#big data transformation tools

[innovativeroutinesinternational]

Mastering Data Transformation: Understanding Big Data Transformation Tools

In today's data-driven world, the ability to transform raw data into meaningful insights is paramount. This process, known as data transformation, is crucial for extracting value from vast amounts of information. Whether you're a data scientist, business analyst, or IT professional, understanding data transformation and the tools available is essential. In this blog, we'll delve into what data transformation entails, explore some of the leading big data transformation tools, and discuss their importance in modern analytics.

What is Data Transformation?

Data transformation involves converting data from one format or structure into another to prepare it for analysis, storage, or presentation. This process is fundamental as raw data often comes in disparate formats, lacks consistency, or requires aggregation before meaningful insights can be extracted. Key tasks in data transformation include:

Cleaning and Validation: Identifying and rectifying errors, inconsistencies, or missing values in the data.

Normalization: Ensuring data conforms to a standard format or structure.

Aggregation: Combining data from multiple sources into a single dataset for analysis.

Integration: Merging different datasets to create a comprehensive view.

Data transformation ensures that data is accurate, reliable, and ready for analysis, enabling organizations to make informed decisions based on trustworthy information.

Importance of Data Transformation

Effective data transformation is critical for several reasons:

Enhanced Data Quality: By cleaning and standardizing data, organizations can trust the accuracy of their analytics.

Improved Decision-Making: Transformed data provides insights that drive strategic decisions and operational improvements.

Operational Efficiency: Automation of transformation processes reduces manual effort and speeds up analysis.

Regulatory Compliance: Ensuring data meets regulatory requirements through proper transformation processes.

Big Data Transformation Tools

As data volumes continue to grow exponentially, traditional methods of data transformation struggle to keep pace. Big data transformation tools are designed to handle the complexities and scale of modern datasets efficiently. Let's explore some prominent tools in this space:

1. Apache Spark

Apache Spark is a powerful open-source framework for distributed data processing. It provides libraries for various tasks including SQL, machine learning, graph processing, and streaming. Spark's DataFrame API facilitates scalable data transformation operations such as filtering, aggregating, and joining datasets. Its in-memory processing capability makes it suitable for handling large-scale data transformation tasks with speed and efficiency.

2. Apache Hadoop

Apache Hadoop is another widely used framework for distributed storage and processing of large datasets. It includes components like HDFS (Hadoop Distributed File System) for storage and MapReduce for parallel processing of data. Hadoop ecosystem tools such as Apache Hive and Apache Pig enable data transformation tasks through high-level query languages (HiveQL and Pig Latin) that abstract complex processing tasks into simpler commands.

3. Talend

Talend is an open-source data integration platform that offers capabilities for data transformation, data integration, and data quality. It provides a graphical interface for designing data transformation workflows, making it accessible to users with varying technical backgrounds. Talend supports integration with various data sources and targets, including cloud-based solutions, making it a versatile choice for organizations looking to streamline their data transformation processes.

4. Informatica PowerCenter

Informatica PowerCenter is a leading enterprise data integration platform that includes robust data transformation capabilities. It supports both traditional on-premises and cloud-based data integration scenarios, offering features such as data profiling, cleansing, and transformation. PowerCenter's visual development environment allows developers to design complex data transformation workflows using a drag-and-drop interface, enhancing productivity and reducing time-to-insight.

5. Apache NiFi

Apache NiFi is an easy-to-use, powerful data integration and dataflow automation tool that excels in handling real-time data streams. It provides a visual interface for designing data pipelines and supports data transformation tasks through a variety of processors. NiFi's flow-based programming model allows for the creation of complex data transformation workflows with built-in support for scalability and fault tolerance.

Choosing the Right Tool

Selecting the right big data transformation tool depends on various factors such as:

Scalability: Ability to handle large volumes of data efficiently.

Ease of Use: Intuitive interfaces that streamline development and maintenance.

Integration Capabilities: Support for diverse data sources and destinations.

Performance: Processing speed and optimization for different types of transformations.

Organizations should evaluate their specific requirements and infrastructure considerations when choosing a tool that aligns with their data transformation needs.

Conclusion

In conclusion, data transformation is a cornerstone of modern analytics, enabling organizations to derive valuable insights from their data assets. Big data transformation tools play a crucial role in simplifying and scaling this process, allowing businesses to process large volumes of data efficiently and effectively. Whether leveraging Apache Spark's distributed computing power or Talend's intuitive interface, choosing the right tool is essential for maximizing the value of data transformation efforts. As data continues to grow in complexity and volume, investing in robust data transformation tools will be key to staying competitive in the digital era.

By mastering data transformation and harnessing the capabilities of big data transformation tools, organizations can unlock the full potential of their data assets and drive innovation across industries.

[jcmarchi]

AI in education: Balancing promises and pitfalls

New Post has been published on https://thedigitalinsider.com/ai-in-education-balancing-promises-and-pitfalls/

AI in education: Balancing promises and pitfalls

The role of AI in education is a controversial subject, bringing both exciting possibilities and serious challenges.

There’s a real push to bring AI into schools, and you can see why. The recent executive order on youth education from President Trump recognised that if future generations are going to do well in an increasingly automated world, they need to be ready.

“To ensure the United States remains a global leader in this technological revolution, we must provide our nation’s youth with opportunities to cultivate the skills and understanding necessary to use and create the next generation of AI technology,” President Trump declared.

So, what does AI actually look like in the classroom?

One of the biggest hopes for AI in education is making learning more personal. Imagine software that can figure out how individual students are doing, then adjust the pace and materials just for them. This could mean finally moving away from the old one-size-fits-all approach towards learning environments that adapt and offer help exactly where it’s needed.

The US executive order hints at this, wanting to improve results through things like “AI-based high-quality instructional resources” and “high-impact tutoring.”

And what about teachers? AI could be a huge help here too, potentially taking over tedious admin tasks like grading, freeing them up to actually teach. Plus, AI software might offer fresh ways to present information.

Getting kids familiar with AI early on could also take away some of the mystery around the technology. It might spark their “curiosity and creativity” and give them the foundation they need to become “active and responsible participants in the workforce of the future.”

The focus stretches to lifelong learning and getting people ready for the job market. On top of that, AI tools like text-to-speech or translation features can make learning much more accessible for students with disabilities, opening up educational environments for everyone.

Not all smooth sailing: The challenges ahead for AI in education

While the potential is huge, we need to be realistic about the significant hurdles and potential downsides.

First off, AI runs on student data – lots of it. That means we absolutely need strong rules and security to make sure this data is collected ethically, used correctly, and kept safe from breaches. Privacy is paramount here.

Then there’s the bias problem. If the data used to train AI reflects existing unfairness in society (and let’s be honest, it often does), the AI could end up repeating or even worsening those inequalities. Think biased assessments or unfair resource allocation. Careful testing and constant checks are crucial to catch and fix this.

We also can’t ignore the digital divide. If some students don’t have reliable internet, the right devices, or the necessary tech infrastructure at home or school, AI could widen the gap between the haves and have-nots. It’s vital that everyone gets fair access.

There’s also a risk that leaning too heavily on AI education tools might stop students from developing essential skills like critical thinking. We need to teach them how to use AI as a helpful tool, not a crutch they can’t function without.

Maybe the biggest piece of the puzzle, though, is making sure our teachers are ready. As the executive order rightly points out, “We must also invest in our educators and equip them with the tools and knowledge.”

This isn’t just about knowing which buttons to push; teachers need to understand how AI fits into teaching effectively and ethically. That requires solid professional development and ongoing support.

A recent GMB Union poll found that while about a fifth of UK schools are using AI now, the staff often aren’t getting the training they need:

View on Threads

Finding the right path forward

It’s going to take everyone – governments, schools, tech companies, and teachers – pulling together in order to ensure that AI plays a positive role in education.

