands now, no longer just a pile of papers but a point of interest. His fingers tighten against the edge, not out of irritation but out of concentration. It’s the look of a man who’s just found something unexpected in a sea of the predictable.

You know this moment. You know it.

Because your mother, Marissa LeChriste, made sure you could recognize this kind of moment before you could even spell leverage.

Marissa is a masterclass in influence- not the shallow kind you see on social media, but the real thing. The art of making herself seem indispensable to a room full of men who hadn’t planned on respecting her, let alone considering her. She can read a person like a teleprompter, knows exactly how to shift her tone, adjust her posture, time her smiles. Knows the exact point where charm turns into control, when friendliness becomes power.

You grew up watching her do it- absorbing every glance, every pause, every moment where she turned skepticism into loyalty. Your first major sponsorship? It wasn’t talent alone that landed you that. It was Marissa, walking into meeting after meeting armed with laminated proposals, strategic data points, and a smile so warm it was damn near a weapon.

And God help the poor bastards who said no- because Marissa never walked out of a room without leaving at least one person regretting it.

So when Mattia’s posture shifts- when his fingers curl just a little tighter around the folder- you see it for exactly what it is.

This isn’t a foot in the door. You’re not stupid enough to believe that. You’re a long way from safe, a long way from in. But this? This is a crack. The smallest sliver of daylight peeking through a door that should have stayed sealed shut. And if there’s one thing Marissa LeChriste taught you, it’s that a crack is more than enough.

Because a crack can become a gap. A gap can become a doorway. And a doorway, with enough pressure, with enough carefully applied force, can be shoved wide open until the whole goddamn wall collapses.

You can work with a crack.

It’s quiet- the way the room adjusts around you, your bible, your life laid out on the table. A glance exchanged between Franz and Mattia, a note scribbled down by one of the legal guys, a slight shift in how the media reps hold themselves, sitting forward like maybe- just maybe- you could be someone worth building a campaign around, if even just for a weekend. They’re not sold, not yet. But they’re considering it. You can feel the air change, like the whole meeting tilts half a degree in your favor.

Helmut doesn’t react.

He hasn’t so much as blinked in your direction, not since you sat down. But you can feel him watching, the same way a snake watches something small and scurrying across the ground, waiting to decide if it’s prey or just scenery.

That’s fine.

That’s good enough for now.

Because here’s the truth: the business side of this? It’s not hard for you. It never has been. You know how to smile at the right people, how to dress the right way, how to be charming without being threatening, how to crack a joke that makes people want to root for you instead of against you. It’s all manipulation, but not the ugly kind -  it’s survival. And you are fucking excellent at survival.

But none of that -  none of the paperwork you just signed, none of the polite nods from Franz, none of the cautious optimism radiating off Mattia -  none of it matters unless you can back it up where it counts.

On the track.

You can dazzle them in the boardroom all you want, but this sport isn’t won in a goddamn boardroom. It’s won with lap times. With split-second reactions. With the brutal, intimate understanding of what a car needs, what it can take, what it’s asking for through every bump and twitch of the wheel. If you can’t master that, everything else -  the marketing, the PR games, the networking -  it’s all just performance art. A nice, neat obituary for a career that never got off the ground.

You won’t be that driver. So you ask for one thing. Not money. Not special treatment. Not even extra setup time with the car -  because you know that will get you about as far as asking for a unicorn. You ask for the only thing that will actually make a difference.

“A dedicated sim rig,” you say, voice level, hands folded on the table like you’re asking for something as ordinary as a cup of coffee. “Set to car specs. Six hours of uninterrupted drive time every day until Friday.”

Mattia blinks, caught slightly off guard by how quickly you’ve shifted from polite first impressions to cold, practical demands.

You keep going. “I don’t care when. Middle of the night, middle of the day. I’ll work around the press obligations, the strategy meetings, the media work -  all of it. But I need six hours. Preferably eight, if you can swing it.”

The room goes quiet.

Not hostile, not disapproving -  just quiet.

Because you know what they’re thinking. They’ve had rookies before, juniors promoted too soon, kids drunk on their own hype. They’ve seen the swagger, the bravado, the ones who show up convinced that talent is enough, that instinct will save them.

But that’s not you.

You don’t believe in talent like it’s some divine gift. You believe in work. In attrition. In being the last one standing when everyone else has burned themselves out. You believe in cramming yourself so full of knowledge that instinct becomes irrelevant-  you won’t need instinct, because you’ll already know.

You don’t have the luxury of leaning back on raw talent. You never did. You came up scrapping for every seat, scraping every inch of track time you could get, making your own damn data because no one else was willing to care enough to collect it for you. And now?

