#Non-linear career progression

[ourjobagency]

In a world that often emphasizes traditional career paths, it's easy to feel pressured to follow a predetermined route to success. However, not everyone fits into the conventional mold, and there are countless examples of individuals who have achieved greatness through unconventional career paths. These alternative routes to success are inspiring and demonstrate that success

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[astrologydray]

Ruler of the 2nd through the houses

when you track the ruler of the 2nd house through the houses, you’re looking at how you make money, where your values lie, what you prioritize, and what brings you a sense of security and self-worth.

1st House 🏡:

I am the resource.

Your body, presence, or identity is a source of value. You might attract wealth through personal branding, entrepreneurship, or just being YOU. Confidence = currency. You naturally radiate value, but must learn to own it.

2nd House 🏡:

Born to build.

This is a powerful placement for money, stability, and long-term growth. You naturally know how to build wealth and manage your resources. You’re probably very grounded and value quality over quantity. Shadow side hoarding, fear of change, or stubbornness.

3rd House 🏡:

Money through the mind.

Your voice, ideas, or communication skills are your goldmine. You might make money through writing, teaching, media, or even tech. You value curiosity, mental stimulation, and versatility. Prone to having scattered energy or difficulty monetizing ideas. Your Strength = quick thinking, adaptability, networking = resource magnet.

4th House 🏡:

Home is the foundation of wealth.

You could inherit money, make money through property, or work from home. Emotional security and family support directly affect your money flow. Your values are deeply rooted in your upbringing.

5th House 🏡:

Creative currency.

You attract money through self-expression, creativity, pleasure, or even romance. Think artists, performers, designers — or people who monetize their passions. You value joy, fun, and being seen. Shadow side here = risky money behavior; tying worth to external validation.

6th House 🏡:

Work = worth.

You build wealth slowly and steadily through dedicated effort, skill development, and service. You might work in healing, wellness, administration, or service industries. You value discipline and reliability. Overworking or tying self-worth to productivity may be a problem for you. Relax and give urself grace.

7th House 🏡:

Money through others.

Your values and income may come through partnerships, collaborations, or clients. Business and romantic relationships affect your money deeply. You value harmony, balance, and reciprocity. Be careful of falling into financial dependency or people-pleasing around money.

8th House 🏡:

The wealth alchemist.

You’re drawn to shared resources, investments, and transformative wealth. You might make money through occult work, finance, psychology, or sex-related fields. Power, trust, and depth play a big role in your money story.

9th House 🏡:

Expand to receive.

You attract abundance through travel, teaching, spirituality, law, or publishing. You value freedom, knowledge, and growth. Belief systems around wealth are HUGE here — mindset is everything.

10th House 🏡:

Public success = personal wealth.

You may gain money and security through career, status, or reputation. You value ambition, recognition, and doing something that matters. This placement often pushes you toward visible leadership or high positions.

11th House 🏡:

Money through the collective.

You earn through networks, innovation, tech, or social causes. Think online businesses, group work, or digital platforms. You value progress, originality, and future-oriented thinking. Be careful of being overreliance on external validation or digital platforms. Your unique ideas, group alignment, big-picture wealth building is where it’s at.

12th House 🏡:

Mystical money flow.

This is the most non-linear placement. You may make money through spiritual work, healing, art, or behind-the-scenes roles. Money may come and go mysteriously, and your values are more ethereal than material.

[wesstars]

crush

cairo sweet x fem!reader (no pronouns used)

summary: when cairo goes home, what comes to mind are thoughts of you. wc: 2.3k tags: explicit, minors DNI!! all characters 18+. university au. masturbation, smoking, non-linear narrative. reader is cairo’s teaching assistant, reader described as masc presenting. a/n: let me know what y’all think :) for the vibes

masterlist

“Is Professor Miller not coming?” Winnie had just dropped into her unassigned assigned seat next to Cairo, two minutes before Greco-Roman Literary Theory started. The flipping of pages punctuated the chatter of other students waiting, a comfortable sound.

“He said he’d be gone today,” Cairo replied absently. “There’s a ‘guest lecturer,’ our teaching assistant.”

“Oh, right. Who’s that?”

Cairo shrugged. “Who knows.” 

As if on cue, the door swung open. Cairo didn’t even look up—Miller mentioned that he kept a handful of research assistants that would be there to help with the advanced reading. But honestly, Cairo wasn’t sure what they could tell her that she didn’t already know. A melodic hum fell through the air for just a moment, a chorus. 

“Good morning.” At your lilting voice, rough with the edge of 10am, Cairo started. She watched you set your messenger bag on the desk. Your white shirt pulled over your shoulders; there was a glint at your collar, a necklace peeking through. A thin watch adorned your wrist. Winnie, along with some of the class, echoed your greeting, and Cairo blinked.

Late spring afternoon draped across the furniture in Cairo’s room, the quickly waning light giving easy way to a blue hour. Dropping her bag at the door, she tore off her shirt and skirt with the confidence of one standing before a crowd. Running a hand up from her sternum to her neck, she stretched languidly, sinking down onto her bed. After so many uneventful days—when she applied to Yale, she didn’t think that there would be any uneventful days—she finally had a story to turn over in her mind. 

You. You were a mystery. Even as you had started the class with an introduction, telling Cairo you’d graduated from a middle-of-nowhere college in California and sought a writing career in Vermont before delving into research, she longed to lay out the details and pull them out from under the rug. Where did you learn to teach? Did you like to drive, or be driven? Mountains, or the sea? Where did you grow up? Was there coffee or tea in your cupboard? Cairo’s stomach burned to know. Her dark eyes burned the ceiling with smoke signals, searching for you even though you were god knows where in that seaside state.

Arching her back, Cairo let her hand travel down, palm flat against her stomach, to trace the seam of her upper thigh. As the class had progressed, your keenly observant nature did not elude Cairo. Maybe listening was something that your pedagogy instilled in you, but the way you held each student’s question in the cant of your head, an answer in your crinkling eyes, listening seemed to be in your nature. It was meticulous, the way you picked apart the class text, weaving in references and tying it all in. In that two hour lecture, Cairo learned that you watched the same way you listened. 

Balmy as it was, the humidity made her dark waves cling to her skin, and she shivered as she brushed them back, thinking of a different pair of slim hands. Your scrutiny of each student had an intention that she couldn’t quite place; a determination that thrilled her. Cairo imagined that you’d observe her the same way, that she would be the one you were most fond of. It was only natural that her own attention would draw yours onto her. Holding the weight of your envisioned gaze made Cairo’s core twist, a pleased little flush that she prayed you could see. Your affected impartiality didn’t bother Cairo—in fact, it pulled her into your shadow. In her bed, she rolled onto her stomach then her knees, shaking her hair out. 

Her hands were steady as she reached for her bedside table, thumb rolling on the wheel of her zippo as she held the cigarette to her lips. Cairo took a drag, blowing out neat smoke rings as she settled back on her heels. The skin of her own fingers was cool against her lips, and when she took the smoke away, she studied the pattern of her lipstick on the white paper as she had so many times before.

She’d watched, unabashedly and unafraid of being caught, as you drummed your fingers on the chalk tray. Would your fingertip be soft or work hardened if it pressed down her tongue? Would your skin carry the stain of her red lip as deeply, as obediently, as the malleable wrapping paper?

“Alright, class,” you cleared your throat, turning slowly around the room to make eye contact with each student. “As you know, Jonathan’s away on a conference today. I’ll start with a bit of roll, just so I can learn your names. Not many of you come to my office hours, I know.” You smiled easily. It was so guileless, Cairo mused, nearly childlike. You had the class go around the rooms with names and majors, a circuit that Cairo gave no attention to other than your lilting rhythm of hums, the tapping of your foot on the floor, the way you flicked the corner of the role sheet with your thumb. Your gaze was soon on hers, waiting expectantly. She looked right back with a blink.

“Cairo Sweet. English major.”

“Cairo.” Her name rolled off your innocent little grin, making her cock her head. “Wonderful.” Fascinating. Would you whisper midnight black desires in her ear, so deep and dark they might be murmured into the ink of your own empty room?

You continued, circling back to the front and easily transitioning to the lesson plan. You had an awfully effortless way of grasping the class’ attention, holding gently and never forcing. It wasn’t like Professor Miller, who always seemed to hasten through the lecture so he could return to his research. She could tell you liked the woods of the text, to fall down into the depths of each word, feeling its weight in you and letting it rock. Just like Cairo. 

She sighed into the warm air prickling up her skin, the curl of your voice around her name making her nipples harden in her bralette, even in retrospect. Exhaling around her cigarette, Cairo brought her hands up to palm her breasts, feeling the drag of her rubied nubs on her palms. Was it the high of the nicotine, the blur of smoke ridden air that made her float straight up into the lofty space you’d created in her mind? Though the feel of her own fingers scraping the lace against her skin was familiar, she found herself keen to think of your soft or callused hands. She was wet already, and she couldn’t remember the last time she’d gotten wet so fast.