We absolutely need clear policies and standards covering ethics, privacy, bias, and making sure AI is accessible to all students. We also need to keep investing in research to figure out the best ways to use AI in education and to build tools that are fair and effective.

And critically, we need a long-term commitment to teacher education to get educators comfortable and skilled with these changes. Part of this is building broad AI literacy, making sure all students get a basic understanding of this technology and how it impacts society.

AI could be a positive force in education – making it more personalised, efficient, and focused on the skills students actually need. But turning that potential into reality means carefully navigating those tricky ethical, practical, and teaching challenges head-on.

See also: How does AI judge? Anthropic studies the values of Claude

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[ellipsus-writes]

Write anything in Ellipsus.

The internet’s a little dystopian right now, and writers (rightfully) have concerns about how their writing might be monitored, scraped, or censored by the tools they use.

That’s why we’ve clarified our privacy policy and terms of service—so you know exactly where we stand on privacy, creative freedom, and writer-first (and pro-transformative works ❤️) policies.

Nothing’s materially changed, but the language is clearer. Here’s the short version:

Your data is yours. We won’t access it, sell it, or misuse it. Ever.

Write what you want. We’re a writing tool, not a gatekeeper. We don’t host, don’t police, and we strongly believe in creative freedom.

You’ll always be in the loop. If anything big changes or we need to adapt to better safeguard and serve our writers, you’ll hear from us first.

We want Ellipsus to be a safe, confident home for your work—no matter what you’re writing.

Want to hear about the latest updates as they happen? Join our Discord to follow announcements and share your feedback.

- the Ellipsus Team XO

[revelboo]

oh shockwave's snarl and antenna flat back in silent aggresiveness, hes so traumatized lmaoo

He wants nothing to do with this nonsense, even if he might be a tiny bit interested which is making him even angrier about it

Everything Is Alright Pt 95

IDW Starscream x Reader, Soundwave x Reader, Megatron x Reader

• “What is he doing here?” Voice tight with anxiety, Starscream cups his other hand protectively around you as he glares at Shockwave. Aware of Megatron stiffening at being questioned. Of Soundwave standing far too close to him, right behind him. A trap? Of course it is, he’s surrounded by enemies here. Wings flaring out, he tries to decide if there’s enough clearance over Soundwave’s head. If he can transform around you and just bolt. If he can get to Skywarp, he can ask to be warped far away. Can he trust his brother, though? Can he trust anyone except himself?

• Optics narrowing as Starscream tenses, Megatron flicks his servos at Shockwave. “I can’t trust Hook to not be… influenced at this point, but Shockwave is merely here to run some non-intrusive scans on the human,” he says, unable to make himself say bond mate. Not about a weak, little organic. Aware of how irritated the scientist is, his antenna back as he grumbles softly. “Soundwave, bring the human,” he says. Watching his communications officer come around the agitated Seeker and offer his cupped hands. Feeling strangely unsettled as you touch the Seeker’s hand, whispering something too soft to catch. The look of almost panic on Starscream’s face instead of his usual arrogant sneer as he moves his hand to let you go to Soundwave. Hooking a servo around your middle like he doesn’t want to release you, before his wings droop and he lets you go. Remembering against his will the sounds you’d made under Starscream as he rutted against you. Soft skin bathed in the light of a spark, so illicit.

• Cradling you close, Soundwave approaches Megatron only to have the warlord point at a console as if not wanting to touch you again. Giving in to impulse, he lifts you up to rub his masked cheek against your face and shoulder. Feeling you lay a hand on him as Megatron watches with a frown. But if the warlord still thinks of him as a friend at all, he needs to reinforce that you matter to him to keep you safe. And he wants to touch you, reassure you that you’re not being abandoned. That this is okay even if he’s not sure it really is. Reluctantly lowering you, his servos linger against you before he pulls away. Feeling your eyes on him and Starscream. All the things he can’t say out loud in front of Megatron and Shockwave hurting him.

• Wrapping your arms around yourself, you watch them go, catching Star’s optic when Soundwave tries to touch his shoulder and the Seeker bares his denta. And you offer him a smile, trying to pretend you’re not terrified, because it’s all out now. No more secrets. Skin prickling as the big, purple cyclops makes a noise and picks up a tool, you tense. There’s nowhere to run to. Flinching when he powers the thing up and harmless light plays over you from head to toe. When that single optic narrows and he makes a circling gesture with his cannon and momentarily gets distracted staring at it, you obediently turn so he can scan your back.

• “Well?” Megatron demands as Shockwave studies the screen. From the corner of his optic, he sees you turn back toward them, arms around yourself. “What is it doing to them?” Because there has to be a sane reason for this. For why two perfectly reasonable- well, one perfectly reasonable mech and Starscream are so obsessed with a human to the point of bonding it. “No signs of its pheromones being able to influence their reasoning,” Shockwave mutters, scrolling through the data. “No psychic outlier abilities. Elevated levels of nanites present suggest numerous couplings. Organic is entirely unremarkable otherwise.”

• Numerous couplings. He’d basically implied you’re a sex fiend. Can the ground just open up and swallow you whole at this point? And entirely unremarkable. Yep. Mortified and offended, you stare at them. “You thought I was mind controlling them?” And then the warlord is frowning at you. Because apparently he really just wanted you to be manipulating them, not wanting to believe that they like you. Care for you.

• “Perhaps a more tactile exploration to reach conclusive results?” Shockwave suggests and you stumble back, eyes widening in fear. And for some reason that bothers Megatron. That fear. “That won’t be necessary,” Megatron says, watching Shockwave glare down at you before walking out. Leaving him with you. Those wide eyes stare up at him as he walks over to retrieve you, feeling your little heart racing when he picks you up and carries you to set on the arm of his throne. Sitting down, he studies you. “You washed off the scent of the Seeker before coming to me?” When you redden and nod, he laughs softly. “Good. You won’t come to me scenting of him.” So it’s not pheromones or some strange ability corrupting his forces. Just the interfacing itself which is almost as bad. No, it’s actually worse. Optics sliding to you, he remembers the glimpse he’d gotten of your back arched, Starscream’s hips moving urgently against you. The wet sound of it. Venting, he presses a servo against his aching head. “How exactly did the Seeker find you?”

Previous

Next

[kyova]

From Alt National Park Service in n FB:

tl;dr:

DOGE accessed all those systems - IRS, Social Security, DHS, every office in the U.S. - not to promote “efficiency”, but to gather and control our electronic lives so they can ruin us if we step out of line in any way. Or if they just feel like it.

Alt National Park Service:

“DOGE has quietly transformed into something far more sinister — not a system for streamlining government, but one designed for surveillance, control, and targeting. And no one’s talking about it. So we’re going to spill the tea.

From the beginning, DOGE’s true mission has been about data — collecting massive amounts of personal information on Americans. Now, that data is being turned against immigrants.

At the center of this effort is Antonio Gracias, a longtime Elon Musk confidante. Though he holds no official government position, Gracias is leading a specialized DOGE task force focused on immigration. His team has embedded engineers and staff across nearly every corner of the Department of Homeland Security (DHS).

But it doesn’t stop there.

DOGE operatives have also been quietly placed inside other federal agencies like the Social Security Administration and the Department of Health and Human Services — agencies that store some of the most sensitive personal data in the country, including on immigrants.

DOGE engineers now working inside DHS include Kyle Schutt, Edward Coristine (nicknamed “Big Balls”), Mark Elez, Aram Moghaddassi, and Payton Rehling. They’ve built the technical foundation behind a sweeping plan to revoke, cancel visas, and rewire the entire asylum process.