Now you’re at war.

Not with Mattia, not with Franz, not with Liam or Pierre or even Max-fucking-Verstappen.

You’re at war with yourself.

With the version of you that lived in the Dale Coyne pit, who ate shit and smiled politely and took every ounce of disrespect because you thought it was the only way to keep your career breathing. With the part of you that still remembers your parents taking out a mortgage on a paid off house just to buy you a seat at that team. With the younger version of you that believed you could make it in this sport if you were just good enough.

There is no "good enough" here. There’s only ruthless.

And if it means you work yourself into the fucking ground for the next four days, so be it. If it means you sleep three hours a night and run on caffeine and adrenaline, fine. If it means you fake your way through every press conference, smiling so wide your cheeks cramp, then collapse in a heap of exhaustion afterward, you’ll do it. Because there’s no going back. You will burn yourself to the ground before you let this opportunity slip.

Mattia glances toward Franz, some unspoken communication passing between them, and then he nods. “Done.” You’re certain it’s not a concession. You’re certain it’s not a favor. You’re certain it’s a test.

You’re certain they want to see if you’ll actually do it. If you’ll show up to that sim rig at some ungodly hour and run laps until your eyes blur, until the seat bruises your back, until the muscle memory starts to override the fear gnawing at the edges of your composure.

They want to see how badly you want this.

They have no idea. They have no idea that you will work every single person sitting here under the table. They have no idea you won’t stop until you’ve outworked every strategist, engineer, pit crew member practicing tracking the tire with his gun. That you’ll outwork the race marshalls, the officials, the fucking janitor sweeping the crusty, smushed french fries from the grandstand floorboards come Sunday night.

“Thank you,” you say. They have no fucking idea.

════════════════════ஓ๑♡๑ஓ══════════════════

Hey guys! Happy season kickoff! Apologies for being gone for so long, I've spent the last few weeks editing and re-writing like a madman as I wanted to be able to bulk publish at least to where the story starts to get more involved with Max, which meant I had to hold back the earlier chapters. So, enjoy the next few posts, we will settle into a more regular updating schedule soon. I promise we are getting to the meat soon- but I want to really nail this exposition, fully flesh out the characters and their relationships with others because it makes everything hit SO much harder when we get to where we're going. Just lean into the ride, it will be fun :).

Working on getting a series master list up for easy navigation. As always, your response and interaction are a huge part of how I stay motivated to do what I do, thank you to everyone who followed, reblogged, or commented on the introductory chapter! I read every single one and so appreciated!

[whompthatsucker1981]

real actual nonhostile question with a preamble: i think a lot of artists consider NN-generated images as an existential threat to their ability to use art as a tool to survive under capitalism, and it's frequently kind of disheartening to think about what this is going to do to artists who rely on commissions / freelance storyboarding / etc. i don't really care whether or not nn-generated images are "true art" because like, that's not really important or worth pursuing as a philosophical question, but i also don't understand how (under capitalism) the rise of it is anything except a bleak portent for the future of artists

thanks for asking! i feel like it's good addressing the idea of the existential threat, the fears and feelings that artists have as to being replaced are real, but personally i am cynical as to the extent that people make it out to be a threat. and also i wanna say my piece in defense of discussions about art and meaning.

the threat of automation, and implementation of technologies that make certain jobs obsolete is not something new at all in labor history and in art labor history. industrial printing, stock photography, art assets, cgi, digital art programs, etc, are all technologies that have cut down on the number of art jobs that weren't something you could cut corners and labor off at one point. so why do neural networks feel like more of a threat? one thing is that they do what the metaphorical "make an image" button that has been used countless times in arguments on digital art programs does, so if the fake button that was made up to win an argument on the validity of digital art exists, then what will become of digital art? so people panic.

but i think that we need to be realistic as to what neural net image generation does. no matter how insanely huge the data pool they pull from is, the medium is, in the simplest terms, limited as to the arrangement of pixels that are statistically likely to be together given certain keywords, and we only recognize the output as symbols because of pattern recognition. a neural net doesn't know about gestalt, visual appeal, continuity, form, composition, etc. there are whole areas of the art industry that ai art serves especially badly, like sequential arts, scientific illustration, drafting, graphic design, etc. and regardless, neural nets are tools. they need human oversight to work, and to deal with the products generated. and because of the medium's limitations and inherent jankiness, it's less work to hire a human professional to just do a full job than to try and wrangle a neural net.