The weight she imagined of your touch on her flushed skin was completely, deliciously foreign. Unbidden but intimately welcome, Cairo wished that your caress would find the map of her chest as familiar as a classic, something you had searched a million times over yet always managed to find something new. Shamelessly, Cairo trailed her fingers down her stomach, nails catching on every rib as she arched her back in the spilled moonlight. The mystery in the crossing of your long legs as you’d leaned back on the desk climbed up her belly, curling in the thump, thump, thump, of her heart. The uneven roll of your sleeves clung to the corners of her eyes, eidetic and oh, so, tempting. She had watched you so ardently—did you like to watch? Would you watch? 

The space between her thighs was achingly empty, craving the set of your narrow hips. She was comfortable there, and she remembered the taut stretch of wool as you dropped into your chair and set one ankle over your knee. There was something endearing about the way your trousers had pulled up to reveal slouchy black socks, and darker her mind went as the material pulling creases around your lap made her shudder and—she reached behind to pull one of her fluffy pillows under her, smoke billowing into the air. 

Cairo gave her hips an experimental roll, imagining it was the soft fabric of your slacks against her aching cunt, and grinned around her cigarette. Unlike the pillow, you would be ever so solid under her, grabbing for her thighs like a dog yearns to please. Were you more likely to bruise her skin, yanking her into you without care for blood—or would you guide her gently, make a home in her innocence and hold her more dearly than life ever could? Either way, your desire for Cairo would be so apparent that you couldn’t help yourself.

The dip of your tongue in her navel, the little smirk you’d undoubtedly wear as you went down further—would you go for her throbbing clit first, or would your lips press so warm—she didn’t know. She didn’t have to, content with all those different versions of you unfurling before her. In her bedroom, each time she moved her hips, it became easier to imagine you guiding her actions, the bump of your nose on her folds, damned if not addicting.

Cairo grinned as she fell onto her forearms, hips pushing into the soft pillow without abandon. The slide of her panties soaked with slick against her sensitive clit felt like the delicate press of your splayed hand on her desk as you’d passed, eyes occupied by the text you were holding. It had only been a split second, but it was enough for her to memorize every crease, every vein. Cairo let out a whine, a demanding little sound, as her movements grew erratic. Looking up into the heaven where you must be, she imagined that you’d murmur to her, “I’m here, I’m here, how could I be anywhere else but here?” as you traced the dip in her back. Her arousal took her down every sullied path she’d ever dreamed of, but her mind stuck on one gesture that made her mouth go dry. 

She remembered the way your shirt got just a bit untucked when you stretched during the class break. You’d instinctively tucked it back in, quick as you surveyed the class. Cairo thought that you’d dress yourself back up the same way after you bent her over the desk after class, pushing her skirt up and shoving your fingers into her, painting bruises onto her hip bones with how tight you held her.

The two of you would share a mutual understanding that she wanted this, wanted it bad enough for you to take it whenever you saw fit. Cairo decided that today, this time, you’d be as rough as you pleased, a cup of pens clattering to the ground as you pushed her down, forearm across her shoulder blades. Your necklace would be cold on her warm skin, would it be cold on her tongue? You’d put two, three fingers inside, humming in that absentminded way you did. She thought you’d nuzzle into her ear, all lips and sharp teeth, asking if she’d sprayed your favorite hair mist of hers because she hoped you’d notice—she did—and take her, break her, whatever you wanted. 

You’d send her plummeting down towards a deeper hell (or was it higher, up to your majestic heaven?), already knowing everything that her body needed. Cairo imagined herself coming so helplessly around the stretch of your fingers, so high strung from nights of trying to mimic the press of your touch on her clit, unable to reach the same heights you sent her to. As she held back tears, eyes on the ceiling in reverence, feeling herself drip to the floor, you’d sigh as your mind wandered to other things already, carelessly running a hand down her back. 

Cairo gasped, dropping her nearly finished cigarette in favor of gripping the bed sheets. The white fabric wrinkled around her fingers, reminiscent of your shirt creasing as you’d rolled your sleeves up. This was something new you could show her, just how fast she could come and just how wet it made her. It was a marvel, feeling the fabric cling to her cunt, almost as good as how you’d feel. Resting her forehead in the crook of her elbow, she murmured your name over and over again, a little susurrus of a litany, so similar to your preoccupied hum. Panting, Cairo giggled in her bliss, soft and bright as Californian oranges clinging to rich leaves. You were dark enough to be tucked into the wrinkles in the soft pillow, dark enough for Cairo to love, as a journal loves a secret.

Sated, Cairo grabbed her phone and typed your name in. The results spilled out, and she scrolled, looking for all of the details in the background of your social media posts, curiously drunk on the year’s gap in your CV. Cairo noticed the perfect little circle where the cigarette had burned when she dropped it, and she brushed away the remnants. The gesture smeared the ash on the sheets.

—

Walking into your office with barely a knock, Cairo took in the familiar room of an academic, but with your unfamiliar knick knacks around the place. A lighter, a leather wallet, glasses and wired headphones. You didn’t look surprised as you glanced up from your laptop. Instead, you smiled. 

“Cairo, isn’t it?” 

A flush of pleasure shot straight into her—you remembered. She nodded. Your shelves were covered in books and stacks of reviews, the morning’s leftover cup of coffee sitting on one of the ledges. Did you smoke before, or after your coffee? The terrible, terrible want to replace the taste of smoke on your tongue with the taste of her gave Cairo just the confidence she needed. 

“What can I do for you?”

Cairo leaned over your desk, watching the way your eyes dropped to her burgundy lipstick. “Would you be able to help me on the Aristophanes reading?” She pushed her copy of The Clouds towards you. “I can’t seem to grasp it.” Your eyes met hers. “Of course.”

--

a/n cont'd: can you read my mind, i’ve been watching you… there’s just something about you, baby… ♪ / hope you enjoyed @woewriting :)

please do not repost, reproduce, copy, translate, or take from my work in any way. thank you!

masterlist

[centrally-unplanned]

In news from a different world, last December J-pop idol Miho Nakayama passed away, quite shockingly so at the age of 54. I have no connection to her music or acting, but of course I do appreciate her role in the very early history of video game development and dating sims via the 1987 Famicom game Nakayama Miho no Tokimeki High School, which I have discussed before. I decided to play the game "in memoriam", as it were - it does in fact have an English patch, and you can see a playthrough of said patch on YouTube here. It was time to experience my very own 80's high school idol love story <3.

To the surprise of no one, this game sucks. It essentially had to, no real fault on the developers, but that doesn't change the facts. It is working with incredibly limited graphical capabilities of course, with the average scene looking like this:

Which just isn’t enough for “ambiance” immersion to work, every setting is generic by definition. That can of course be saved by a good plot or gameplay, but neither shows up here; there is barely any story to speak of. Main Guy goes to new school, meets “Mizuho”, realizes she is secretly pop idol Miho trying to live a normal life, they start dating, and paparazzi-types and the pressures of her career get in the way such that eventually (based on your route progression) she breaks up with you or you stay a couple and ride off into the sunset together. Literally by the way, a friend loans you a motorcycle so you can escape the press:

You might be saying “surely you are skipping some things” but I assure you it is nothing important. Neither Miho nor the main character have any personality to speak of, and your time is filled generally by comedic hijinks or just the mechanics of progressing the relationship. There is a fat-faced friend who gossips about school, you have a family that ~exists, there is a stuck-up rich girl you speak to about twice before she kidnaps you in order to serve you drugged food so you will date her (as was typical for 1980’s courtship norms) which happens solely to make you late for a date with Miho to create drama, and so on - it is all as tiresome as it is irrelevant. You can even poke your head into the girl’s locker room at some point, the crown jewel of filler content:

This isn’t even arcade-cabinet-strip-mahjong levels of hot, I know video games of the era could do better than this! Though for all the extraneous plot beats and side characters, I did like “The Trio”, a group of cackling girls who follow you around like a Greek chorus taunting you for your desires:

In another game these fey spirits would devour your organs at the right moment, mad respect. 

Anyway, all of this plot filler is used to stretch out the non-story but in that task it gets a helping hand from the game mechanics, which are a classic example of arbitrary progression gatekeeping. Half the dialogue options are just variants of the same core emotion, and the right answer is inscrutable. You get moments like this one, where Miho is apologizing to you for a misunderstanding:

And all of these answers are pretty dismissive? But the right answer is A, the meanest of them! Guess she has a type, but since you as a player haven’t negotiated her safe words yet you don’t know that and are just gonna facecheck your way through these.

As the cherry on top the advertised “facial expression” system is actually a letdown - it is very rarely used, most dialogue options don’t ask for it, and when they do you have six options:

But you actually never use half of these, and 90% of the time the correct answer is “normal”. At least this was bad in a “too easy” way, so it doesn’t waste your time, but you could just remove it as a mechanic and miss nothing. All of the “interactive” elements could be replaced by linear narrative, actually, and nothing would be lost.

