#Data Techniques

[howgalling]

idk if I mentioned but I got to speak to JRO after the con at tfnation; specifically about the first aid plot line he didn’t end up getting to include in the mtmte comics!!

I missed the panel where he was talking about it, but one of my friends mentioned it, so I had to ask for more information!!!!!. First Aid essentially loses the plot for a while trying to find a way to bring ambulon back. it’s briefly mentioned as an off comment by (maybe ratchet?) that first aid has been isolating himself for months with ambulon’s dead body in the comics. Which is crazy btw lol anyway

first aid spends all that time between the plot failing to find ways to bring ambulon back to life. He fully repairs ambulons body, every fragment of him is visually perfect, /technically/ functional. He even repaints ambulon, touching up the patchy paint job Ambulon was known for.

But ambulon is truly dead. What made ambulon who he was, his memories, his personality, his knowledge, his friendship- was obliterated in the bisecting, as was his spark. It was a complete brain-death by cybertronian standards.

As far as I’m aware, the spark is just a power source, relative to the memory banks portion of their processors and whatnot, specifically the sections that store the bots actual personality and memories. Though it’s always worded differently. JRO didn’t give an exact time range (understandable lol) for how long they were on Delphi, but the general thought is that First Aid and Ambulon were on Delphi together for a decent chunk of years, relative to the timeline.

:((( first aid and ambulon really endured Delphi together and bonded throughout that, and to see him murdered so abruptly, (by their ex-boss who they watched descend into madness- no less!!!) was extremely traumatising, as much as it’s played as a joke by the fandom lolol. ambulon suffered what was essentially complete brain death at the moment of injury, non-recoverable.

The dynamic between ratchet, Pharma and first aid/ambulon is really interesting, other people can word it better than me but it ties massively into the feedback loop first aid gets lost in from that point. Watching his closest friend die in a hysterical power play between his two superiors would be helpless agony. And it hurts more because we know first aids temperament :(( he’s still soft, despite it all, and he’s the type of character that you just want to shield from the nastiness of it all. 😭😭

First aid goes as far tracking down the mechs that were involved in the faulty combiner experiment Ambulon was forced into. Looking for maybe even an echo or an imprint, faint traces in the data+code of the mech’s that might have had /anything/ of his friend left in it. Pieces he could put together to make him whole again. But there’s nothing, just surface level memories from a bond that never succeeded. MAN

Anyway, I’m going to try and draw something for it, please give me focus

[ihaveaweirdidea]

[2mo3cm-man]

[idol--hands]

[bahadurislam011444]

Unveiling the Best SEO Worker in Bangladesh: Driving Digital Success

[elavilaonfire]

Armando Reverón – Reina con corona y vestido de Reverón, ND.

[lensnure-solutions]

Lensnure Solutions is a passionate web scraping and data extraction company that makes every possible effort to add value to their customer and make the process easy and quick. The company has been acknowledged as a prime web crawler for its quality services in various top industries such as Travel, eCommerce, Real Estate, Finance, Business, social media, and many more.

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[corpusdigitalhub]

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[swarmenterprises]

Swarm Enterprises

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[laurin4475]

“To sum up, just because someone put a lot of work into gathering data and making a nice color-coded chart, doesn’t mean the data is GOOD or VALUABLE.”

^^^^^^^ this!! A massive pet peeve of mine is that people assume just because “analysis” has been done on data that it’s valid. But you can do analysis on junk data and still turn out a pretty picture! Your analysis is only as good as the data and assumptions you have made.

If something looks hinky, investigate it further. Maybe it’s nothing, but maybe the assumptions underlying the data are wrong, or the type of analysis someone has done is wrong.

AO3 Ship Stats: Year In Bad Data

You may have seen this AO3 Year In Review.

It hasn’t crossed my tumblr dash but it sure is circulating on twitter with 3.5M views, 10K likes, 17K retweets and counting. Normally this would be great! I love data and charts and comparisons!

Except this data is GARBAGE and belongs in the TRASH.

I first noticed something fishy when I realized that Steve/Bucky – the 5th largest ship on AO3 by total fic count – wasn’t on this Top 100 list anywhere. I know Marvel’s popularity has fallen in recent years, but not that much. Especially considering some of the other ships that made it on the list. You mean to tell me a femslash HP ship (Mary MacDonald/Lily Potter) in which one half of the pairing was so minor I had to look up her name because she was only mentioned once in a single flashback scene beat fandom juggernaut Stucky? I call bullshit.

Now obviously jumping to conclusions based on gut instinct alone is horrible practice... but it is a good place to start. So let’s look at the actual numbers and discover why this entire dataset sits on a throne of lies.