as to the areas of the art industry that are at risk of losing job opportunities to ai like freelance illustration and concept art, they are seen as replaceable to an industry that already overworks, underpays, and treats them as disposable. with or without ai, artists work in precarized conditions without protections of organized labor, even moreso in case of freelancers. the fault is not of ai in itself, but in how it's yielded as a tool by capital to threaten workers. the current entertainment industry strikes are in part because of this, and if the new wga contract says anything, it's that a favorable outcome is possible. pressure capital to let go of the tools and question everyone who proposes increased copyright enforcement as the solution. intellectual property serves capital and not the working artist.

however, automation and ai implementation is not unique to the art industry. service jobs, manufacturing workers and many others are also at risk at losing out jobs to further automation due to capital's interest in maximizing profits at the cost of human lives, but you don't see as much online outrage because they are seen as unskilled and uncreative. the artist is seen as having a prestige position in society, if creativity is what makes us human, the artist symbolizes this belief - so if automation comes for the artist then people feel like all is lost. but art is an industry like any other and artists are not of more intrinsic value than any manual laborer. the prestige position of artist also makes artists act against class interest by cooperating with corporations and promoting ip law (which is a bad thing. take the shitshow of the music industry for example), and artists feel owed upward social mobility for the perceived merits of creativity and artistic genius.

as an artist and a marxist i say we need to exercise thinking about art, meaning and the role of the artist. the average prompt writer churning out big titty thomas kinkade paintings posting on twitter on how human made art will become obsolete doesnt know how to think about art. art isn't about making pretty pictures, but is about communication. the average fanartist underselling their work doesn't know that either. discussions on art and meaning may look circular and frustrating if you come in bad faith, but it's what exercises critical thinking and nuance.

[covid-safer-hotties]

Judge orders CDC to stop deleting emails of departing staff, calling it ‘likely unlawful’ - Published Aug 9, 2024

As an MLIS-holding data-shepherd, this breach of records management best practices leaves me hopping mad.

The CDC has likely been violating federal law for years by systematically deleting lower-level employees’ emails, a federal judge ruled Friday.

The ruling by U.S. District Judge Rudolph Contreras came in a lawsuit brought by a legal group allied with former President Donald Trump and was accompanied by an order forcing the public health agency to immediately halt the erasures.

“The Court concludes that CDC’s policy and practice of disposing of former employees’ emails ninety days after the end of their employment is likely unlawful,” Contreras wrote in a 36-page opinion.

Contreras, an Obama appointee, found that the agency had been employing a records-retention policy that had not been approved by the National Archives. That policy led the agency to delete lower-level employees’ emails 90 days after their departure from the agency, rather than the three-to-seven-year retention required by standard National Archives procedures.

The judge said the CDC, along with all other Department of Health and Human Services agencies, had adopted a National Archives protocol known as Capstone that calls for senior officials’ emails to be preserved permanently and sets retention periods of between three and seven years for messages in the accounts of lower-level employees. CDC maintained it only signed on to part of the Capstone approach, but Contreras said the agency appeared to have embraced the whole plan and then abandoned part of it without permission.

“The available evidence suggests that CDC did indeed commit to manage and dispose of its employees’ emails pursuant to the [Capstone] schedule,” Contreras wrote. “Because CDC disposed of former employees’ email records pursuant to a schedule that was not approved by the Archivist, it is likely that … records removed or deleted under the CDC’s unapproved policy were removed or deleted unlawfully.”

Contreras also said that under longstanding federal recordkeeping laws, the National Archives should have referred the matter to the Justice Department but had failed to do so.

Spokespeople for DOJ and the Archives did not immediately respond to requests for comment. A CDC spokesperson had no immediate comment.

The dispute arose last year, when the Trump-aligned America First Legal Foundation filed a Freedom of Information Act request for records about a CDC publication entitled “LGBTQ Inclusivity in Schools: A Self-Assessment Tool.” After months of wrangling, the CDC identified three employees who worked on the document but indicated that two of them had departed the agency and their emails had likely been destroyed.

America First Legal challenged the CDC’s recordkeeping practices as unlawful and urged Contreras to impose a “preliminary injunction,” a legal order requiring that the CDC immediately stop deleting employee emails until the court determines the legality of the process. Contreras’ decision to grant the injunction indicated he believes the Trump-aligned group is likely to prevail.

“The Biden-Harris Administration was actively destroying the records of federal employees at the CDC in blatant violation of the law — and we are pleased that the U.S. District Court for the District of Columbia has ordered a stop to their illegal conduct,” America First Legal’s executive director Gene Hamilton said in a statement. “The Biden-Harris Administration’s politicization of records management must end.”