Besides the competitive media mix aspects of the game, obviously. Which is what it is all about, right? This ain’t some random 8-bit idol, this is Miho Nakayama! And even in-game she is pretty cute, I do like the design for the close-up convos:

The glasses-for-disguise are nice with her moe eyes, the details of the shading really pop in an 8-bit context, and really the whole framework of the UI as this sort of flip picture book is adding value here (as opposed to being irrelevant in the location shots). They even give her a bunch of different outfits on your dates because as the heroine she deserves it:

“Ash, those first two are literally just palette swaps” “No man, look, the red one is using dithering to create a fade effect on the colors, implying a more complicated pattern like plaid thatching, while the blue one uses bold lines to imply a striped coat”. It was impressive in 1987, alright! This girl has no textual personality but there is life in this design that stands out from its peers.

But of course it isn’t the in-game graphics doing the heavy lifting here. As mentioned before, this was a “Telephone Game”, where players would be prompted at times to call phone numbers Nintendo had rented out to hear voicemails Nakayama had recorded. These voicemails are, to the best I can tell, lost to us - I have not found an existing recording online. They were only up briefly actually, for a few months after the game was released - this was not an era where longevity for games was considered important. We do have transcripts of them though, and I can imagine that picking up your house phone, calling a phone number, and getting the actual voice of the “character” in the game talking to you - making your heart go doki doki if you will - must have been pretty cool.

(Miho even travels throughout the game, and the phone numbers - according to this blogger - actually use location-appropriate area codes so it feels like you are really calling Osaka or Hokkaido! Very cool…unless - according to another blogger - you got hit with long distance calling charges for your pursuit of troubled love, as was reported in the media at the time. Now that’s authenticity?)

This mechanic is essentially a ludomantic experience that is impossible to capture today, because voice acting in video games is incredibly common; so much so that it would come off as gimmicky to make someone go through such a multi-device process. But since the Famicom couldn’t make vocal sounds, it had to make you use your phone, which created the simulacrum of actually calling a real human outside of the game to talk to. That is pretty neat!

As mentioned, the media mix came bundled with a competition - the winners were the first 16,000 players to submit a “Best Ending” record via the barely-used Famicom Disk Fax system. As helpfully explained in the instruction manual alongside photos of the IRL Nakayama:

And the big prize of a VHS tape of behind-the-scenes Nakayama stuff has been preserved, and is easily available if you want to watch it. Don’t though, it isn’t worth it; it is primarily b-roll footage of her doing typical day-to-day tasks and softball interview questions about “what is her type” with generic answers, stuff like that. Solid C- for the genre. But still, you didn’t know that when competing, right? The pressure to get your game file in was fierce.

I mentioned how the game essentially “had to be bad” at the start, and I want to dig into why that is. In my initial post I linked, I actually made a false statement - I said the development time for the game was “2 weeks”. I said that because the game’s Wikipedia page in English says it and so it is common trivia on the net, but I don’t think that it is true. Even when I typed it in that original post, the back of my mind was going “wait, that can’t be literally true, it is very hard to make a game that fast in that era - these guys are coding in Assembly!”, but I sort of hand-waived it away as, oh something like they were harvesting an existing game prototype or somesuch. But I believe this fact comes from a mistranslation of interviews like this one:

岩田: 坂口さんは『ファイナルファンタジー』の開発を終えて、『トキメキハイスクール』に合流されたんですか？ 坂口: ええ。チームの何名かが合流して、３カ月間くらいでしょうか。で、最後は１０名くらいのメンバーといっしょに京都にやって来て、２週間くらいカンヅメになって、なんとか開発を終えることができたんです。

Or:

Iwata: Sakaguchi, did you join the "Tokimeki High School" project after finishing development on "Final Fantasy"? Sakaguchi: Yes, that’s right. Several team members joined the project for about 3 months, I think. And then near the end of development, about 10 of us came down to Kyoto and we holed up for around 2 weeks until we somehow managed to finish the game.

So what is going on here is the game’s development was a joint production between Nintendo - in Kyoto at this time - and up-and-coming game company Square in Tokyo. And yes, they were literally working on Final Fantasy right before this game, and switched gears to tackle this new project. Or at least some of them did, for 3 months, and then famed-director-of-Final-Fantasy Sakaguchi came down to Kyoto and lived out of a hotel for two weeks doing crunch to finish it off. That fact, probably because Sakaguchi is the famous person reporters would care about, got transformed into the idea that the whole game took 2 weeks to make. 

In this same interview they talk about how, at the end of that crunch, they all went out for drinks to celebrate…until they got a phone call about how the motorcycle in the ending credits is glitching out and flying off the screen, which they thought was a hilarious, beautifully fitting bug for their time together. And that is hilarious, the primary reason I am recounting it, but I also think it goes to show that this was a hot mess of a game dev process. 2 weeks or ~3 months, both of those are not enough time. And with two companies in different cities, doing crunch out of a hotel, wrangling with a record label for a pop idol’s permission, setting up phone line recordings and VHS tapes and a bonus competition using experimental fax machines, all aligned with a media blitz? All for a game genre that honestly hadn’t been done before? I have checked, and you can authentically argue this is the first ever dating sim, at least on a console. People overstate what it is inventing - it is pulling tropes from romance anime and manga, of course - but even that process of transference is tough. This wasn’t a genre yet, and in a way they weren’t even trying to make a dating sim. They were trying to make an event.

One that today you just can’t experience. Very few people care about Nakayama Miho “like that” anymore, we aren’t seeing the commercials or the magazine ads or buying the discount unofficial strategy guide that invented a fake protagonist and never used Miho’s name because they didn’t have the rights. Today you play the game just because it is a game, and when you hit the phone numbers you tab over to a transcript of the voicemails…or maybe don’t even bother. The game was just a vessel for the hype. That doesn’t make the game good, by the way, I don’t want to go that far. The game was a not-very-good vessel for the hype, and an anachronistically better team could have made a better game. It isn’t really worth playing, in the end. But it is worth researching! As an event, it is really cool. As a piece of history, it is probably unique. And I respect the team behind it for that.

[reyaint]

the classes | mandatory

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date: march 23, 2025. 3:01 am. (starting). i fell asleep. lmao. 10:30

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✧˖*°࿐ The Mandatory Classes

𓂃༊veltrius Lumos Academy's mandatory curriculum blends rigorous academics with cultural and artistic exploration. these courses ensure students develop critical thinking, research skills, creativity, and problem-solving abilities, preparing them for higher education and global careers.

✧˖*°࿐ Language Studies

𓂃༊students are required to take Haiqinian, Greek, and English throughout their academic journey.

*ೃ༄Haiqinian Language and Composition (3 years, Pre-AP & AP Available)

𓂃༊ Year 1 (Pre-AP or Regular Haiqinian Language & Composition I):

✧ 𓂃 › grammar & sentence structure: verb conjugations, syntax, and advanced sentence formation.

✧ 𓂃 › composition: essay writing, formal letters, and literary analysis.

✧ 𓂃 › literary study: introduction to Haiqinian classical and modern literature.

𓂃༊ Year 2 (AP or Regular Haiqinian Language & Composition II):

✧ 𓂃 › advanced grammar & writing: rhetorical devices, argumentation, and structured compositions.

✧ 𓂃 › comparative literature: study of Haiqinian texts alongside global literature.

✧ 𓂃 › research & analysis: writing research papers and learning source evaluation.

𓂃༊ Year 3 (AP or Regular Haiqinian Literature & Composition III):

✧ 𓂃 › critical literary analysis: deep dive into Haiqinian poetry, novels, and plays.

✧ 𓂃 › creative writing & public speaking: writing short stories, poetry, and persuasive speeches.

✧ 𓂃 › capstone research paper: a long-form thesis-style paper analyzing a Haiqinian literary work.

*ೃ༄Greek Language & Literature (3 years, required for all students)

𓂃༊ Year 1 (Greek I – Basic Grammar & Conversation):

✧ 𓂃 › introduction to the greek alphabet & pronunciation.

✧ 𓂃 › basic sentence structure: verb forms, nouns, and adjectives.

✧ 𓂃 › conversational skills: daily interactions, greetings, and essential expressions.

𓂃༊ Year 2 (Greek II – Intermediate Grammar, Translation & History):

✧ 𓂃 › complex sentence structures: subjunctive, conditional, and imperative verb forms.

✧ 𓂃 › translation practice: excerpts from Homer, Aesop, and historical texts.

✧ 𓂃 › greek culture & history: myths, political systems, and philosophy.

𓂃༊ Year 3 (Greek III – Advanced Reading, Writing & Translation):

✧ 𓂃 › advanced text analysis: works of Plato, Sophocles, and Aristophanes.

✧ 𓂃 › academic writing & discussion: essays on Greek mythology, ethics, and politics.

✧ 𓂃 › capstone project: a final presentation translating and analyzing a classical Greek work.

*ೃ༄English Language & Composition (2 years, English III is an elective)

𓂃༊ Year 1 (English I – General English Skills, Literature & Creative Writing):

✧ 𓂃 › grammar & vocabulary: structure, syntax, and advanced composition skills.

✧ 𓂃 › literature study: analysis of classic and modern English literature.

✧ 𓂃 › creative writing: poetry, short stories, and personal narratives.

𓂃༊ Year 2 (English II – Critical Thinking & Analytical Writing):

✧ 𓂃 › advanced literature study: British and American literature from different eras.

✧ 𓂃 › essay writing & rhetoric: persuasive essays, literary analysis, and argument development.