One of the most disturbing aspects of this plan? Flagging immigrants as “deceased” in the Social Security system — effectively canceling their SSNs. Without a valid Social Security number, it becomes nearly impossible to open a bank account, get a job, or even apply for a loan. The goal? Make life so difficult that people “self-deport.”

And if you’re marked as dead in the Social Security system, good luck fixing it. There’s virtually no path back — it’s a bureaucratic black hole.

You might ask: why do immigrants, asylum seekers, or refugees even have Social Security numbers? Because anyone authorized to work in the U.S. legally is issued one. It’s not just for citizens. It’s essential for participating in modern life — jobs, housing, banking, taxes. Without it, you’re locked out of society.

Last week, this plan was finalized in a high-level White House meeting that included DHS Secretary Kristi Noem, Antonio Gracias, senior DOGE operatives, and top administration officials.

In recent weeks, the administration has moved aggressively to strip legal protections from hundreds of thousands of immigrants and international students — many of whom have been living and working in the U.S. legally for years.

At the core of this crackdown? Data.

DOGE has access to your SSN, your income, your political donations — and more. What was once sold as a tool for “government efficiency” has become something else entirely: a weaponized surveillance machine.

And if you think this ends with immigrants, think again.

Antonio Gracias has already used DOGE’s access to Social Security and state-level data to push voter fraud narratives during past elections. The system is in place. The precedent has been set. And average Americans should be concerned.”

[mariacallous]

On Saturday, an Associated Press investigation revealed that OpenAI's Whisper transcription tool creates fabricated text in medical and business settings despite warnings against such use. The AP interviewed more than 12 software engineers, developers, and researchers who found the model regularly invents text that speakers never said, a phenomenon often called a “confabulation” or “hallucination” in the AI field.

Upon its release in 2022, OpenAI claimed that Whisper approached “human level robustness” in audio transcription accuracy. However, a University of Michigan researcher told the AP that Whisper created false text in 80 percent of public meeting transcripts examined. Another developer, unnamed in the AP report, claimed to have found invented content in almost all of his 26,000 test transcriptions.

The fabrications pose particular risks in health care settings. Despite OpenAI’s warnings against using Whisper for “high-risk domains,” over 30,000 medical workers now use Whisper-based tools to transcribe patient visits, according to the AP report. The Mankato Clinic in Minnesota and Children’s Hospital Los Angeles are among 40 health systems using a Whisper-powered AI copilot service from medical tech company Nabla that is fine-tuned on medical terminology.

Nabla acknowledges that Whisper can confabulate, but it also reportedly erases original audio recordings “for data safety reasons.” This could cause additional issues, since doctors cannot verify accuracy against the source material. And deaf patients may be highly impacted by mistaken transcripts since they would have no way to know if medical transcript audio is accurate or not.

The potential problems with Whisper extend beyond health care. Researchers from Cornell University and the University of Virginia studied thousands of audio samples and found Whisper adding nonexistent violent content and racial commentary to neutral speech. They found that 1 percent of samples included “entire hallucinated phrases or sentences which did not exist in any form in the underlying audio” and that 38 percent of those included “explicit harms such as perpetuating violence, making up inaccurate associations, or implying false authority.”

In one case from the study cited by AP, when a speaker described “two other girls and one lady,” Whisper added fictional text specifying that they “were Black.” In another, the audio said, “He, the boy, was going to, I’m not sure exactly, take the umbrella.” Whisper transcribed it to, “He took a big piece of a cross, a teeny, small piece … I’m sure he didn’t have a terror knife so he killed a number of people.”

An OpenAI spokesperson told the AP that the company appreciates the researchers’ findings and that it actively studies how to reduce fabrications and incorporates feedback in updates to the model.

Why Whisper Confabulates

The key to Whisper’s unsuitability in high-risk domains comes from its propensity to sometimes confabulate, or plausibly make up, inaccurate outputs. The AP report says, "Researchers aren’t certain why Whisper and similar tools hallucinate," but that isn't true. We know exactly why Transformer-based AI models like Whisper behave this way.

Whisper is based on technology that is designed to predict the next most likely token (chunk of data) that should appear after a sequence of tokens provided by a user. In the case of ChatGPT, the input tokens come in the form of a text prompt. In the case of Whisper, the input is tokenized audio data.

The transcription output from Whisper is a prediction of what is most likely, not what is most accurate. Accuracy in Transformer-based outputs is typically proportional to the presence of relevant accurate data in the training dataset, but it is never guaranteed. If there is ever a case where there isn't enough contextual information in its neural network for Whisper to make an accurate prediction about how to transcribe a particular segment of audio, the model will fall back on what it “knows” about the relationships between sounds and words it has learned from its training data.

According to OpenAI in 2022, Whisper learned those statistical relationships from “680,000 hours of multilingual and multitask supervised data collected from the web.” But we now know a little more about the source. Given Whisper's well-known tendency to produce certain outputs like "thank you for watching," "like and subscribe," or "drop a comment in the section below" when provided silent or garbled inputs, it's likely that OpenAI trained Whisper on thousands of hours of captioned audio scraped from YouTube videos. (The researchers needed audio paired with existing captions to train the model.)

There's also a phenomenon called “overfitting” in AI models where information (in this case, text found in audio transcriptions) encountered more frequently in the training data is more likely to be reproduced in an output. In cases where Whisper encounters poor-quality audio in medical notes, the AI model will produce what its neural network predicts is the most likely output, even if it is incorrect. And the most likely output for any given YouTube video, since so many people say it, is “thanks for watching.”

In other cases, Whisper seems to draw on the context of the conversation to fill in what should come next, which can lead to problems because its training data could include racist commentary or inaccurate medical information. For example, if many examples of training data featured speakers saying the phrase “crimes by Black criminals,” when Whisper encounters a “crimes by [garbled audio] criminals” audio sample, it will be more likely to fill in the transcription with “Black."

In the original Whisper model card, OpenAI researchers wrote about this very phenomenon: "Because the models are trained in a weakly supervised manner using large-scale noisy data, the predictions may include texts that are not actually spoken in the audio input (i.e. hallucination). We hypothesize that this happens because, given their general knowledge of language, the models combine trying to predict the next word in audio with trying to transcribe the audio itself."

So in that sense, Whisper "knows" something about the content of what is being said and keeps track of the context of the conversation, which can lead to issues like the one where Whisper identified two women as being Black even though that information was not contained in the original audio. Theoretically, this erroneous scenario could be reduced by using a second AI model trained to pick out areas of confusing audio where the Whisper model is likely to confabulate and flag the transcript in that location, so a human could manually check those instances for accuracy later.

Clearly, OpenAI's advice not to use Whisper in high-risk domains, such as critical medical records, was a good one. But health care companies are constantly driven by a need to decrease costs by using seemingly "good enough" AI tools—as we've seen with Epic Systems using GPT-4 for medical records and UnitedHealth using a flawed AI model for insurance decisions. It's entirely possible that people are already suffering negative outcomes due to AI mistakes, and fixing them will likely involve some sort of regulation and certification of AI tools used in the medical field.

[greenwitchcrafts]

2025 update:

February 2024 witch guide

Full moon: February 24th

New moon: February 9th

Sabbats: Imbolc-February 1st

February Snow Moon

Known as: Eagle Moon, Horning Moon, Solmonath Moon, Bear moon, Ice Moon, Wild Moon, Raccoon Moon, Big Winter Moon, Groundhog Moon, Quickening Moon, Storm Moon, Goose Moon, Hungry Moon & Red/Cleansing Moon

Element: Fire

Zodiac: Aquarius & Pisces

Nature spirits: House Faeries

Deities: Aphrodite, Brigid & Nut

Animals: Otter & Unicorn

Birds: Chickadee & Eagle

Trees: Cedar, laurel, myrtle & rowan

Herbs: Balm of Gilead, hyssop, myrrh, sage & spikenard

Flowers: Primrose

Scents: Heliotrope & wisteria

Stones: Amethyst, jasper, moonstone, obsidian, onyx , rose quartz, topaz & red zircon

Colors: Light blue & violet

Energy:  Astral travel, banishing, beginnings, breaking bad habits, creativity expressiveness, empowerment, energy working to the surface, fertility, forgiveness, freedom, friendships, future plans, growth, healing, problem solving, purification, responsibility & science

February’s full Moon is a “Micromoon” this year. Think of this term as the opposite of a “Supermoon.” It simply means that the full Moon is at its farthest point from Earth (not the nearest point).