Here are the results of filtering the Steve/Bucky tag for all works created between Jan 1, 2023 and Dec 31, 2023:

Not only would that place Steve/Bucky at #23 on this list, if the other counts are correct (hint: they're not), it’s also well above the 1520-new-work cutoff of the #100 spot. So how the fuck is it not on the list? Let’s check out the author’s FAQ to see if there’s some important factor we’re missing.

The first thing you��ll probably notice in the FAQ is that the data is being scraped from publicly available works. That means anything privated and only accessible to logged-in users isn’t counted. This is Sin #1. Already the data is inaccurate because we’re not actually counting all of the published fics, but the bots needed to do data collection on this scale can't easily scrape privated fics so I kinda get it. We’ll roll with this for now and see if it at least makes the numbers make more sense:

Nope. Logging out only reduced the total by a couple hundred. Even if one were to choose the most restrictive possible definition of "new works" and filter out all crossovers and incomplete fics, Steve/Bucky would still have a yearly total of 2,305. Yet the list claims their total is somewhere below 1,500? What the fuck is going on here?

Let’s look at another ship for comparison. This time one that’s very recent and popular enough to make it on the list so we have an actual reference value for comparison: Nick/Charlie (Heartstopper). According to the list, this ship sits at #34 this year with a total of 2630 new works. But what’s AO3 say?

Off by a hundred or so but the values are much closer at least!

If we dig further into the FAQ though we discover Sin #2 (and the most egregious): the counting method. The yearly fic counts are NOT determined by filtering for a certain time period, they’re determined by simply taking a snapshot of the total number of fics in a ship tag at the end of the year and subtracting the previous end-of-year total. For example, if you check a ship tag on Jan 1, 2023 and it has 10,000 fics and check it again on Jan 1, 2024 and it now has 12,000 fics, the difference (2,000) would be the number of "new works" on this chart.

At first glance this subtraction method might seem like a perfectly valid way to count fics, and it’s certainly the easiest way, but it can and did have major consequences to the point of making the entire dataset functionally meaningless. Why? If any older works are deleted or privated, every single one of those will be subtracted from the current year fic count. And to make the problem even worse, beginning at the end of last year there was a big scare about AI scraping fics from AO3, which caused hundreds, if not thousands, of users to lock down their fics or delete them.

The magnitude of this fuck up may not be immediately obvious so let’s look at an example to see how this works in practice.

Say we have two ships. Ship A is more than a decade old with a large fanbase. Ship B is only a couple years old but gaining traction. On Jan 1, 2023, Ship A had a catalog of 50,000 fics and ship B had 5,000. Both ships have 3,000 new works published in 2023. However, 4% of the older works in each fandom were either privated or deleted during that same time (this percentage is was just chosen to make the math easy but it’s close to reality).

Ship A: 50,000 x 4% = 2,000 removed works Ship B: 5,000 x 4% = 200 removed works

Ship A: 3,000 - 2,000 = 1,000 "new" works Ship B: 3,000 - 200 = 2,800 "new" works

This gives Ship A a net gain of 1,000 and Ship B a net gain of 2,800 despite both fandoms producing the exact same number of new works that year. And neither one of these reported counts are the actual new works count (3,000). THIS explains the drastic difference in ranking between a ship like Steve/Bucky and Nick/Charlie.

How is this a useful measure of anything? You can't draw any conclusions about the current size and popularity of a fandom based on this data.

With this system, not only is the reported "new works" count incorrect, the older, larger fandom will always be punished and it’s count disproportionately reduced simply for the sin of being an older, larger fandom. This example doesn’t even take into account that people are going to be way more likely to delete an old fic they're no longer proud of in a fandom they no longer care about than a fic that was just written, so the deletion percentage for the older fandom should theoretically be even larger in comparison.

And if that wasn't bad enough, the author of this "study" KNEW the data was tainted and chose to present it as meaningful anyway. You will only find this if you click through to the FAQ and read about the author’s methodology, something 99.99% of people will NOT do (and even those who do may not understand the true significance of this problem):

The author may try to argue their post states that the tags "which had the greatest gain in total public fanworks” are shown on the chart, which makes it not a lie, but a error on the viewer’s part in not interpreting their data correctly. This is bullshit. Their chart CLEARLY titles the fic count column “New Works” which it explicitly is NOT, by their own admission! It should be titled “Net Gain in Works” or something similar.

Even if it were correctly titled though, the general public would not understand the difference, would interpret the numbers as new works anyway (because net gain is functionally meaningless as we've just discovered), and would base conclusions on their incorrect assumptions. There’s no getting around that… other than doing the counts correctly in the first place. This would be a much larger task but I strongly believe you shouldn’t take on a project like this if you can’t do it right.