[womaneng]

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Hey there! 🚀 Becoming a data analyst is an awesome journey! Here’s a roadmap for you:

1. Start with the Basics 📚:

- Dive into the basics of data analysis and statistics. 📊

- Platforms like Learnbay (Data Analytics Certification Program For Non-Tech Professionals), Edx, and Intellipaat offer fantastic courses. Check them out! 🎓

2. Master Excel 📈:

- Excel is your best friend! Learn to crunch numbers and create killer spreadsheets. 📊🔢

3. Get Hands-on with Tools 🛠️:

- Familiarize yourself with data analysis tools like SQL, Python, and R. Pluralsight has some great courses to level up your skills! 🐍📊

4. Data Visualization 📊:

- Learn to tell a story with your data. Tools like Tableau and Power BI can be game-changers! 📈📉

5. Build a Solid Foundation 🏗️:

- Understand databases, data cleaning, and data wrangling. It’s the backbone of effective analysis! 💪🔍

6. Machine Learning Basics 🤖:

- Get a taste of machine learning concepts. It’s not mandatory but can be a huge plus! 🤓🤖

7. Projects, Projects, Projects! 🚀:

- Apply your skills to real-world projects. It’s the best way to learn and showcase your abilities! 🌐💻

8. Networking is Key 👥:

- Connect with fellow data enthusiasts on LinkedIn, attend meetups, and join relevant communities. Networking opens doors! 🌐👋

9. Certifications 📜:

- Consider getting certified. It adds credibility to your profile. 🎓💼

10. Stay Updated 🔄:

- The data world evolves fast. Keep learning and stay up-to-date with the latest trends and technologies. 📆🚀

. . .

[scopethings-blog]

Scope Computers

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[fraoula1]

𝐓𝐨𝐩 5 𝐌𝐮𝐬𝐭-𝐊𝐧𝐨𝐰 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐓𝐨𝐨𝐥𝐬 – 2025 𝐄𝐝𝐢𝐭𝐢𝐨𝐧!

Looking to level up your data science skills? These 5 must-know tools are essential for every data scientist in 2025! From data wrangling to machine learning, these tools will take your projects to the next level. 💡📊

Watch - https://youtube.com/shorts/n-hNExa09sI

Which data science tool do YOU swear by? Drop your thoughts below! ⬇️

🔔 Subscribe for more data science tips & trends!

[gumnut-logic]

Along the way (Part Two)

Sweetapple | Dear Mr Tracy | Along the way - Part 1 | Part 2

Here we are after one hell of a long wait, the next part of the third Sweetapple fic :D A good percentage of this was written back in April, but today I have managed to wrangle it to do what I needed it to do.

Many, many thanks to @onereyofstarlight for the readthrough and cheerleading :D And for Thunderfam cheering me on. Honestly, you guys are amazing to me and I cherish all the support you give me ::hugs the lots of you::

I hope you enjoy this bit :D

-o-o-o-

Māhia was seconds away via Thunderbird One. The blur of mach ‘I need to get there now’, cleared as Scott braked mid-air above the facility.

Experienced eyes scanned the grounds and his heart sank as the clear signs of a building collapse in the older part of the property became clear.

“Thunderbird Five, visual confirmation on scans. Several of the lab buildings have collapsed.” As he lowered One down to land, he shoved her landing gear down in frustration. Those buildings had been reinforced for exactly this reason. Why had they collapsed?

But that was a question for later as he did mental calculations on who might own those five life signs Five’s scanners were reporting.

They taunted him on his display as One blew up dust and settled softly to the ground.

“Five, I need employee locations and I need them now.”

It was Eos who answered. “Yes, commander, compiling them and sending them to your mobile device.”

His thank you was little more than a whisper as he stepped out onto Tracy Industries land as an emergency responder.

Tia hurried across the grounds towards him, Fireman Fred, not far behind.

“Sir, we have six staff missing.”

Damn.

He nodded and pulled up the data Eos had sent him.

It correlated.

Alexander Sweetapple was at the top of the list, followed by Erica Stoltz, Bruce Palmer, Gus Kinnear, Emily Anderson and Violet Drummer.

As he moved to assess the situation, he shut those names in the back of his mind and jammed his hope and despair into a box of professionalism.

Tia’s breath came in a rush. “The engineering team have set up scanners and have been directing the rest of the staff to move debris, but…” Her voice trailed off as her amber eyes stared up at him.

A nod and he put more confidence into his stride. Fireman Fred started spouting stressor numbers and building structures…

Just another rescue, use the tools, save lives and console later.

-o-o-o-

Thunderbird Two swooped in low over Gisborne and Virgil groaned. A series of apartments on the main street appeared to be compromised with partial to full collapse. He hated apartment buildings.