The explanation behind February’s full Moon name is a fairly straightforward one: it’s known as the Snow Moon due to the typically heavy snowfall that occurs in February. On average, February is the United States’ snowiest month, according to data from the National Weather Service. In the 1760s, Captain Jonathan Carver, who had visited with the Naudowessie(Dakota), wrote that the name used for this period was the Snow Moon, “because more snow commonly falls during this month than any other in the winter.” 

Imbolc

Known as: Feast of Torches, Feast of Waxing Light, Oimele & Brigid's Day

Season: Winter

Symbols: Besoms, Brighid's crosses, candles, candle wheels, fertility symbols, fire, ploughs, priapic wands & white flowers

Colors: Black, brown, Earth tones, lavender, light green, orange, pink, red, white & yellow

Oils/Incense: Apricot, basil, bay, carnation, chamomile, cinnamon, dragon's blood, frankincense, heather, jasmine, myrrh, neroli, red sandalwood, sage, vanilla, violet & wisteria

Animals: Badger, cow, deer,groudhog, robin, sheep, snake, & swan

Mythical: Dragon

Stones: Amethyst, bloodstone, citrine, clear quartz, garnet, green tourmaline, hematite, iron, lodestone, onyx, red zircon, rose quartz, ruby, turquoise, yellow tourmaline

Food: Breads, chives, curries, dairy products, grains, garlic, herbal teas, honey cakes, lamb, muffins, onions, peppers, poppy seed cakes, pork, poultry, pumpkin seeds, raisins, scones, spiced wines & sunflower seeeds

Herbs/Plants: Angelica, ashleaf, balsam, basil, bay laurel, benzoin, blackberry, clover, coltsfoot, coriander, dragon's blood, garlic, heather, lemon, myrrh, rosemary, sage, vervain, wheat & witch hazel

Flowers: Celandine, chamomile, iris, rose hips, snowdrop, sunflower, tansy, violets, white flowers & yellow flowers

Goddesses: Anu, Aradia, Arianrhod, Artio, Athena, Branwen, Brigid, Danu, Februa, Gaia, Inanna, Juno, Selene, Sirona & Vesta

Gods: Aegus Mac Og, Bragi, Cupid, Dian Cecht, Dumuzi, Eros, Februus & Pax

Issues, Intentions & Powers: Activation/awakening, animals, beginnings, fertility, healing, hope, illumination, inspiration, light, pregnancy/childbirth, prophecy, transformation, well-being & youth

Spellwork: Air magick, banishings, candle spells, divination, fertility spells, prosperity & purification

Activities:

• Make & light white candles

• Clean/decorate your altar & consecrate your  altar tools

• Go on a walk in nature & look for signs of spring

• Make a Brigid's Cross

• Have a feast with your family/friends

• Give thanks & leave offerings to the Earth

• Set intentions, reflect & look deeper into your goals for spring

• Start a bonfire

• Find Imboloc prayers & devotionals that bid farewell to the winter months, honor the goddess Brigid, as well as seasonal blessings for your meals, hearth, & home.

• Pepare plans for your upcoming garden

• Craft a priapic wand

• Spend time with children celebrating Imbolc by making crafts & or baking

• Practice divination & fire scrying

• Draw a cleansing ritual bath for yourself

• Meditate, reflect & say your farewells to winter

• Cleanse & clean your house to prepare for spring

• Create a Brídeóg: a doll of Brigid made of straw

• Make Bride's bouquet satchets & exchange as symbols of good luck and fertility

• Set aside food & or drinks as an offering to Brigid to invite her in your home

Imbolc is a Gaelic festival marking the beginning of spring. Most commonly it is held on January 31 – February 1, or halfway between the winter solstice & the spring equinox. The holiday is a festival of the hearth, home, a celebration of the lengthening days & the early signs of spring. 

The word "imbolc" means "in the belly" and refers to the pregnancy of ewes at this time of year. The term "oimelc" means ewe's milk. Around this time of year, many herd animals give birth to their first offspring of the year or are heavily pregnant & as a result, they are producing milk. This creation of life’s milk is a part of the symbolic hope for spring.

Imbolc is mentioned in some of the earliest Irish literature and it is associated with important events in Irish mythology. It has been suggested that it was originally a pagan festival associated with the goddess Brigid and that it was Christianized as a festival of Saint Brigid, who herself is thought to be a Christianization of the goddess.

Some use Imbolc to celebrate the longer days which herald the return of Spring & The Goddess's recovery from giving birth to The Sun (The God) at Yule. The God & The Goddess are children symbolizing new life, new beginnings & new resurrections.

Related festivals:

• Groundhog Day-  Is a tradition observed in the United States & Canada on February 2 of every year. It derives from the Pennsylvania Dutch superstition that if a groundhog emerges from its burrow on this day & sees its shadow, it will retreat to its den & winter will go on for six more weeks; if it does not see its shadow, spring will arrive early.

While the tradition remains popular in the 21st century, studies have found no consistent association between a groundhog seeing its shadow & the subsequent arrival time of spring-like weather.

•St. Brigid's Day- 1 February. It was originally Imbolc, the first day of spring in Irish tradition. Because Saint Brigid has been theorised as linked to the goddess Brigid, some associate the festival of Imbolc with the goddess. St. Brigid is the patroness saint (or 'mother saint') of Ireland. She is patroness of many things, including poetry, learning, healing, protection, blacksmithing, livestock & dairy production. In her honour, a perpetual fire was kept burning at Kildare for centuries.

A recent campaign successfully established her feast day as a national holiday in 2023.

• Chinese New Year- (February 10th) the festival that celebrates the beginning of a new year on the traditional lunisolar Chinese calendar. In Chinese, the festival is commonly referred to as the Spring Festival,- marking the end of winter and the beginning of the spring season. Observances traditionally take place from Chinese New Year's Eve, the evening preceding the first day of the year, to the Lantern Festival, held on the 15th day of the year. The first day of Chinese New Year begins on the new moon that appears between January 21st & February 20th.

The Chinese New Year is associated with several myths and customs. The festival was traditionally a time to honour deities as well as ancestors. Within China, regional customs and traditions concerning the celebration of the New Year vary widely & the evening preceding the New Year's Day is frequently regarded as an occasion for Chinese families to gather for the annual reunion dinner.

It is also a tradition for every family to thoroughly clean their house, in order to sweep away any ill fortune & to make way for incoming good luck. Another custom is the decoration of windows & doors with red paper-cuts and couplets. Popular themes among these paper-cuts and couplets include good fortune or happiness, wealth & longevity. Other activities include lighting firecrackers  & giving money in red envelopes.

•  Candlemas- is a Christian feast day on February 2nd commemorating the presentation of Jesus at the Temple. It is based upon the account of the presentation of Jesus in Luke 2:22-40. 

While it is customary for Christians in some countries to remove their Christmas decorations on Twelfth Night, those in other Christian countries historically remove them after Candlemas.On Candlemas, many Christians also take their candles to their local church, where they are blessed and then used for the rest of the year.