To sum up, just because someone put a lot of work into gathering data and making a nice color-coded chart, doesn’t mean the data is GOOD or VALUABLE.

[greatonlinetrainingsposts]

How Do You Use a SAS Tutorial to Learn Data Cleaning Techniques?

Before you start analyzing data, it's important to understand how clean your dataset is. If your data has missing values, duplicate entries, or inconsistent formatting, it can throw off your entire analysis. Even the most advanced model won’t work well if the data going into it is flawed.

That’s where SAS programming comes in. When you follow a SAS tutorial, you’re not just learning how to write code—you’re learning how to think through data problems. A good tutorial explains what each step does and why it’s important.

Here’s how to use a SAS tutorial to build your data cleaning skills, step by step.

1. Start by Inspecting the Data

The first thing most SAS tutorials will show you is how to explore and inspect your dataset. This helps you understand what you’re working with.

You’ll learn how to use:

PROC CONTENTS to see the structure and metadata

PROC PRINT to view the raw data

PROC FREQ and PROC MEANS to check distributions and summaries

As you review the outputs, you’ll start spotting common problems like:

Too many missing values in key variables

Numbers stored as text

Values that don’t make sense or fall outside expected ranges

These early steps help you catch red flags before you go deeper.

2. Learn How to Handle Missing Data

Missing data is very common, and a good SAS tutorial will show you a few ways to deal with it.

This includes:

Using IF conditions to identify missing values

Replacing them with zeros, averages, or medians

Removing variables or rows if they’re not useful anymore

The tutorial might also explain when to fill in missing data and when to just leave it out. Real-world examples from healthcare, marketing, or finance help make the decisions easier to understand.

3. Standardize and Format Your Data

A lot of data comes in messy. For example, dates might be stored in different formats or categories might use inconsistent labels like "M", "Male", and "male".

With SAS programming, you can clean this up by:

Converting dates using INPUT and PUT functions

Making text consistent with UPCASE or LOWCASE

Recoding values into standardized categories

Getting your formatting right helps make sure your data is grouped and analyzed correctly.

4. Remove Duplicate Records

Duplicate records can mess up your summaries and analysis. SAS tutorials usually explain how to find and remove duplicates using:

PROC SORT with the NODUPKEY option

BY group logic to keep the most recent or most relevant entry

Once you understand the concept in a tutorial, you’ll be able to apply it to more complex datasets with confidence.

5. Identify Outliers and Inconsistencies

Advanced tutorials often go beyond basic cleaning and help you detect outliers—data points that are far from the rest.

You’ll learn techniques like:

Plotting your data with PROC SGPLOT

Using PROC UNIVARIATE to spot unusual values

Writing logic to flag or filter out problem records

SAS makes this process easier, especially when dealing with large datasets.

6. Validate Your Cleaning Process

Cleaning your data isn’t complete until you check your work. Tutorials often show how to:

Re-run summary procedures like PROC MEANS or PROC FREQ

Compare row counts before and after cleaning

Save versions of your dataset along the way so nothing gets lost

This step helps prevent mistakes and makes sure your clean dataset is ready for analysis.

Why SAS Programming Helps You Learn Faster

SAS is great for learning data cleaning because:

The syntax is simple and easy to understand

The procedures are powerful and built-in

The SAS community is active and supportive

Whether you're a beginner or trying to improve your skills, SAS tutorials offer a strong, step-by-step path to learning how to clean data properly.

Final Thoughts

Learning data cleaning through a SAS tutorial doesn’t just teach you code—it trains you to think like a data analyst. As you go through each lesson, try applying the same steps to a dataset you’re working with. The more hands-on experience you get, the more confident you’ll be.

If you want to improve your data analysis and make better decisions, start by getting your data clean. And using SAS programming to do it? That’s a smart first move.

[idol--hands]

LORE + SPOCK : Star Trek #5

Spock: “It may prove useful to our investigation, and when you are engaged with such a scientific puzzle, you seem far less threatening and potentially violent.” Lore: “Hm. Sure.”

IDOL ⭐️ HANDS: apparently they already re-dressed themselves or do the outfits repair themselves?? *insert stripper music* 🎶 V'Ger

A sentient, massive entity which threatened Earth in 2271, en route to find its "Creator." In doing so, V'Ger destroyed anything it encountered by digitizing it for its memory chamber.

Generating a power field "cloud" about itself of over 22 AUs in diameter, the entity had gained sentience after unknown aliens repaired the old Earth space probe that formed its core — Voyager 6, whose name in corrupted English gave the sentience its name.