✧ 𓂃 › public speaking: presentations, debates, and discussions on literary themes.

𓂃༊ Year 3 (English III – Elective, Optional for Advanced Study):

✧ 𓂃 › world literature focus: exploring literature from South America, Asia, and Europe.

✧ 𓂃 › research & thesis writing: students write and defend a long-form literary thesis.

✧ 𓂃 › experimental writing styles: creative non-fiction, stream-of-consciousness, and hybrid prose.

✧˖*°࿐ Mathematics (3 years, AP Available)

*ೃ༄Core Math Progression:

𓂃༊ Year 1 (Algebra I w/ Probability – Pre-AP or Regular):

✧ 𓂃 › linear & quadratic equations: graphing, inequalities, and polynomials.

✧ 𓂃 › probability & statistics: basic probability theory, combinatorics, and statistics.

✧ 𓂃 › real-world applications: business forecasting, data analysis, and logical reasoning.

𓂃༊ Year 2 (Algebra II w/ Statistics + Precalculus – AP or Regular):

✧ 𓂃 › advanced algebra concepts: exponential/logarithmic functions, matrices, and conic sections.

✧ 𓂃 › statistics & data science: regression analysis, probability distributions, and data visualization.

✧ 𓂃 › pre-calculus introduction: trigonometric functions, sequences, and limits.

𓂃༊ Year 3 (AP Calculus + Finance or Regular Finance):

✧ 𓂃 › differential & integral calculus: derivatives, integrals, and applications in physics/economics.

✧ 𓂃 › financial mathematics: investments, banking, risk analysis, and economic modeling.

✧ 𓂃 › capstone project: using calculus and finance principles to analyze a real-world financial trend.

✧˖*°࿐ History & Social Sciences (3 years, AP Available)

*ೃ༄Year 1 (AP or Regular Haiqin History):

𓂃༊ linear & quadratic equations: graphing, inequalities, and polynomials.

𓂃༊ probability & statistics: basic probability theory, combinatorics, and statistics.

𓂃༊ real-world applications: business forecasting, data analysis, and logical reasoning.

*ೃ༄Year 2 (AP or Regular World History):

𓂃༊ advanced algebra concepts: exponential/logarithmic functions, matrices, and conic sections.

𓂃༊ statistics & data science: regression analysis, probability distributions, and data visualization.

𓂃༊ pre-calculus introduction: trigonometric functions, sequences, and limits.

*ೃ༄Year 3 (AP or Regular Government & Economics):

𓂃༊ differential & integral calculus: derivatives, integrals, and applications in physics/economics.

𓂃༊ financial mathematics: investments, banking, risk analysis, and economic modeling.

𓂃༊ capstone project: using calculus and finance principles to analyze a real-world financial trend.

✧˖*°࿐ Science Studies (3 years, AP Available for Some Courses)

*ೃ༄Year 1 (AP or Regular Chemistry):

𓂃༊ atomic theory & molecular structure: periodic trends and chemical bonding.

𓂃༊ thermodynamics & reaction kinetics: understanding physical and chemical reactions.

𓂃༊ lab work: hands-on chemical experiments, titration, and organic synthesis.

*ೃ༄Year 2 & 3 (Choice of Science, Must Take at Least One More):

𓂃༊ environmental science: climate change, ecosystems, and sustainable development.

𓂃༊ forensics: DNA analysis, fingerprinting, toxicology, and forensic anthropology.

𓂃༊ anatomy & physiology: human body systems, genetics, and medical applications.

𓂃༊ physics: classical mechanics, electromagnetism, and astrophysics.

𓂃༊ marine biology: ocean ecosystems, marine conservation, and field research.

✧˖*°࿐ Specialized & Cultural Studies

*ೃ༄AP or Regular Myths & Legends:

𓂃༊ greek & roman mythology: The Iliad, The Odyssey, Aeneid.

𓂃༊ comparative mythology: Norse, Celtic, Japanese, and Mesopotamian myths.

𓂃༊ symbolism & influence: how mythology influences modern media and storytelling.

*ೃ༄Astrology I (AP or Regular):

𓂃༊ foundations of astrology: birth charts, planetary movements, zodiac signs.

𓂃༊ cultural perspectives: astrology in Greek, Chinese, and Vedic traditions.

𓂃༊ scientific & spiritual debate: skepticism vs. belief, practical applications.

[fiveredlights]

FIC MASTERLIST

completed

there’s glitter on the floor after the party — max/daniel. established relationship, relationship reveal. social media. (64.4 k)

Daniel returns to Red Bull and starts "soft launching" his relationship with Max across the 2023 - 2027 season.

↳ fic | rambles | asks | deleted scenes | commentary

please don’t ever become a stranger (whose laugh i could recognise anywhere) — max/daniel. getting together, non-linear narrative. (8.3k)

Prequel to glitter on the floor. Daniel invites Max to his farm during pandemic. Told partly through voicemails & texts.

↳ fic | rambles

fool me once — max/daniel. relationship reveal. social media implemented through work skins. (1k)

Daniel and Max have some news to share. It just happens to be April 1st.

↳ fic

takes one to know one — max/daniel. getting together, relationship reveal, singer!daniel. social media. (21.9k)

Daniel's a singer who goes semi-viral after a video of him reacting to his favourite driver's retirement blows up on social media. Max is the favourite driver. He has no idea who Daniel is, but he would love to find out.

↳ fic | rambles | extended author’s note

i'll never leave (never mind) — max/daniel. established relationship, hurt/comfort. post singapore gp (1.9k)

It was a bad idea in the end. to come back.

↳ fic | rambles

old habits die screaming — max/daniel. getting together, relationship reveal. social media implemented work skins. (52.9k)

Daniel comes back in 2028 as RB's team principal after retiring in 2023 due to his hand injury.

↳ fic | rambles | extended author’s note

hold onto the memories — max/daniel. glitter on the floor epilogue. social media.

An epilogue 20 years into the future focusing on Matilda's career to wrap up the glitter on the floor universe.

↳ fic | rambles

The Designated Alternate Universe Driver (DAUD) — max/daniel. getting together. alternate universe. parent trap-style shenanigans (16.4K)

Daniel doesn’t pass out when the 2025 DAUD is announced. Benjamin wishes he did. There was a whole bet going.

↳ fic | rambles | extended author's note

active work in progress

a habit to kick — matthew/callan.

In 2031, Callan is announced to be Mercedes' new driver, Matthew is decidedly not okay about it, Daniel just wants them to listen to him for once, and Max has given up on retiring in peace.

↳ rambles

click here for other works in progress/tumblr au's

[moonstone987]

Data Science Training in Kerala: Building Future-Ready Careers in a Data-Driven World

In the 21st century, data has emerged as the most valuable asset for businesses across the globe. Every click, swipe, purchase, and search generates a massive amount of information. Companies now depend on skilled professionals who can analyze, interpret, and derive actionable insights from data to maintain a competitive edge. This rising demand has made data science training in Kerala a popular and crucial stepping stone for individuals aiming to secure a thriving career in technology.

Kerala, renowned for its literacy and educational achievements, is rapidly becoming a hub for tech and data science learning. If you aspire to make a mark in this exciting field, understanding the landscape of data science and choosing the right training program is key to your success.

Why Data Science is the Career of the Future

1. Explosive Job Growth

As businesses continue to digitize, the volume of data being generated is growing exponentially. According to LinkedIn and other career platforms, data science roles are among the fastest-growing job categories worldwide. Data analysts, data engineers, and machine learning specialists are in high demand.

2. High Salaries and Career Progression

Data science professionals command some of the highest salaries in the technology sector. In India, an entry-level data scientist can earn anywhere between ₹6–10 LPA, and seasoned professionals easily cross the ₹25 LPA mark. Beyond salary, data science also offers clear paths for career advancement into leadership and strategic roles.

3. Applicability Across Industries

One of the best aspects of pursuing data science training in Kerala is the versatility it offers. Whether it's healthcare, finance, e-commerce, education, or logistics, data science skills are applicable across multiple industries.

Core Components of a Data Science Training Program

A comprehensive best data science training in Kerala should offer a well-rounded curriculum covering technical and soft skills. Here's what you should expect:

1. Mathematical and Statistical Foundations

Understanding the basics of statistics, probability, and linear algebra is crucial for data-driven decision-making and model-building.

2. Programming Skills

Python is the most popular language in data science. Training should cover:

Data manipulation with Pandas and NumPy

Data visualization using Matplotlib and Seaborn

Machine learning libraries like Scikit-Learn, TensorFlow, and PyTorch

3. Data Handling and Preprocessing

Cleaning and preparing data is a critical first step in any data science project. Knowledge of handling missing data, encoding categorical variables, and feature scaling should be emphasized.

4. Machine Learning Algorithms

From regression and classification models to clustering and dimensionality reduction techniques, a strong understanding of machine learning is non-negotiable for aspiring data scientists.

5. Big Data Technologies

Familiarity with big data ecosystems such as Hadoop, Spark, and cloud platforms like AWS, Azure, or Google Cloud can provide a significant advantage.