•Setsubun- (February 3rd) Is the day before the beginning of spring in the old calendar in Japan. The name literally means 'seasonal division', referring to the day just before the first day of spring.

Both Setsubun & Risshun are celebrated yearly as part of the Spring Festival (Haru matsuri ) in Japan. In its association with the Lunar New Year, Setsubun, though not the official New Year, was thought of as similar in its ritual & cultural associations of 'cleansing' the previous year as the beginning of the new season of spring. Setsubun was accompanied by a number of rituals & traditions held at various levels to drive away the previous year's bad fortunes & evil spirits for the year to come.

Other Celebrations:

• Lupercalia-

In ancient Rome, this festival was conducted annually on February 13th through 15th under the superintendence of a corporation of priests called Luperci. The origins of the festival are obscure, although the likely derivation of its name from lupus (Latin: “wolf”) has variously suggested connection with an ancient deity who protected herds from wolves and with the legendary she-wolf who nursed Romulus and Remus. As a fertility rite, the festival is also associated with the god Faunus.

to purify the city, promoting health & fertility.

Each Lupercalia began with the sacrifice by the Luperci of goats and a dog, after which two of the Luperci were led to the altar, their foreheads were touched with a bloody knife & the blood was wiped off with wool dipped in milk; the ritual required that the two young men laugh. The sacrificial feast followed, after which the Luperci cut thongs from the skins of the sacrificial animals & ran in two bands around the Palatine hill, striking with the thongs at any woman who came near them. A blow from the thong was supposed to render a woman fertile.

In 494 CE the Christian church under Pope Gelasius I forbade participation in the festival. Tradition holds that he appropriated the form of the rite as the Feast of the Purification (Candlemas), celebrated on February 2, but it is likely that the Christian feast was established in the previous century. It has also been alternately suggested that Pope Gelasius I replaced Lupercalia with St. Valentine’s Day, celebrated on February 14th, but the origin of that holiday was likely much later.

Sources:

Farmersalmanac .com

Llewellyn's Complete Book of Correspondences by Sandra Kines

Wikipedia

A Witch's Book of Correspondences by Viktorija Briggs

Encyclopedia britannica

Llewellyn 2024 magical almanac Practical magic for everyday living

[trooper1023]

From Alt Park National Service on FB:

DOGE has quietly transformed into something far more sinister — not a system for streamlining government, but one designed for surveillance, control, and targeting. And no one’s talking about it. So we’re going to spill the tea.

From the beginning, DOGE’s true mission has been about data — collecting massive amounts of personal information on Americans. Now, that data is being turned against immigrants.

At the center of this effort is Antonio Gracias, a longtime Elon Musk confidante. Though he holds no official government position, Gracias is leading a specialized DOGE task force focused on immigration. His team has embedded engineers and staff across nearly every corner of the Department of Homeland Security (DHS).

But it doesn’t stop there.

DOGE operatives have also been quietly placed inside other federal agencies like the Social Security Administration and the Department of Health and Human Services — agencies that store some of the most sensitive personal data in the country, including on immigrants.

DOGE engineers now working inside DHS include Kyle Schutt, Edward Coristine (nicknamed “Big Balls”), Mark Elez, Aram Moghaddassi, and Payton Rehling. They’ve built the technical foundation behind a sweeping plan to revoke, cancel visas, and rewire the entire asylum process.

One of the most disturbing aspects of this plan? Flagging immigrants as “deceased” in the Social Security system — effectively canceling their SSNs. Without a valid Social Security number, it becomes nearly impossible to open a bank account, get a job, or even apply for a loan. The goal? Make life so difficult that people “self-deport.”

And if you’re marked as dead in the Social Security system, good luck fixing it. There’s virtually no path back — it’s a bureaucratic black hole.

You might ask: why do immigrants, asylum seekers, or refugees even have Social Security numbers? Because anyone authorized to work in the U.S. legally is issued one. It’s not just for citizens. It’s essential for participating in modern life — jobs, housing, banking, taxes. Without it, you’re locked out of society.

Last week, this plan was finalized in a high-level White House meeting that included DHS Secretary Kristi Noem, Antonio Gracias, senior DOGE operatives, and top administration officials.

In recent weeks, the administration has moved aggressively to strip legal protections from hundreds of thousands of immigrants and international students — many of whom have been living and working in the U.S. legally for years.

At the core of this crackdown? Data.

DOGE has access to your SSN, your income, your political donations — and more. What was once sold as a tool for “government efficiency” has become something else entirely: a weaponized surveillance machine.

And if you think this ends with immigrants, think again.

Antonio Gracias has already used DOGE’s access to Social Security and state-level data to push voter fraud narratives during past elections. The system is in place. The precedent has been set. And average Americans should be concerned.

[erik-even-wordier]

DOGE has transformed into a system for surveillance, control, and targeting

Posted by Alt National Park Service to Facebook on April 12, 2025

DOGE has quietly transformed into something far more sinister — not a system for streamlining government, but one designed for surveillance, control, and targeting. And no one’s talking about it. So we’re going to spill the tea.

From the beginning, DOGE’s true mission has been about data — collecting massive amounts of personal information on Americans. Now, that data is being turned against immigrants.

At the center of this effort is Antonio Gracias, a longtime Elon Musk confidante. Though he holds no official government position, Gracias is leading a specialized DOGE task force focused on immigration. His team has embedded engineers and staff across nearly every corner of the Department of Homeland Security (DHS).

But it doesn’t stop there.

DOGE operatives have also been quietly placed inside other federal agencies like the Social Security Administration and the Department of Health and Human Services — agencies that store some of the most sensitive personal data in the country, including on immigrants.

DOGE engineers now working inside DHS include Kyle Schutt, Edward Coristine (nicknamed “Big Balls”), Mark Elez, Aram Moghaddassi, and Payton Rehling. They’ve built the technical foundation behind a sweeping plan to revoke, cancel visas, and rewire the entire asylum process.

One of the most disturbing aspects of this plan? Flagging immigrants as “deceased” in the Social Security system — effectively canceling their SSNs. Without a valid Social Security number, it becomes nearly impossible to open a bank account, get a job, or even apply for a loan. The goal? Make life so difficult that people “self-deport.”

And if you’re marked as dead in the Social Security system, good luck fixing it. There’s virtually no path back — it’s a bureaucratic black hole.

You might ask: why do immigrants, asylum seekers, or refugees even have Social Security numbers? Because anyone authorized to work in the U.S. legally is issued one. It’s not just for citizens. It’s essential for participating in modern life — jobs, housing, banking, taxes. Without it, you’re locked out of society.

Last week, this plan was finalized in a high-level White House meeting that included DHS Secretary Kristi Noem, Antonio Gracias, senior DOGE operatives, and top administration officials.

In recent weeks, the administration has moved aggressively to strip legal protections from hundreds of thousands of immigrants and international students — many of whom have been living and working in the U.S. legally for years.

At the core of this crackdown? Data.

DOGE has access to your SSN, your income, your political donations — and more. What was once sold as a tool for “government efficiency” has become something else entirely: a weaponized surveillance machine.

And if you think this ends with immigrants, think again.

Antonio Gracias has already used DOGE’s access to Social Security and state-level data to push voter fraud narratives during past elections. The system is in place. The precedent has been set. And average Americans should be concerned.

[theyhavetakenovermylife]

im not gonna lie, a little sad n disappointed that u use genAI for images…

Not gonna lie, I am very sad and disappointed that this is a conversation that is need to be had, on the platform of a TMNT reader insert writer. A conversation base of an image, created for the soul purpose of creating a idea for an outfit in a smut, generated in a part of the world, where AI regulations are becoming more and more strict (which I fully support, may I add), with the focus on ethical usage of Data and AI, among many other laws that are put in place to protect people's jobs and their personal data.