They were always the most heartbreaking.

And they weren’t alone, it was obvious even from this height that there were several sites that would need their attention. With confirmation from John on the priority list, Virgil lowered Two as delicately as he could down into a main intersection, as close as safely possible to the first site.

Automatically speeding through post-flight, Virgil set up the job in his mind. “Gordon and Alan, you’re on pick-up-stick duty. Clear as much debris as possible. Alan, you have Two if needed. I’ll be in the exosuit.” He threw up a live scan of the collapsed group of buildings, absently noting the age of the structures and the possible reason for initial failure. He pointed at the first lifesign. “We’ll start here, follow through to here and pick them off one by one. Any information that requires a change in these priorities, I need to know.” The sixty-four lifesigns flickered at him like heartbeats.

“FAB.” And the professionals who were his brothers were moving.

He climbed out of his pilot’s seat and followed.

-o-o-o-

Scott was missing both his heavy lifting brother and his ‘bird.

Thunderbird One was good for many things, but in this case, Two was better.

Not that he’d ever admit it out loud.

The fortunate thing was that with so many Tracy Industries employees on hand, most of the equipment required to dig a person out of this mess was available.

Mostly.

He missed his brother’s support.

And their ease of working together.

But then, if Alex wasn’t one of these lifesigns, there was no way Scott wanted his brother here.

Virgil had formed an attachment to the likeable engineer. There was no other explanation for his repeated visits to Māhia. At least once a week found him down here knocking heads with Alex. He claimed it was Siliwrap development and Scott had no doubt that was part of it, a very small part. He had seen his brother on a design binge many a time in the past and this wasn’t it.

What it was, was hopeful and although Virgil had yet to declare any intentions of any kind, Scott couldn’t help but smile each time Virgil babbled to him about Alex.

Because he did. Scott knew more about the smart, dark-eyed, pale-haired engineer than he ever thought he would. But then considering his brother’s interest, he made it his business to know as much as possible about Alex. Both Kayo and John had done a thorough security risk assessment on the man and everything had come up green…repeatedly.

This just left Scott happy for his brother.

And nodding and smiling each time one of their late-night discussions turned to the topic of Alex.

Again.

He doubted his brother was even aware of it…and that made Scott smile even more.

But so far nothing had come of it beyond friendship and Scott wasn’t going to push other than give his hardworking little brother a little extra time with his friend when he could.

All Scott had to do today was find that friend.

“Sir! We found him!” Scott startled and despite himself, abandoned the area he had been clearing and ran over to where Fred and his crew were lifting out a prone body.

For just a moment, Scott’s heart lifted, only to be dashed by dark hair and a beard, followed by shame to even be thinking that way. Gus Kinnear was the lucky soul that beat the odds down to one in four that Alex was safe.

Scott saw to the groggy man, only to hand him to the onsite Tracy Industries medical team. His life was in no danger and he would be well attended to while they recovered the others.

Rolling his shoulders, Scott went back to shifting debris.

Just as the ground started rumbling.

-o-o-o-

“Aftershock!” John’s voice bounced through comms as Virgil lifted a couple of tonnes worth of masonry off what turned out to be a mother and child.

He gasped. “Can you move?”

The woman was covered in dust, her baby clutched to her chest, tears had streaked her cheeks. But she nodded, hesitantly, and did her best to get her feet under her just as the earth beneath him literally jumped.

He stumbled, his exo-suit whining under stress as he struggled to restabilise the weight. “Gordon!” He could barely hear his own voice over the roar of aftershock and for a split second he thought he was going to lose it.

But his brother was there. Swooping in and gathering the woman into his arms, baby and all, just fast enough.

Virgil was still holding the weight, hydraulics hissing as he fought with it and the ground beneath him shifted his footing.

“You’re clear, Virgil! Let it go!”

He wasn’t sure if it was Gordon or John, but the load was coming down, regardless. He clung to it, following it down with as much control as he could give it, but when it hit, brickwork flew, catching his legs, his helmet, and taking him down with it.

Virgil hit the ground on his side, a tangled mess of exo-suit and flailing limbs.

-o-o-o-

Part 3

[helicalinsight]

Ask On Data for Big Data: How It Handles Complex Data Transformation Tasks

In today’s world of data-driven decision-making, the ability to efficiently wrangle large datasets is crucial. For businesses and organizations dealing with massive amounts of data, the process of data wrangling becomes even more complex. That’s where a powerful data wrangling tool like Ask On Data comes into play. Designed to handle intricate data transformation tasks, Ask On Data simplifies the process of cleaning, reshaping, and preparing big data for analysis.