6. Real-World Projects

Theory is only valuable when applied. A quality training program includes live projects in areas like fraud detection, customer segmentation, recommendation systems, and predictive analytics.

7. Soft Skills and Business Acumen

Understanding the business context and being able to present data-driven insights effectively is as crucial as technical skills. Presentation, storytelling with data, and communication training are essential parts of good data science education.

Why Kerala is a Rising Hub for Data Science Education

Kerala is traditionally known for its commitment to education and innovation. With its increasing focus on IT parks, startups, and entrepreneurship, it is now becoming a promising destination for tech training as well.

IT Infrastructure Growth: Areas like Kochi (Infopark, SmartCity) and Trivandrum (Technopark) are rapidly expanding, hosting numerous tech companies.

Affordable Quality Education: Compared to other metropolitan cities, Kerala offers high-quality education at more reasonable costs.

Skilled Talent Pool: Kerala’s excellent universities and focus on STEM education mean students have a strong foundation to specialize further through courses like data science training in Kerala.

Vibrant Tech Community: Regular tech events, workshops, hackathons, and seminars help students and professionals network and stay updated on industry trends.

What to Look for When Choosing a Data Science Training Program?

When investing time and resources into data science institute in Kerala, consider the following factors:

Expert Trainers: Courses should be taught by experienced professionals, not just theorists.

Updated Curriculum: Data science is evolving rapidly; a good course must reflect current tools, trends, and technologies.

Project Work: Hands-on learning via real-world projects ensures you are job-ready upon completion.

Placement Support: Good institutes provide career guidance, resume reviews, and connections to hiring companies.

Flexible Learning Options: Depending on your schedule, options for online, hybrid, or weekend classes can be extremely beneficial.

Zoople Technologies: Leading the Future of Data Science Training in Kerala

When it comes to world-class data science training in Kerala, Zoople Technologies stands out as a leader. With a mission to bridge the gap between academic knowledge and industry demands, Zoople offers comprehensive and career-focused training that truly transforms learners into professionals.

Why Choose Zoople Technologies?

Industry-Expert Trainers: Learn from mentors who have rich experience in multinational corporations and cutting-edge tech projects.

Comprehensive Curriculum: Zoople’s program covers everything from foundational mathematics to advanced machine learning, AI, and big data technologies.

Project-Based Learning: Students work on live projects, gaining practical experience that employers value.

Strong Placement Assistance: With dedicated career support teams, Zoople helps students with mock interviews, resume preparation, and job placements.

Flexible and Modern Learning: Whether you prefer in-person, hybrid, or online classes, Zoople offers flexible formats tailored to your needs.

Vibrant Alumni Network: Connect with Zoople’s alumni community, many of whom now work with leading tech giants across India and abroad.

Choosing Zoople Technologies for your data science training in Kerala ensures that you are not just learning theoretical concepts but also gaining the practical, hands-on experience required to excel in a competitive job market.

Final Words

Data science is no longer just a buzzword; it is a career-defining opportunity for anyone passionate about technology, analytics, and innovation. As industries become increasingly data-driven, professionals with strong data science skills will be the ones leading change.

If you're serious about launching a successful career in this exciting field, enrolling in a trusted data science training in Kerala program is the first step—and Zoople Technologies is the ideal partner to guide you on that journey.

[xaltius]

Common Career Mistakes in Data Science and How to Avoid Them

The field of data science is booming, attracting bright minds eager to unravel insights from the ever-growing ocean of data. However, navigating this exciting career path isn't always smooth sailing. Like any profession, data science has its common pitfalls that can derail progress and hinder success. Whether you're a recent graduate or a seasoned professional transitioning into data science, being aware of these mistakes is the first step towards avoiding them.

1. Focusing Too Much on Tools, Not Enough on Fundamentals:

It's easy to get caught up in learning the latest libraries and frameworks (Python's Pandas, Scikit-learn, TensorFlow, etc.). While tool proficiency is important, neglecting the underlying mathematical and statistical foundations can limit your ability to understand algorithms, interpret results, and solve complex problems effectively.

How to Avoid It: Invest time in strengthening your understanding of linear algebra, calculus, probability, statistics, and machine learning principles. Tools change, but fundamentals remain constant.

2. Jumping Straight into Modeling Without Understanding the Data:

Rushing to build fancy models without thoroughly exploring and understanding the data is a recipe for disaster. This can lead to biased models, inaccurate insights, and ultimately, flawed decisions.

How to Avoid It: Dedicate significant time to exploratory data analysis (EDA). Visualize data, identify patterns, handle missing values, and understand the relationships between variables. This crucial step will inform your modeling choices and lead to more robust results.

3. Ignoring Data Quality:

"Garbage in, garbage out" is a fundamental truth in data science. Working with messy, incomplete, or inaccurate data will inevitably lead to unreliable outcomes.

How to Avoid It: Prioritize data cleaning and preprocessing. Develop skills in identifying and addressing data quality issues. Understand the sources of your data and implement strategies for ensuring its integrity.

4. Building Overly Complex Models:

While sophisticated models can be tempting, simpler models are often more interpretable and robust, especially with limited data. Overly complex models can overfit the training data and perform poorly on unseen data.

How to Avoid It: Start with simpler models and gradually increase complexity only if necessary. Focus on understanding the trade-off between bias and variance. Employ techniques like cross-validation to evaluate model performance on unseen data.

5. Poor Communication and Data Storytelling:

Technical expertise is only half the battle. Data scientists need to effectively communicate their findings to non-technical stakeholders. Failing to translate complex analyses into actionable insights can diminish the impact of your work.

How to Avoid It: Develop strong communication and data visualization skills. Learn to tell compelling stories with data, highlighting the business value of your insights. Practice presenting your work clearly and concisely.

6. Working in Isolation:

Data science is often a collaborative field. Siloed work can lead to missed opportunities, duplicated efforts, and a lack of diverse perspectives.

How to Avoid It: Actively seek collaboration with team members, domain experts, and other stakeholders. Share your work, ask for feedback, and be open to learning from others.

7. Neglecting Domain Knowledge:

Understanding the business context and the domain you're working in is crucial for framing problems effectively and interpreting results accurately.

How to Avoid It: Invest time in learning about the industry, the business processes, and the specific challenges you're trying to address. Collaborate with domain experts to gain valuable insights.

8. Focusing Solely on Accuracy Metrics:

While accuracy is important, it's not the only metric that matters. Depending on the problem, other metrics like precision, recall, F1-score, and AUC might be more relevant.

How to Avoid It: Understand the business implications of different types of errors and choose evaluation metrics that align with your objectives.

9. Not Staying Up-to-Date:

The field of data science is constantly evolving with new algorithms, tools, and best practices emerging regularly. Failing to keep up can quickly make your skills outdated.

How to Avoid It: Dedicate time to continuous learning. Follow industry blogs, attend conferences, take online courses, and engage with the data science community.

10. Underestimating the Importance of Deployment and Production:

Building a great model is only the first step. Getting it into production and ensuring its ongoing performance is critical for delivering business value.

How to Avoid It: Learn about the deployment process, monitoring techniques, and the challenges of maintaining models in a production environment.

Level Up Your Skills with Xaltius Academy's Data Science and AI Course:

While this blog focuses on data science pitfalls, a strong foundation in related areas can significantly enhance your ability to avoid many of them. Xaltius Academy's Data Science and AI Course provides a robust understanding of the core principles and techniques used in the field. This includes machine learning, deep learning, data visualization, and statistical analysis, all essential for becoming a well-rounded and impactful data professional.

Conclusion:

A career in data science offers immense potential, but navigating it successfully requires awareness and proactive effort. By understanding and avoiding these common mistakes, you can steer clear of potential roadblocks and build a fulfilling and impactful career in this exciting field. Remember to focus on fundamentals, understand your data, communicate effectively, and never stop learning.

[blogbyahad]

How can I grow from a data analyst to a data scientist?

1. Enhance Your Programming Skills

Learn Advanced Python/R: Gain proficiency in programming languages commonly used in data science, focusing on libraries for data manipulation (Pandas, NumPy) and machine learning (Scikit-learn, TensorFlow, PyTorch).

Practice Coding: Engage in coding challenges on platforms like LeetCode or HackerRank to strengthen your problem-solving skills.

2. Deepen Your Statistical Knowledge

Advanced Statistics: Familiarize yourself with concepts like hypothesis testing, regression analysis, and statistical significance. Understanding Bayesian statistics can also be beneficial.

Mathematics: Brush up on linear algebra and calculus, which are foundational for understanding algorithms in machine learning.

3. Learn Machine Learning

Practical Application: Work on projects where you apply machine learning algorithms to real-world datasets, focusing on both supervised and unsupervised learning.

4. Gain Experience with Big Data Technologies

Familiarize with Tools: Learn about tools and frameworks like Apache Spark, Hadoop, and databases (SQL and NoSQL) that are crucial for handling large datasets.

Cloud Services: Explore cloud platforms (AWS, Google Cloud, Azure) to understand how to deploy models and manage data storage.

5. Build a Portfolio

Real Projects: Work on projects that demonstrate your ability to analyze data, build models, and derive insights. Use platforms like GitHub to showcase your work.