I live in the EU - one of the places with the most strict laws when it comes to personal data, and one of the first places to actually put an AI law into place. The AI act of 2024.

To those of you that do not know about the EU AI act, you can read about it here on the EU Parliament's webside: https://www.europarl.europa.eu/topics/en/article/20230601STO93804/eu-ai-act-first-regulation-on-artificial-intelligence

In other words, the EU wants AI to help create jobs and job opportunities, not steal them. Part of that is classifiactions. The AI I used to help give the reader an idea of what Bluestar's outfit would be (which I also disclosed as an AI image, as the law requrise in this part of the world), is classified as a low/lower-risk generative AI, that has to follow transparency requirements and the EU copyright law.

I do not know how other countries tackles AI, nor what laws they are planning on putting in place. I follow along in the EU and Danish conversations and laws about AI, as they actually are a big part of my studies as a future pedagog.

Part of my job is to look at AI as an aiding tool - not an overtaker. More specifically, I am learning how I can use AI as a tool for people in our community, at may have limited ways of expression or need extra help. Along with that, I also have make sure that the AI used, aline with the EU GDPR law (General Data Protection Regulation), as that is very much a big part of not just my future job, but everyone else's in the EU.

Now, I've just told you about the EU AI Act of 2024, but not how my country of Denmark has been talking and putting in protective measures on AI since 2019, with the focus on ethical usage of AI, aka protecting people and their jobs. Heck, this is the country that outlawed Uber, because it took jobs from Taxi drivers.

Companies in Denmark has been requred - by law - to report on their use of AI and Data Ethics since 2021. In other words, my country take AI and data very seriously, and don't mess around when it come to that.

Why this focus on the EU and Denmark you may ask? Well, that's because all of these laws and proposed laws, are some that I follow on a daily basis as a Danish citizen. A country that has focused on the digital ethics for years. I live in a country, where we teach children how to use AI in an ethical manner, and where we teach university how to use AI as a tool - a compliment to their studies - and not something that is supposed to take over their studies, rendering them obsolete.

I'm sorry, but whatever lack of rules and regualtions other countries may have, does not apply to me, and the increasingly strict set of rules I have to follow in my country.

I am very aware that people in Hollywood are losing their jobs due to AI, and I think it's horrifing. I'm very aware that graffic designers in other countries have lost jobs to AI, and it makes me very sad. But I am not a lord of regulations in other countries - missing or not. I am just a Danish citizen, following the Danish law and rules, and whether or not other places will look at such rules and laws themselves, I have no control over.

I am sad and disappointed in the comments in my Inbox, that is calling me all sorts of names, saying that I don't care about the environment, because I generated an AI image, totally disregarding the fact that I live in one of the most carbon neutral cities in the western world, and not knowing that I was one of the first generations of students, studying at the Danish FN's World Goals profile schools.

I am sad and disappointed in the comments in my Inbox, telling me i am uneducated, when this is literally part of my studies. When my studies and future job opportunities - among many others -, literally requries that I'm up to date with the laws regarding data and AI usage for my own country, and have been doing so, ever since I first started working in 2020.

I am sad and disappointed in the comments in my Inbox saying that I am ruining the job opportunities, because I didn't pay an artist to illustrate an image. What money may I ask you? I'm a student, doing this for free, because I enjoy it, with literally no monetary gains from this, what so ever. So what money??

I am sad and disappointed that the platform I use to write TMNT stories, should now become a ground for dicussions about AI, when that is something that should be taken up with the rule makers of where ever you live, and not a TMNT reader insert writer on Tumblr.

I refuse to take further part in the discussion about AI using my Tumblr platform, only deciding to do so now, as many messages I started recivieng were hurtful, starting to boarder on the abusive.

I leave you with this power point, showing you how AI is viewed along side jobs and people's job views and opportunities in Denmark as by 2024, made by the Nordic firm Implement Consultation Group.

Remember to treat each other with kindness, and understand the round world is different everywhere💚

[darkmaga-returns]

Apr 24, 2025

🛸 00:00 - Introduction to Trey Hudson and the Meadow Project

Trey Hudson, author, military veteran, and paranormal investigator, joins to discuss his book and research into a high-strangeness area known as "The Meadow" in the southern U.S., a location compared to Skinwalker Ranch.

🌌 00:10 - Bob's Orb Transformation Incident

During a 2016 investigation, a team observed Bob, a field operative, inexplicably transform into an orb of light on thermal imaging—while Bob claimed he simply walked across the area.

👣 00:20 - Encounters and GPS Anomalies

Team experienced encounters with hominid-like figures and odd GPS tracking data showing impossible straight-line paths. Bob’s data later disappeared from his SD card. Hypnotic regression failed to recover his memory.

🔲 00:30 - February 2017: The Thermal Cubes

Team observed man-sized heat signatures and bizarre thermal-only visible cubes appearing in the meadow. A volunteer entered one, vanishing from thermal view and encountering an altered reality. Two people from that encounter later died.

⌛ 00:40 - Temporal and Spatial Anomalies

Trey experienced a profound disorientation near a creek, unable to recognize familiar terrain. Found an unusual sapling bent into a perfect oval—possibly a natural marker or dimensional portal.

💡 00:50 - Equipment Dreams and Consciousness Experiments

Discussed desire for advanced monitoring tools like lasers and EMF/radiation loggers. Team also uses techniques like the Estes Method, meditation, Monroe Institute principles, and the God Helmet to enhance encounters.

🔮 01:00 - Abductions, Entities, and Ultra-Terrestrials

Mention of classical abduction symptoms, shadow entities, and dogman tracks found near Trey's home. Discussed theory of ultra-terrestrials—entities that manifest in different forms (UFOs, Bigfoot, ghosts) depending on how they interact with our dimension.

📦 01:10 - Hitchhikers, Cubes, and Spiritual Implications

Team members have experienced poltergeist-type activity post-expedition. Trey's team is exploring simulation theory and notes thematic overlaps between high-strangeness sites like Skinwalker Ranch and ancient esoteric symbols (e.g., cubes, sacred geometry).

🧠 01:20 - Precognition and Remote Viewing

Touched on mainstream acceptance of precognitive dreams. Explained military’s remote viewing programs and correlations with high-strangeness areas. Trey’s team trained in controlled remote viewing; plans to use AI for future pattern recognition.

📚 01:30 - Wrap-Up and Promotions

Trey promotes his second book, “Return to the Meadow,” due May 27. Emphasizes the importance of preserving the meadow site and continuing to share these stories with the world.

[jcmarchi]

How can AI unlock human potential in the supply chain?

New Post has been published on https://thedigitalinsider.com/how-can-ai-unlock-human-potential-in-the-supply-chain/

How can AI unlock human potential in the supply chain?

AI is driving a new revolution across a number of industries and the supply chain is no exception. AI has been the most transformative technology of the decade, and it’s no secret it has helped supply chains become more efficient, resilient, and responsive, while allowing organisations to become more efficient and ensuring workforces to focus on more strategic growth.

However despite the benefits of the technology, many businesses are slow to adopt the technology, with recent statistics showing only one in ten of SME’s regularly use AI technology, indicating companies and employees are still not operating at their full potential, thus missing out on opportunities for growth and optimisation. 

Transforming the supply chain through AI

The potential that AI has in the supply chain is undeniable, with some estimating that AI helps businesses reduce logistics costs by 15%, reduce inventory levels by 35% and raise service levels by 65%. In contrast, failure to implement AI tools could set companies back, leave employees feeling unmotivated and unproductive and result in a weak supply chain and poor staff retention.

Now, more than ever, it’s time for businesses to not just pay lip service to AI – they must start using it within their supply chains to truly enhance operations. Due to the evolving market dynamics, AI is not just a competitive advantage; it’s essential for business agility and profitability. Here are two ways in which organisations can use AI to improve their supply chains.