What is Ask On Data?

Ask On Data is an advanced data wrangling tool that streamlines the process of transforming and preparing raw data for analysis. Unlike traditional data preparation methods, which can be time-consuming and prone to errors, Ask On Data uses automated algorithms and AI-driven techniques to efficiently clean and structure datasets. It is particularly useful for handling large, complex data sources that require extensive manipulation before they can be analysed or visualized.

Handling Big Data Challenges

Big data refers to datasets that are so large or complex that traditional data processing methods are insufficient. In the realm of data wrangling, this often translates to challenges such as dealing with missing values, duplicate records, inconsistent formats, and unstructured data. Additionally, working with big data often means managing high volumes of information in real-time, making it even more difficult to transform and clean the data without specialized tools.

Ask On Data excels in this area by providing robust features that address these challenges. Here’s how

Automated Data Cleaning and Pre-processing: One of the key features of Ask On Data is its ability to automate data cleaning. It identifies and handles missing values, duplicates, and inconsistencies in data formats, saving time and reducing errors. This is particularly beneficial when working with large datasets that often contain inconsistencies that need to be addressed before any meaningful analysis can take place.

Efficient Data Transformation: Ask On Data supports a wide range of data transformations, from simple operations like filtering and grouping to more advanced transformations such as pivoting, reshaping, and aggregation. These transformations are essential when preparing big data for analysis, as raw data often needs to be reshaped to fit the desired analytical models or reporting formats.

Handling Unstructured Data: Big data is not always in a structured format. Many datasets come in unstructured forms such as text, images, or logs, which require specific techniques to process. Ask On Data has built-in capabilities for parsing and transforming unstructured data, allowing users to convert it into a more structured format for analysis.

Scalability: Big data often involves datasets that are too large to be processed on a single machine. Ask On Data can handle scalable datasets, allowing users to process data across distributed systems. This ensures that even the largest datasets can be transformed and analysed without performance degradation.

Real-Time Data Wrangling: In many cases, businesses need to work with real-time data streams to make quick decisions. Ask On Data can perform real-time data wrangling on live data, allowing organizations to clean and prepare data as it arrives. This is essential for use cases like IoT data analysis, real-time business intelligence, and fraud detection.

Why Choose Ask On Data for Big Data Wrangling?

Ask On Data stands out among other data wrangling tool due to its comprehensive features and ease of use. By automating much of the tedious and repetitive tasks associated with data wrangling, it significantly reduces the time required to prepare large datasets for analysis. Furthermore, its ability to handle both structured and unstructured data makes it a versatile tool for any big data project.

For organizations working with massive datasets, Ask On Data’s scalability ensures that users can handle data growth without worrying about system limitations. Whether it’s cleaning up messy data or transforming raw data into a structured format ready for analysis, Ask On Data provides the tools necessary to get the job done.

Conclusion

Big data presents unique challenges in data wrangling, but with a powerful tool like Ask On Data, these tasks become much more manageable. From automating data cleaning to handling unstructured data and ensuring scalability, Ask On Data provides all the features necessary to tackle complex data transformation tasks. For anyone working with large datasets, Ask On Data is a must-have data wrangling tool that can drastically improve efficiency and data quality.

[innovativeroutinesinternational]

Effortless Data Wrangling and Test Data Preparation

Data wrangling and test data preparation tools simplify organizing, cleaning, and transforming raw or test data into accurate, usable formats. These tools automate data extraction, validation, and formatting, ensuring high-quality datasets for testing and analysis. They are essential for enhancing efficiency, reducing errors, and improving decision-making in data-driven workflows. Visit Us: https://www.iri.com/solutions/business-intelligence/bi-tool-acceleration/overview

[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!

[neebifytool]

Big Sales Growth: How Our Platform Delivered Real Results?

Neebify is the platform which uses automation coupled with CRM systems for delivering real and measurable results for businesses. It automates getting connection requests, messaging, and follow-ups in line with guidelines on LinkedIn. The other core features include automated outreach, CRM integration, targeting your audience, customized campaigns, and analytics or reporting.

There are high expectations to view LinkedIn automation as a step that can play an important role in today's sales strategy, including sending in connection requests, messages, and follow-ups at mass levels by streamlining the efficiency. In this case, it will make sure to produce even more qualified leads, consistency in engagement, scalability, and even super management of the data.