Kaggle Competitions: Participate in Kaggle competitions to gain hands-on experience and learn from the community.

6. Network and Collaborate

Connect with Professionals: Attend meetups, webinars, and conferences to network with data scientists and learn about industry trends.

Seek Mentorship: Find a mentor who can guide you through your transition, offering advice and feedback on your progress.

7. Develop Soft Skills

Communication: Focus on improving your ability to communicate complex data findings to non-technical stakeholders. Consider practicing through presentations or writing reports.

Critical Thinking: Enhance your problem-solving and analytical thinking skills, as they are crucial for identifying and framing data science problems.

8. Stay Updated

Follow Trends: Keep up with the latest advancements in data science by reading blogs, listening to podcasts, and following key figures in the field on social media.

Continuous Learning: Data science is a rapidly evolving field. Engage in lifelong learning to stay relevant and informed about new tools and techniques.

9. Consider Advanced Education

Certificates or Degrees: Depending on your career goals, consider pursuing a master’s degree in data science or specialized certificates to deepen your knowledge and credentials.

[steroidelegal]

Career Advancement

In a international this is continuously converting, evolving, and turning into greater interconnected, the phrase "explore, analyze, Evolve" captures the essence of contemporary human undertaking. It speaks to a collective journey that transcends borders, cultures, and disciplines, bringing together diverse minds from throughout the globe to innovate and form the destiny. This idea not simplest highlights the electricity of collaboration however additionally underscores the importance of lifelong getting to know and growth in an an increasing number of complex global.

Exploration: The force for Discovery Exploration is the inspiration of human progress. From the earliest days of humanity, we have been pushed by means of curiosity to discover new lands, ideas, and possibilities. whether or not it’s coming across new clinical concepts, traversing unfamiliar terrain, or unlocking the mysteries of area, exploration is what drives us forward.

nowadays, exploration is not restricted to physical trips. It extends into the virtual world, wherein people can join and percentage understanding immediately. With the net and advanced communique technologies, all and sundry can discover new thoughts, cultures, and information with just a click. digital explorations of museums, libraries, and databases have democratized get right of entry to to statistics, allowing human beings in exceptional corners of the sector to percentage in collective discoveries.

in the context of "Uniting Minds throughout the Globe," exploration is a shared pursuit. humans from diverse backgrounds come together to analyze and discover in methods that have been unattainable only some decades ago. Collaborative exploration empowers us to solve global challenges, which includes climate exchange, health crises, and technological development.

getting to know: the important thing to growth Exploration obviously leads to mastering. As we find new facts and confront challenges, we grow both as individuals and as a international society. getting to know inside the 21st century is greater reachable than ever earlier than, thanks to the proliferation of on line courses, educational structures, and sources that span across borders.

In an interconnected international, learning is not confined to standard school rooms. learning takes vicinity in each interplay, in each shared piece of know-how, and in every alternate of thoughts. virtual structures permit people from various nations and backgrounds to get entry to international-magnificence education, fostering a more inclusive and informed worldwide community.

moreover, getting to know is now not a linear method. it's miles dynamic, non-stop, and ever-evolving. people are actually empowered to analyze new abilties at any degree of lifestyles, adapt to new environments, and live relevant in an more and more speedy-paced global. this is in particular essential in the era of technological disruption, in which new developments in artificial intelligence, robotics, and biotechnology continuously reshape industries.

Career Advancement

[erikabsworld]

Exploring the Future of Signal Processing: Integrating AI and ML

In the rapidly evolving landscape of signal processing, advancements are paving the way for a future where Artificial Intelligence (AI) and Machine Learning (ML) play pivotal roles. Recent developments in educational curricula highlight a significant shift towards incorporating these cutting-edge technologies. Traditionally, signal processing has relied on mathematical models and statistical methods to analyze and manipulate data. However, with the advent of AI and ML, there's a paradigm shift towards more adaptive and intelligent systems capable of handling complex, non-linear data patterns more efficiently than ever before.

Advancements in Signal Processing Education

The integration of AI and ML techniques within signal processing education signifies a progressive step forward. These technologies are instrumental in the development of sophisticated algorithms that can automate tasks, recognize patterns, and make decisions based on data inputs. For students pursuing signal processing courses, mastering these advanced techniques is becoming increasingly crucial. Educational programs are now focusing on bridging the gap between theory and practical application by incorporating AI and ML into their coursework. This approach not only enhances students' understanding of fundamental concepts but also equips them with skills that are highly sought after in today's job market.

Impact on Industry and Career Opportunities

The incorporation of AI and ML in signal processing has profound implications for various industries. From telecommunications to biomedical engineering, these technologies enable more accurate data analysis, enhanced predictive modeling, and real-time signal processing capabilities. As industries continue to adopt AI-powered solutions, professionals with a strong foundation in both signal processing and machine learning will be in high demand. Students who receive comprehensive training in these areas will have a competitive edge when entering the workforce.

How AI and ML Enhance Signal Processing

AI and ML techniques bring several advantages to signal processing:

Adaptive Algorithms: Algorithms can adapt and improve based on data feedback, enhancing accuracy over time.

Complex Data Handling: Capable of processing large volumes of complex, non-linear data efficiently.

Pattern Recognition: Automates the identification of patterns in signals, enabling faster decision-making.

Our Commitment to Students: Signal Processing Assignment Help Online

At matlabassignmentexperts.com, we understand the challenges students face in mastering signal processing concepts and applying AI and ML techniques effectively. Our team of experienced professionals is dedicated to providing the best signal processing assignment help online. Whether you need assistance with understanding theoretical concepts, implementing algorithms, or completing assignments, our experts are here to support you.

Conclusion

As signal processing continues to evolve with the integration of AI and ML, students have a unique opportunity to acquire skills that are not only relevant but essential in today's technological landscape. By embracing these advancements and leveraging resources like matlabassignmentexperts.com, students can effectively prepare themselves for promising careers in industries at the forefront of innovation. Stay informed, stay engaged, and explore the endless possibilities of signal processing in the age of AI and ML.

[vibinjack]

Tips For Career Transition To Data Science For Beginners

Introduction:

In the rapidly evolving landscape of modern industry, data science has emerged as a pivotal force driving decision-making, innovation, and efficiency. With the exponential growth of data generation, the demand for skilled professionals in data science continues to surge. For beginners aspiring to transition into this dynamic field, embarking on a career journey in data science can be exciting and daunting. However, navigating this transition with the right approach and guidance can lead to rewarding opportunities and professional fulfilment. In this article, we delve into essential tips tailored for beginners aiming to transition into data science, providing a roadmap for success.

Understanding The Landscape Of Data Science

Before delving into the specifics of transitioning into data science, it is crucial to develop a comprehensive understanding of the field's landscape. Data science encompasses a multifaceted discipline integrating statistics, computer science, domain expertise, and critical thinking to derive insights and solve complex problems. From machine learning and data visualisation to predictive analytics and artificial intelligence, data science offers various applications across various industries, including healthcare, finance, marketing, and technology. Are you interested in enrolling in Data Science Training In Chennai?

Embrace Lifelong Learning

One of the fundamental principles of thriving in data science is a commitment to lifelong learning. Given the rapid advancements in technology and methodologies, staying abreast of emerging trends and tools is paramount. As a beginner, investing time in building a strong foundation in key areas such as programming languages (e.g., Python, R), statistics, and machine learning algorithms lays the groundwork for success. Leverage online courses, tutorials, and interactive platforms such as Coursera, Udacity, and Kaggle to acquire new skills and deepen your understanding of data science concepts.

Build A Solid Foundation In Mathematics And Statistics

A profound understanding of mathematics and statistics lies at the heart of data science. These mathematical principles are the cornerstone for data analysis and interpretation, from probability theory and linear algebra to inferential statistics and hypothesis testing. Aspiring data scientists should devote time to mastering these foundational concepts, which form the basis for advanced techniques such as regression analysis, clustering, and classification. Online resources such as Khan Academy, MIT OpenCourseWare, and textbooks like "Introduction to Statistical Learning" can comprehensively cover these topics.

Gain Hands-On Experience Through Projects

While theoretical knowledge is indispensable, practical experience is equally crucial for aspiring data scientists. Hands-on projects reinforce theoretical concepts, hone problem-solving skills, and foster creativity. Start by tackling simple projects such as data cleaning and exploratory data analysis (EDA) using publicly available datasets. As you progress, undertake more complex projects that involve predictive modelling, natural language processing (NLP), or computer vision. Platforms like GitHub, Kaggle, and data science communities offer a wealth of resources and project ideas to kick-start your journey.

Cultivate A Strong Coding Proficiency

Proficiency in programming is a non-negotiable skill for data scientists. Python and R are two of the field's most widely used programming languages, offering robust libraries and frameworks for data manipulation, analysis, and visualisation. Familiarise yourself with these languages' syntax, data structures, and functions, and explore popular libraries such as NumPy, pandas, sci-kit-learn (Python), and tidyverse (R). Additionally, cultivate good coding practices such as documentation, modularization, and version control using tools like Git and GitHub.