Automating the supply chain & harnessing the power of AI for resilience

AI allows businesses to tackle supply chain challenges head-on by automating time-consuming manual processes, such as data-logging whilst reducing errors. By taking over repetitive and potentially hazardous tasks, AI frees up employees to focus on strategic initiatives that drive business value. For example, a recent report highlighted that nearly three quarters of warehouse staff surveyed are excited about the possibilities of generative AI and robotics improving their job roles.

Needless to say, a supply chain still can’t operate at its peak without resilience – which is the capacity of a supply chain to withstand and recover from disruptions – ensuring uninterrupted operations and minimal impact to businesses and customers.

As global markets continue to evolve & expand, businesses are challenged to adapt swiftly to unforeseen disruptions. AI enables businesses to provide real time data analysis, providing unprecedented insights into the web of supply chain dynamics and acting as the eyes and ears of a supply chain. This empowers each component with the ability to make informed decisions quickly to meet supply chain demands. Allowing insights into every aspect of their warehouse operations, real time data enables visibility which permits precise monitoring, enhanced customer service and reduced downtime – identifying potential issues before they become a major problem.

At the heart of the supply chain is communication between all stakeholders, with technology such as AI providing real time data, seamless collaboration is enabled by providing a shared platform where suppliers, manufacturers, and distributors can exchange information instantaneously. Enhanced communication leads to quicker issue resolution, enabling the supply chain to adapt rapidly to changing circumstances. Robotics, AI and real-time data introduce an all-encompassing visibility of the good’s journey, which leads to resilience.

Human expertise with robot precision

Building on the theme of resilience, in the next couple of years the industry will witness AI-integrated robots becoming collaborative partners to their human co-workers. Particularly in environments requiring vast coverage and extensive data capture, robots that are equipped with groundbreaking sensor technologies will navigate, adapt and work with greater levels of autonomy along with other machinery and people in busy environments. This will result in speed of data acquisition and most importantly, allowing companies to make decisions based on actionable insights a lot faster than ever before. 

These advancements will transform robots into true cobots and will take human-robot teamwork to an unprecedented level. We will also see that robots will become better with understanding nuanced human gestures and intentions. This evolution in collaboration with technology will redefine what humans and machines can accomplish together.

What’s next for the industry?

In theory implementing AI and advanced technology in the supply chain has the potential to bring significant benefits. However, we will only begin to see substantial results once these innovations are widely adopted in practice. By automating the supply chain and using data to fuel predictions, these technologies are the foundations for a new industrial revolution that will shape the future of the industries for years to come. Those that delay starting their journeys will risk being left behind.

Photo by Miltiadis Fragkidis on Unsplash

Want to learn more about AI and big data from industry leaders? Check out AI & Big Data Expo taking place in Amsterdam, California, and London. The comprehensive event is co-located with other leading events including Intelligent Automation Conference, BlockX, Digital Transformation Week, and Cyber Security & Cloud Expo.

Explore other upcoming enterprise technology events and webinars powered by TechForge here.

[sak-shi]

Python Libraries to Learn Before Tackling Data Analysis

To tackle data analysis effectively in Python, it's crucial to become familiar with several libraries that streamline the process of data manipulation, exploration, and visualization. Here's a breakdown of the essential libraries:

 1. NumPy

   - Purpose: Numerical computing.

   - Why Learn It: NumPy provides support for large multi-dimensional arrays and matrices, along with a collection of mathematical functions to operate on these arrays efficiently.

   - Key Features:

     - Fast array processing.

     - Mathematical operations on arrays (e.g., sum, mean, standard deviation).

     - Linear algebra operations.

 2. Pandas

   - Purpose: Data manipulation and analysis.

   - Why Learn It: Pandas offers data structures like DataFrames, making it easier to handle and analyze structured data.

   - Key Features:

     - Reading/writing data from CSV, Excel, SQL databases, and more.

     - Handling missing data.

     - Powerful group-by operations.

     - Data filtering and transformation.

 3. Matplotlib

   - Purpose: Data visualization.

   - Why Learn It: Matplotlib is one of the most widely used plotting libraries in Python, allowing for a wide range of static, animated, and interactive plots.

   - Key Features:

     - Line plots, bar charts, histograms, scatter plots.

     - Customizable charts (labels, colors, legends).

     - Integration with Pandas for quick plotting.

 4. Seaborn

   - Purpose: Statistical data visualization.

   - Why Learn It: Built on top of Matplotlib, Seaborn simplifies the creation of attractive and informative statistical graphics.

   - Key Features:

     - High-level interface for drawing attractive statistical graphics.

     - Easier to use for complex visualizations like heatmaps, pair plots, etc.

     - Visualizations based on categorical data.

 5. SciPy

   - Purpose: Scientific and technical computing.

   - Why Learn It: SciPy builds on NumPy and provides additional functionality for complex mathematical operations and scientific computing.

   - Key Features:

     - Optimized algorithms for numerical integration, optimization, and more.

     - Statistics, signal processing, and linear algebra modules.

 6. Scikit-learn

   - Purpose: Machine learning and statistical modeling.

   - Why Learn It: Scikit-learn provides simple and efficient tools for data mining, analysis, and machine learning.

   - Key Features:

     - Classification, regression, and clustering algorithms.

     - Dimensionality reduction, model selection, and preprocessing utilities.

 7. Statsmodels

   - Purpose: Statistical analysis.

   - Why Learn It: Statsmodels allows users to explore data, estimate statistical models, and perform tests.

   - Key Features:

     - Linear regression, logistic regression, time series analysis.

     - Statistical tests and models for descriptive statistics.

 8. Plotly

   - Purpose: Interactive data visualization.

   - Why Learn It: Plotly allows for the creation of interactive and web-based visualizations, making it ideal for dashboards and presentations.

   - Key Features:

     - Interactive plots like scatter, line, bar, and 3D plots.

     - Easy integration with web frameworks.

     - Dashboards and web applications with Dash.

 9. TensorFlow/PyTorch (Optional)

   - Purpose: Machine learning and deep learning.

   - Why Learn It: If your data analysis involves machine learning, these libraries will help in building, training, and deploying deep learning models.

   - Key Features:

     - Tensor processing and automatic differentiation.

     - Building neural networks.

 10. Dask (Optional)

   - Purpose: Parallel computing for data analysis.

   - Why Learn It: Dask enables scalable data manipulation by parallelizing Pandas operations, making it ideal for big datasets.

   - Key Features:

     - Works with NumPy, Pandas, and Scikit-learn.

     - Handles large data and parallel computations easily.

Focusing on NumPy, Pandas, Matplotlib, and Seaborn will set a strong foundation for basic data analysis.

[joemardesichcms]

The Future of Commercial Loan Brokering: Trends to Watch!

The commercial loan brokering industry is evolving rapidly, driven by technological advancements, changing market dynamics, and shifting borrower expectations. As businesses continue to seek financing solutions, brokers must stay ahead of emerging trends to remain competitive. Here are some key developments shaping the future of commercial loan brokering:

1. Rise of AI and Automation

Artificial intelligence (AI) and automation are revolutionizing loan processing. From AI-driven underwriting to automated document verification, these technologies are streamlining workflows, reducing manual effort, and speeding up loan approvals. Brokers who leverage AI-powered tools can offer faster and more efficient services.

2. Alternative Lending is Gaining Momentum

Traditional banks are no longer the only players in commercial lending. Alternative lenders, including fintech platforms and private lenders, are expanding options for businesses that may not qualify for conventional loans. As a result, brokers must build relationships with non-bank lenders to provide flexible financing solutions.