Yes, a mid-sized B2B software company had a case study that showed how Neebify transformed the entire LinkedIn outreach approach to remarkable sales growth. Neebify explained to the company that they could help it overcome the challenge of a small number of connections, which could not help scale, and a limited reach because their sales force had to send connection requests and follow-up messages manually. Neebify offered them a solution that would allow them to reach more prospects, keep leads engaged consistently, and connect their LinkedIn efforts with their CRM for better management of leads.

In conclusion, Neebify is the future of sales growth automation, as through good automation and proper CRM systems, its use can bring optimum results for businesses in the future.

A B2B software company used Neebify for automated LinkedIn outreach and its integration with their CRM. They used Neebify to send customized connection requests to industry decision-makers and influencers and then followed up automatically with a series of follow-up communications to nurture those connections and move people further down the sales funnel. They hooked up their CRM so sales were spending more time closing deals and less time wrangling data.

Neebify provided advanced filtering features that allowed the company to pinpoint leads of interest with precision based on title, industry, and company size. The company was allowed to create several campaigns on LinkedIn, targeting different aspects of the target audience.

Between three months, the company managed to realize an impressive 200% growth in sales opportunities, and conversion rates improved, leads were handled in a better way, less time spent at work not automated, and data-driven decisions made in a much more efficient way. Among the most outstanding ones was the plan targeting decision-makers in the tech industry where the acceptance rate of connection requests had reached as high as 150%.

By combining Neebify's LinkedIn automation tool with its integration into a CRM, it could potentially really get the sales of businesses sky-rocketing. This is due to the fact that every sales effort would scale while not dropping personalization and efficiency. One good example would be the case of the B2B software company where the right application of automation on a LinkedIn strategy could really reach new heights while bringing in significant, measurable results.

[education43]

What Are the Qualifications for a Data Scientist?

In today's data-driven world, the role of a data scientist has become one of the most coveted career paths. With businesses relying on data for decision-making, understanding customer behavior, and improving products, the demand for skilled professionals who can analyze, interpret, and extract value from data is at an all-time high. If you're wondering what qualifications are needed to become a successful data scientist, how DataCouncil can help you get there, and why a data science course in Pune is a great option, this blog has the answers.

The Key Qualifications for a Data Scientist

To succeed as a data scientist, a mix of technical skills, education, and hands-on experience is essential. Here are the core qualifications required:

1. Educational Background

A strong foundation in mathematics, statistics, or computer science is typically expected. Most data scientists hold at least a bachelor’s degree in one of these fields, with many pursuing higher education such as a master's or a Ph.D. A data science course in Pune with DataCouncil can bridge this gap, offering the academic and practical knowledge required for a strong start in the industry.

2. Proficiency in Programming Languages

Programming is at the heart of data science. You need to be comfortable with languages like Python, R, and SQL, which are widely used for data analysis, machine learning, and database management. A comprehensive data science course in Pune will teach these programming skills from scratch, ensuring you become proficient in coding for data science tasks.

3. Understanding of Machine Learning

Data scientists must have a solid grasp of machine learning techniques and algorithms such as regression, clustering, and decision trees. By enrolling in a DataCouncil course, you'll learn how to implement machine learning models to analyze data and make predictions, an essential qualification for landing a data science job.

4. Data Wrangling Skills

Raw data is often messy and unstructured, and a good data scientist needs to be adept at cleaning and processing data before it can be analyzed. DataCouncil's data science course in Pune includes practical training in tools like Pandas and Numpy for effective data wrangling, helping you develop a strong skill set in this critical area.

5. Statistical Knowledge

Statistical analysis forms the backbone of data science. Knowledge of probability, hypothesis testing, and statistical modeling allows data scientists to draw meaningful insights from data. A structured data science course in Pune offers the theoretical and practical aspects of statistics required to excel.

6. Communication and Data Visualization Skills

Being able to explain your findings in a clear and concise manner is crucial. Data scientists often need to communicate with non-technical stakeholders, making tools like Tableau, Power BI, and Matplotlib essential for creating insightful visualizations. DataCouncil’s data science course in Pune includes modules on data visualization, which can help you present data in a way that’s easy to understand.

7. Domain Knowledge

Apart from technical skills, understanding the industry you work in is a major asset. Whether it’s healthcare, finance, or e-commerce, knowing how data applies within your industry will set you apart from the competition. DataCouncil's data science course in Pune is designed to offer case studies from multiple industries, helping students gain domain-specific insights.

Why Choose DataCouncil for a Data Science Course in Pune?

If you're looking to build a successful career as a data scientist, enrolling in a data science course in Pune with DataCouncil can be your first step toward reaching your goals. Here’s why DataCouncil is the ideal choice:

Comprehensive Curriculum: The course covers everything from the basics of data science to advanced machine learning techniques.