Network And Engage With The Data Science Community

Building a strong professional network is invaluable for career growth and development in data science. Connect with fellow data enthusiasts, practitioners, and industry experts through online forums, social media platforms, and local meetups. Participate in discussions, share insights, and seek advice from experienced professionals to broaden your perspectives and stay informed about industry trends. Leveraging platforms like LinkedIn, Twitter, and data science communities such as Data Science Central and Towards Data Science can facilitate networking opportunities and foster mentorship relationships.

Develop Domain Expertise And Specialization

While technical skills are essential, domain expertise can set you apart as a data scientist. Specialising in a specific industry or domain, whether healthcare, finance, or e-commerce, enables you to understand domain-specific challenges, nuances, and opportunities. Immerse yourself in relevant literature, attend industry conferences, and engage with domain experts to gain insights into the unique requirements and applications of data science within your chosen field. Developing a niche specialisation enhances your value proposition and opens doors to exciting career opportunities.

Create A Compelling Portfolio And Resume

As you embark on your journey into data science, crafting a compelling portfolio and resume is paramount for showcasing your skills and experience to potential employers. Highlight your educational background, relevant coursework, technical skills, and hands-on projects clearly and concisely. Include detailed descriptions of projects, methodologies, and outcomes to showcase your problem-solving abilities, creativity, and impact. Consider creating a personal website or blog to demonstrate your passion for data science, share insights, and showcase your projects to a wider audience.

Pursue Continuous Growth And Adaptation

Data science is characterised by constant innovation and evolution, necessitating a mindset of continuous growth and adaptation. Embrace opportunities for professional development, whether it's attending workshops, obtaining certifications, or pursuing advanced degrees. Stay curious, experiment with new tools and techniques, and remain adaptable to changing industry dynamics. By embracing a growth mindset and remaining resilient in the face of challenges, you'll position yourself for long-term success and fulfilment in the dynamic field of data science. For those seeking the Best Software Training In Chennai, look no further than our comprehensive and industry-leading programs.

Conclusion

Embarking on a career transition into data science as a beginner is a challenging yet rewarding endeavour. Aspiring data scientists can navigate this transition successfully and unlock a world of opportunities by embracing lifelong learning, gaining hands-on experience, cultivating strong technical skills, and fostering a robust professional network. With dedication, perseverance, and a passion for problem-solving, beginners can embark on a fulfilling career journey in data science, driving innovation and making a meaningful impact in today's data-driven world.

[tenaciouspostfun]



Nimbus Blog

WHAT'S HAPPENIN' NEW YORK

a look inside the great white way by Broadway Bob

The Seven Year Disappear

2/26/2024

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​"The Seven Year Disappear" is a non linear, performance art play that deals with a mother, Miriam (Cynthia Nixon) and her son, business partner (Taylor Trensch). Nixon is a famous artist who has left her son Naphtali for seven years only to return leaving him confused. The illusion of Bi-Polar disorder surfaces as both mother and son struggle with mental disorders and substance abuse. Writer Jordan Seavey has created a body of work in which the audience decides what the play is really about... the journey here is the thing; and quite a journey it is!

 "Seven Year Disappear" is a modern play; it is not for the faint of heart as it deals with AIDS, Alcoholism, drug addiction and mental disorder. Serving the more progressive theater goer this performance play will resonate for the entire 90 minutes. The more traditional theater person may find this play difficult to keep up with. Scenes move in and out with no particular order, we see past relationships of both characters and the intimacy's that they experience. Director Scott Ellis let's us see the vulnerabilities in their lives and the rocky relationships that they share. Trying to make sense of it all, Ellis has the actors in peak form; we believe these people and the world that they live in. 

 What is most notable about this play is the deft acting in Nixon, changing roles, accents and body language, Nixon plays one of her best roles that I have seen in her stellar career. Trensch too is very good  throughout the show; both blend superbly together. The scenic design by Derek Mclane

and the lighting by Jeff Croiter is some of the best I have witnessed in a long time! On a black and white set, the purples that Croiter hits the stage with are breathtaking as it adds to the very cool setting. The feeling of a retro atmosphere permeates the stage. The last thing that is most noteworthy is the projection by John Narun. Much of the action is off stage if you will, we only see the faces of the actors and often in the back of the stage away from us. Narun keeps it suspenseful, somewhat erotic and perfectly captures the pulse of this play.

 The New Group has had two great plays so far this year; Sabbath's Theatre and now this one. Although not all the scenes are memorable and the play gets slow at times and the political commentary is unnecessary, it is a different kind of play that brings a different take to the audience.

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Author

ROBERT M. MASSIMI

is a resident drama critic for Metropolitan Magazine and other sources.  He has produced a dozen plays on Broadway, has worked as a film editor, and is also a member of the Dramatists Guild.  He is the acting director of  the SWM-NY division.

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[denimbex1986]

'At this point in Christopher Nolan's career, the director's name might as well be synonymous with the concept of ambiguity. In addition to making a number of mind-bending movies, all of Nolan's films bear the hallmark of the auteur's obsession with the power cinema holds over the fourth dimension (also known as "time" for those of you who haven't brushed up on your Einstein), the medium having the ability to extend or compress everything from a single moment to several lifetimes.

His wife and producer, Emma Thomas, points out in the official press kit for her husband's latest film, "Oppenheimer," that Nolan has "always been fascinated by subjectivity and objectivity," and that "Oppenheimer" is no exception. The film, a biopic of the infamous creator of the atomic bomb, J. Robert Oppenheimer (played by Cillian Murphy), is written by Nolan and based on Kai Bird and Martin J. Sherwin's 2005 biography entitled "American Prometheus." Most biopics involve a framing device in which the central character is invited to reflect back on their life, and while that device is in "Oppenheimer," it's not used in straightforward, linear fashion.

Throughout the film, Nolan presents scenes shot either in color or black and white. Instead of this disparity signaling a change of time period, the difference stands instead for scenes presented subjectively or objectively. Yet, this being a Nolan film, even the "objective" scenes can't be truly called such, as they're primarily from the point of view of a man who had huge issues with Oppenheimer, Lewis Strauss (Robert Downey Jr.). It's a quintessential bit of Nolan cheekiness that even the black-and-white scenes are not really in "black and white."

It's black, it's white, it's tough for Nolan to just be that

Of course, "Oppenheimer" is not the first instance of Nolan using black and white photography. His very first film, "Following," is fully shot in black and white, and the reason for this is partially due to budget: the movie was a fully independent production, and was shot on 16mm film over a series of Saturdays for a full year due to cast and crew needing to work full-time jobs during the week. The other reason is that, since "Following" is a neo-noir with a non-linear structure, this use of black and white is both a nod to noir tradition and a winking contrast to the film's ambiguity.

Nolan's follow-up feature, "Memento," continued the filmmaker's interests in neo-noir and non-linearity, as the film famously follows a backwards structure where each succeeding scene takes place prior to the one just viewed. However, that's only half the story; while Nolan shoots the backwards scenes in full color, there are scenes shot in black and white that progress in normal linear fashion, eventually revealed to be flashbacks to events prior to when the color scenes begin. Given that "Memento" is the story of a man who's been stricken with a rare memory defect that doesn't allow him to make new memories past a certain point in his life, this structure as a whole gives audiences a fully subjective experience, putting them in the character's place.

Or does it? Nolan also loves ambiguity, to the point where the commentary track he recorded for "Memento" features alternate endings wherein the director gives conflicting answers as to the mysteries within the movie. The black and white is thus revealed as being another layer of ambiguity beneath supposed clarity, a visual shorthand for truth or reality that is itself made suspect.

A tale of two hearings

Unlike the non-linear experiments of Nolan's early work, "Oppenheimer" is a little more straightforward, depicting events in the theoretical physicist's life from the 1930s to the 1950s in between two anchor points, both of which were hearings: Oppenheimer having his Atomic Energy Commission security clearance reviewed in 1954, and Strauss' confirmation to the Senate being considered in 1959. Although they're hearings, they hew much closer to being like trials, with each man cross-examined and having their true motives called into question.

Nolan recently explained the difference between the color and black and white to AP News:

"I knew that I had two timelines that we were running in the film. One is in color, and that's Oppenheimer's subjective experience. That's the bulk of the film. Then the other is a black and white timeline. It's a more objective view of his story from a different character's point of view."

That "different character" is of course Strauss, and the black and white scenes do indeed present Oppenheimer in a different light from the subjective color scenes: in color, Oppenheimer is a brilliant, ambitious, and troubled man, while in black and white, he seems far more arrogant, judgmental, and suspicious. In this way, the device allows Nolan to retain a good deal of ambiguity about his subject, not presenting the father of the atomic bomb as either a wholly misunderstood great guy nor a monstrous egotist.

Yet there's the added wrinkle of the black-and-white scenes not being wholly objective, either. While it presents a different Oppenheimer, it's an Oppenheimer viewed through the perspective of Strauss, a man who envied and resented Oppenheimer as well as suspected (or at least led others to suspect) him of being anti-American. It's Nolan once again using black and white as a subversion, presenting ambiguity in the guise of objectivity.