3. Data-Driven Decision Making

Big data and analytics are transforming how loans are assessed and approved. Lenders are increasingly using alternative data sources, such as cash flow analysis and digital transaction history, to evaluate creditworthiness. Brokers who understand and utilize data-driven insights can better match clients with the right lenders.

4. Regulatory Changes and Compliance Requirements

The commercial lending landscape is subject to evolving regulations. Compliance with federal and state laws is becoming more complex, requiring brokers to stay updated on industry guidelines. Implementing compliance-friendly processes will be essential for long-term success.

5. Digital Marketplaces and Online Lending Platforms

Online lending marketplaces are making it easier for businesses to compare loan offers from multiple lenders. These platforms provide transparency, efficiency, and better loan matching. Brokers who integrate digital platforms into their services can enhance customer experience and expand their reach.

6. Relationship-Based Lending Still Matters

Despite digital advancements, relationship-based lending remains crucial. Many businesses still prefer working with brokers who offer personalized service, industry expertise, and lender connections. Building trust and maintaining strong relationships with both clients and lenders will continue to be a key differentiator.

7. Increased Focus on ESG (Environmental, Social, and Governance) Lending

Sustainability-focused lending is gaining traction, with more lenders prioritizing ESG factors in their financing decisions. Brokers who understand green financing and social impact lending can tap into a growing market of businesses seeking sustainable funding options.

Final Thoughts

The commercial loan brokering industry is undergoing a transformation, with technology, alternative lending, and regulatory changes shaping the future. Brokers who embrace innovation, stay informed on market trends, and continue building strong relationships will thrive in this evolving landscape.

Are you a commercial loan broker? What trends are you seeing in the industry? Share your thoughts in the comments below!

[digitaldetoxworld]

Data Visualization: Transforming Data into Insight

 In an technology wherein information is produced at an remarkable tempo, the ability to extract significant insights is extra vital than ever. Data visualization plays a vital function on this procedure, enabling individuals and corporations to understand complex statistics sets, pick out trends, and communicate findings effectively. By converting abstract numbers into intuitive visuals, information visualization bridges the gap among uncooked data and human cognition, turning complexity into readability.

Data Visualization In Research 

The Importance of Data Visualization

Data visualization is the graphical illustration of information and facts. By the use of visible elements like charts, graphs, and maps, statistics visualization tools make it less difficult to see and understand styles, trends, and outliers in facts. Its importance lies in numerous key areas:

Improved Understanding: Visuals are processed 60,000 times faster than textual content by way of the human mind. Graphs and charts can screen insights that would pass omitted in spreadsheets.

Enhanced Communication: Well-crafted visualizations allow statistics to be shared in a manner that’s available to a broader audience, no longer simply records analysts or statisticians.

Data-Driven Decision Making: In enterprise, governments, and medical research, visualizations support selection-making via without a doubt showing the implications of various statistics tendencies.

Pattern and Anomaly Detection: They help users quick become aware of deviations, spikes, or drops in data, which could suggest possibilities or threats.

Types of Data Visualization

Data visualization encompasses a big selection of techniques, each applicable to precise types of records and analytical desires. Some of the most commonly used sorts include:

1. Bar Charts

Bar charts are best for comparing quantities throughout classes. They are simple however effective for displaying differences among agencies.

2. Line Graphs

Often used to music changes over time, line graphs display tendencies and fluctuations, making them a fave for time-series information.

3. Pie Charts

 They’re satisfactory for simple, clear percent facts.

4. Histograms

Histograms display the distribution of a dataset, making them beneficial for understanding records spread, crucial tendency, and frequency.

5. Heat Maps

Heat maps use colour gradients to indicate value depth throughout two dimensions. 

6. Scatter Plots

Scatter plots are used to pick out relationships between  variables, often revealing correlations or clusters in facts.

7. Box Plots

Box plots show the distribution of a dataset thru its quartiles, highlighting medians, variability, and ability outliers.

8. Geospatial Maps

These visualizations display facts associated with geographic regions and are extensively utilized in demographic research, environmental tracking, and logistics.

9. Dashboards

Dashboards integrate multiple visualizations into one interface, supplying a actual-time assessment of key metrics and overall performance signs.

Tools for Data Visualization

A huge range of tools is to be had for growing effective statistics visualizations.  Popular alternatives encompass:

Tableau: A leading platform for interactive, shareable dashboards with drag-and-drop functions.

Power BI: Microsoft's enterprise analytics tool with sturdy integration into the Office atmosphere.

Google Data Studio: A unfastened tool for developing customizable reports the use of Google records sources.

Ggplot2: A effective R package for constructing state-of-the-art plots the use of the grammar of snap shots.

Each device gives distinctive competencies depending at the user’s technical information, information complexity, and desired results.

Best Practices in Data Visualization

Creating effective facts visualizations requires more than just technical skill. It includes an information of design ideas, cognitive psychology, and storytelling. Here are key exceptional practices:

1. Know Your Audience

Tailor the visualization to the information stage and pursuits of your target market. What a statistics scientist unearths intuitive is probably complicated to a business executive.

2. Choose the Right Chart

Using an inappropriate chart kind can deceive or confuse the viewer. For instance, a line chart ought to not be used for specific information.

Three. Simplify and Clarify

Avoid muddle. Focus on essential statistics and put off unnecessary elements like immoderate gridlines, decorative snap shots, or redundant labels.

Four. Use Color Thoughtfully

Color can enhance know-how but additionally lie to if used improperly. Stick to a consistent color scheme and use contrasts to highlight key points.

5. Tell a Story

Effective facts visualizations guide the viewer through a story. Highlight tendencies, anomalies, or correlations that support your message.

6. Maintain Integrity

Never manipulate axes or distort scales to magnify findings. Ethical visualization ensures accurate illustration of statistics.

Real-World Applications

Data visualization is applied in nearly each region, transforming industries through stepped forward insight and communication.

1. Business Analytics

In commercial enterprise, visualization tools assist in monitoring sales, client behavior, supply chain efficiency, and extra. 

2. Healthcare

In medicinal drug and public health, visualizations are crucial for tracking disorder outbreaks, affected person records, and treatment results. For example, COVID-19 dashboards performed a main function in information the pandemic's unfold.

3. Finance

Financial analysts use records visualization to recognize market tendencies, examine investment overall performance, and check chance.

Four. Education

Educators and researchers use visualization to track pupil performance, perceive mastering gaps, and gift studies findings.

Five. Government and Policy

Policymakers use visible facts to understand social trends, aid allocation, and financial overall performance. 

6. Journalism

Data journalism is growing hastily. Visual stories on topics like weather change, election results, or social inequality use charts and infographics to inform and engage readers.

Challenges and Limitations

Despite its electricity, facts visualization isn't with out demanding situations:

Data Quality: Inaccurate or incomplete information can lead to deceptive visuals.

Over-Simplification: Trying to make information too easy can lead to lack of nuance or important info.

Misinterpretation: Poor design selections or biased displays can cause audiences to draw wrong conclusions.

Tool Limitations: Not all equipment aid the extent of customization or interactivity wished for unique projects.

Overcoming these demanding situations requires a mix of technical talent, area information, and moral responsibility.

The Future of Data Visualization

The future of statistics visualization is increasingly interactive, actual-time, and AI-assisted. Emerging traits include:

Augmented and Virtual Reality (AR/VR): Immersive visualizations permit users to explore records in three-dimensional environments.

Machine Learning Integration: Algorithms can now endorse or even vehicle-generate visualizations based on the information furnished.

Collaborative Platforms: Teams can now work collectively in actual time on visualization dashboards, improving communique and agility.

These advancements will hold to make records greater accessible and insightful throughout all domain names.

Difference Between  Augmented Reality (AR) and Virtual Reality (VR) 

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