Hands-On Projects: You'll work on real-world projects that mimic the challenges faced by data scientists in various industries.

Experienced Faculty: Learn from industry professionals who have years of experience in data science and analytics.

100% Placement Support: DataCouncil provides job assistance to help you land a data science job in Pune or anywhere else, making it a great investment in your future.

Flexible Learning Options: With both weekday and weekend batches, DataCouncil ensures that you can learn at your own pace without compromising your current commitments.

Conclusion

Becoming a data scientist requires a combination of technical expertise, analytical skills, and industry knowledge. By enrolling in a data science course in Pune with DataCouncil, you can gain all the qualifications you need to thrive in this exciting field. Whether you're a fresher looking to start your career or a professional wanting to upskill, this course will equip you with the knowledge, skills, and practical experience to succeed as a data scientist.

Explore DataCouncil’s offerings today and take the first step toward unlocking a rewarding career in data science! Looking for the best data science course in Pune? DataCouncil offers comprehensive data science classes in Pune, designed to equip you with the skills to excel in this booming field. Our data science course in Pune covers everything from data analysis to machine learning, with competitive data science course fees in Pune. We provide job-oriented programs, making us the best institute for data science in Pune with placement support. Explore online data science training in Pune and take your career to new heights!

[sonicasura]

Random ideas for the Transformer Crossovers with Pokémon and Digimon

Sleuth Half Data form ends up doing a Mega Man and copying appearances of Transformers

Frontier accidently revives a Predacon Fossil and ends up having too wrangle the newly revived Predacon

Mix and Scraper take too calling HeavyLeomon "Big Boss" and calling the Mega Level Digimon an Honorary ConstructiBot

All Transformer iteration with Sleuth and Frontier will have them finding and taking care of the Dinobots

Soundwave finding 2 other Pokémon besides Rumble and taking care of them in the Pokémon Crossover

Sleuth and Frontier treating the Autobots too some well deserved Rest and Relaxation in the Bayverse Iteration

Invismon and Soundwave showing of Stealth Wing and Lazerbeak too each other

I just have the image of Invisimon and Soundwave holding their respective companion like a cat. Two things came to mind with Sounders getting more Pokemon. Ravage being a Hisuian Zorua that prefers disguising themselves as a cat and Frenzy is an Iron Bundle who's quick to freeze any uninvited snoopers in Soundwave's space while he's gone.

Sleuth would be such a menace if they ever got ahold of Cybertronian cosmetic data. I totally see HeavyLeomon being confused for a Constructicon than just a Beastformer since their design features lion style heavy construction tools from jackhammers to shovels.

[andmaybegayer]

Last Monday of the Week 2024-04-15

alright everyone gather round the kringlefucker and let's get this over with

Listening: A while ago I asked about who TMBG was and I think @sybilius recommended starting with Flood. I would not say I'm up to actually liking this yet but I can see myself getting there if I stick with it.

Watching: I stopped midway through The Mandalorian Season 2 so I picked that back up. Just met Ahsoka for the first time in a while. I continue to be glad that they're doing things in Star Wars that don't necessarily involve the Skywalkers all the time, I really do need to read the Thrawn books at some point. They should make a movie with the Yuzhan Vong (they should not make a movie with the Yuzhan Vong.)

Reading: Got Piranesi by Susannah Clarke from the library over the weekend, and a Blake poetry collection. About ¼ of the way into Piranesi and as a big fan of Piranesi-esque Big Fucking Spaces this is great. I love the idea of the tides holy shit. Perfect imagery.

Playing: Back at Dark Souls, killed Seath the Scaleless and now I'm trying to make my way into the New Londo Ruins, I got a good way in and then got kicked off a ledge by a ghost.

Making: Fidgeting with different approaches to scripting for the RGB LED project, finally figured out why my Lua performance was shit and fixed that, so I think I'll just do Lua scripting, just need to make sure you can access data from Lua into C fast enough to make it worth it. Should be fine.

This project has been fun even if it has been mostly dead ends, I have gone from writing a scripting Lisp in Rust to trying to wrangle some expression evaluators to shooting instructions directly into RAM to embedding Lua.

Tools and Equipment: cosmetics things! I have spates of really annoying acne and the only thing I have ever found that reliably deals with that is benzoyl peroxide cream. You just dab or wipe some over the affected regions and it'll dry up pimples in a few hours.

The only thing to watch out for is that it is an extremely strong bleach so you can't sleep with it, and you also don't want it on your clothes., usually what I do is put it on when I get back from work and keep it on until I shower at night. One to two days of that and pretty much any acne is gone.