Fission + fusion = destruction

There's yet another layer to the way "Oppenheimer" uses its color and black and white scenes, which lies in the on-screen titles demarcating them and the way they comment on one of the movie's major themes. Right from the start of the film, Nolan labels the color and black and white scenes: "1. Fission" for color, and "2. Fusion" for black and white. This helps orient the audience with regard to the stylistic change and signals that there are bigger reasons for it.

At first, this distinction seems to be similar to Nolan's prior World War II-era feature, "Dunkirk," which used similar titles to distinguish between three separate timelines operating over different lengths. True to form, "Oppenheimer" isn't a strictly linear movie, jumping around between time periods at will as well as between color and black and white perspectives. This time around, however, Nolan isn't experimenting with structure as much as he is with point of view, utilizing the movie's timelines as fission and fusion, two disparate reactions that achieve similar results.

Those results are, of course, highly destructive and damaging, resulting in permanent change that cannot be reversed. It turns out that this is the fate most feared by Oppenheimer, not just in terms of potential nuclear destruction but in terms of his part in creating a world where that potential even exists. As Nolan depicts in the film, Oppenheimer is tortured by the possibility that testing the atom bomb could result in a chain reaction that sets the entire planet on fire, and even though it doesn't literally happen, Nolan posits the idea that maybe it did in another fashion. Due to a world in crisis, a nation looking to use that crisis to assert dominance, a populace brainwashed by militaristic propaganda and jingoism, one man's almost willfully blind obsession and another man's petty grievances, the threat of nuclear destruction arrived and is here to stay — what that means and how you feel about it is likely not so black and white.'

[yetanothersupernova]

Empathy

Design thinking is a reiterative and non-linear process (Stevens, 2023) that allows designers to problem-solve in creative ways. It typically consists of 5 stages; ‘empathise’, ‘define’, ‘ideate’, ‘prototype’, and ‘test’, with each phase targeting a specific area of the problem (Interactive Design Foundation, n.d.). This semester, we utilised the design thinking process to deconstruct and find a solution to a solitary wicked problem. Wicked problems are described as complex social issues, typically without a straightforward solution (Ritchey, 2011). We were given a short list of problems to choose from as a group, and we decided on exploring Hendra Virus.

The first several weeks of semester were primarily targeted towards introducing the class to collaborative learning. 

In Week 1, we participated in a silent exercise that assisted our understanding of the design process. Each student was required to illustrate the individual steps of making toast without the use of words or numbers. Once completed, the class split into two groups to reorder each image in silence. It was a simple task, but effective in introducing us to group contribution and separating design into individual stages. 

The second week consisted of randomised group allocation, which was solely based on our degrees, major/s, and skill sets. After the class was separated into groups, I was introduced to my own group members:

Lillian Mackaway

Robert Hall

Katherine Dowd

Astrid Goodley

After our group was assembled, we filled out a contract for collaboration to document each other’s contact details, as well as have an official document to ensure all members were productive and collaborative throughout the semester. Additionally, we set up Microsoft Teams to successfully communicate outside of class. Unfortunately, Katherine left the course in Week 3, and was ultimately replaced by Sean Pobie in Week 4.

During Week 3, the empathise phase of the design process began. We decided on the Hendra Virus WICKED problem, and made quick work of reframing its context to better understand the issue and who it impacts. Most of our initial efforts during class in Week 2 was put into researching hendra virus (HeV) to gain an understanding of where to progress.

Getting used to the group was particularly a challenge. With development in the early stages, most of us had limited input. Each of us had different learning styles, but we determined that open communication was the best way forward to ensure we’re all consistent with our understanding. 

REFERENCES:

Ritchey, T. (2011). Wicked Problems - Social Messes: Decision Support Modelling with Morphological Analysis. Springer Berlin Heidelberg.

Siang, T. (2009). What is Design Thinking? The Interaction Design Foundation; The Interaction Design Foundation. https://www.interaction-design.org/literature/topics/design-thinking

Stevens, E. (2023, April 19). The Key Principles and Steps of the Design Thinking Process. Career Foundry. https://careerfoundry.com/en/blog/ux-design/design-thinking-process/#:~:text=The%20Design%20Thinking%20process%20can%20be%20divided%20into%20five%20key,Ideate%2C%20Prototype%2C%20and%20Test.

[zenruption]

AI Is Here, and It Is Already Replacing Me

Well, at least, that’s the only assumption I can make. Everything correlates, but correlation doesn’t necessarily mean causality; I just can’t find another explanation.

Let’s start from the beginning.

I had done plenty of writing and marketing over my lifetime, but financial desperation during the pandemic pushed me to try copy and content writing through internet-based writing services. It turned out I am good at it. Ok, better than good; one of the elite.

Things started steamrolling fast, and I pronounced writing my full-time profession two months after I started and only continued to my career and earnings for the next 13 months.

Then Along Came AI

It has now been three weeks with close to zero work, which correlates very well with the release of ChatGPT 4.0. And I’m not alone. Other very proficient copy and content writers have no work whatsoever. We feared AI when it took off, like the USS Enterprise jumping to warp speed, but we all thought there would be a lot more time.

ChatGPT was released on November 30, 2022, and reached 100 million users within two months. No other internet application even comes close to that rapidity of growth. Facebook needed four and a half years to reach 100 million users. Instagram needed two and a half years, Twitter took five years, and even the sensation that is TikTok took nine months. 

Within the first month of ChatGPT, realtors were already praising it for its ability to write their listings, but overall it was pretty lousy. Its writing was bland and reflected that the system merely redistilled what it found online. We writers were left thinking, “Ok, so the content mills comprised of writers in India will be replaced, but we’re too damn good to replace for at least some time.” 

It wouldn’t replace those of us that get results, right?

AI Moves Faster Than We Can Comprehend

When one hears that AI is self-learning, that description is bound by our own experience. We think of learning something as a process that takes extended periods of time with subsequent gain after gain, but when AI learns, it isn’t linear or at a fixed rate. AI learns and grows exponentially, meaning it can learn anything in ever shorter periods of time.

Here’s an example: ChatGPT 3.5 passed two sections of the Bar Exam (and failed the multiple choice section) in January, achieving similar scores to average test takers. Chat GPT 4.0 passed the entire Bar Exam in the top 10% of test-takers in mid-March.

ChatGPT didn’t only develop an incredible legal ability in a couple of months but has shown the same progression and results on medical and business school exams. It will only continue to improve, and even its creators don’t know where it will be heading next.

The Behavioral Economics Question

As a huge fan of using economics to evaluate human behavior, I have to question how willing businesses are to trade off high-quality writing that speaks to their customers for lackluster free content. 

Unfortunately, writers see our services constantly devalued, with people that want excellence and highly educated writers for minimum wage rates. AI will better serve those people, but how will those that spend more for quality work weigh the quality/cost tradeoff? For now, it seems that cheap is winning. Where we go from here is questionable, but most freelancers can’t handle this lack of work for any time.

Many companies might eventually realize that using original, non-AI-generated writing gives them a competitive edge over others, but that remains to be seen.

Other Careers in Question

Where human creativity isn’t prized, AI might already impact those working as paralegals, 1st-year attorneys, and coders. Eventually, we can expect it to start replacing financial advisors, bookkeepers, truck drivers, receptionists, retail workers, and people monitoring and programming the AI itself.

The Timeline has Changed

Once, I believed the futurists were fairly correct, if not overly cautious. Google futurist Ray Kurzweil predicted that about 2050, AI resulting from incredible computing power would create daily innovations and disruptions to the point of imbuing humanity with near-god-like powers, including the possibility of never dying. All bets would be off the table.

David Levy, in his seminal work “Love and Sex With Robots,” predicted it would be in that same time frame that humans would be falling in love with and marrying humanoid robots.

Personally, I thought they were about 5-7 years behind, but we were all wrong.

While we all based predictions on computing power growth, we missed the advancement in how the code is written. Intermodal large language models work across what were once separate AI systems and learn at rates not previously imagined. All bets are off the table now.

What was predicted to happen around 2050 could happen by the early 2030s. Within a year, the changes could be seismic.

So, What Now

For now, I am still the far best choice for companies that need outstanding writing; whether they come to me is another story entirely. Strategizing and marketing myself is my daily regimen. I hope to find a full-time job immediately before a writer and his puppy have to hold up a sign at the intersection stating, “One of the first automated away. Will write for food.”

My greatest concern is the total lack of preparedness in our country. It's been apparent for years that this day would eventually come. Yet, we’ve been hamstrung with a political party more worried about morality laws and culture wars than feeding our own and planning for the future.

This doesn’t clear itself up, and it doesn’t create new jobs, only replaces them. This isn’t the 90’s tech boom.

Unless addressed now, AI tremendously exacerbates inequality worldwide and reverses the worldwide gains in alleviating poverty that has happened over decades. Our economics have to change, taxation has to become far more progressive, and laws need to be enacted to reign in a new corporate AI war that could quickly upend humanity.

Combined with the impacts of climate change we continue to fail to address, mankind is not on a good path, and we must act now.

Brian McKay is (maybe was) a professional writer, MBA, political scientist, the creator of a few silly things, and an overall decent dude. He urges you to promote progressive political candidates that take the future of our world seriously.’