The entity, which viewed organic lifeforms as carbon-based units "infesting" starships, later joined with Starfleet's Lt. Ilia and Cmdr. Will Decker and evolved into a higher, yet unknown lifeform. source: https://www.startrek.com/database_article/vger

[tudip123]

Demystifying Data Analytics: Techniques, Tools, and Applications

Introduction: In today’s digital landscape, data analytics plays a critical role in transforming raw data into actionable insights. Organizations rely on data-driven decision-making to optimize operations, enhance customer experiences, and gain a competitive edge. At Tudip Technologies, the focus is on leveraging advanced data analytics techniques and tools to uncover valuable patterns, correlations, and trends. This blog explores the fundamentals of data analytics, key methodologies, industry applications, challenges, and emerging trends shaping the future of analytics.

What is Data Analytics? Data analytics is the process of collecting, processing, and analyzing datasets to extract meaningful insights. It includes various approaches, ranging from understanding past events to predicting future trends and recommending actions for business optimization.

Types of Data Analytics: Descriptive Analytics – Summarizes historical data to reveal trends and patterns Diagnostic Analytics – Investigates past data to understand why specific events occurred Predictive Analytics – Uses statistical models and machine learning to forecast future outcomes Prescriptive Analytics – Provides data-driven recommendations to optimize business decisions Key Techniques & Tools in Data Analytics Essential Data Analytics Techniques: Data Cleaning & Preprocessing – Ensuring accuracy, consistency, and completeness in datasets Exploratory Data Analysis (EDA) – Identifying trends, anomalies, and relationships in data Statistical Modeling – Applying probability and regression analysis to uncover hidden patterns Machine Learning Algorithms – Implementing classification, clustering, and deep learning models for predictive insights Popular Data Analytics Tools: Python – Extensive libraries like Pandas, NumPy, and Matplotlib for data manipulation and visualization. R – A statistical computing powerhouse for in-depth data modeling and analysis. SQL – Essential for querying and managing structured datasets in databases. Tableau & Power BI – Creating interactive dashboards for data visualization and reporting. Apache Spark – Handling big data processing and real-time analytics. At Tudip Technologies, data engineers and analysts utilize scalable data solutions to help businesses extract insights, optimize processes, and drive innovation using these powerful tools.

Applications of Data Analytics Across Industries: Business Intelligence – Understanding customer behavior, market trends, and operational efficiency. Healthcare – Predicting patient outcomes, optimizing treatments, and managing hospital resources. Finance – Detecting fraud, assessing risks, and enhancing financial forecasting. E-commerce – Personalizing marketing campaigns and improving customer experiences. Manufacturing – Enhancing supply chain efficiency and predicting maintenance needs for machinery. By integrating data analytics into various industries, organizations can make informed, data-driven decisions that lead to increased efficiency and profitability. Challenges in Data Analytics Data Quality – Ensuring clean, reliable, and structured datasets for accurate insights. Privacy & Security – Complying with data protection regulations to safeguard sensitive information. Skill Gap – The demand for skilled data analysts and scientists continues to rise, requiring continuous learning and upskilling. With expertise in data engineering and analytics, Tudip Technologies addresses these challenges by employing best practices in data governance, security, and automation. Future Trends in Data Analytics Augmented Analytics – AI-driven automation for faster and more accurate data insights. Data Democratization – Making analytics accessible to non-technical users via intuitive dashboards. Real-Time Analytics – Enabling instant data processing for quicker decision-making. As organizations continue to evolve in the data-centric era, leveraging the latest analytics techniques and technologies will be key to maintaining a competitive advantage.

Conclusion: Data analytics is no longer optional—it is a core driver of digital transformation. Businesses that leverage data analytics effectively can enhance productivity, streamline operations, and unlock new opportunities. At Tudip Learning, data professionals focus on building efficient analytics solutions that empower organizations to make smarter, faster, and more strategic decisions. Stay ahead in the data revolution! Explore new trends, tools, and techniques that will shape the future of data analytics.

Click the link below to learn more about the blog Demystifying Data Analytics Techniques, Tools, and Applications: https://tudiplearning.com/blog/demystifying-data-analytics-techniques-tools-and-applications/.

[pseuddamntired]

Reblogging to add a specific thing I’ve messed around with: blurring data is not secure. Blurred data can be recovered, with some trial and error. Just gotta guess the right values for the kernel (the blurring filter, basically) that was applied and you can “reverse” the blur effect (it’s not likely to be one to one, there will likely be some artifacts) well enough that a person could read data from the image. If they get it right. But they have as many tries to get the parameters right as they need.

Censor it as completely as you can, destroy any pixels that could give data.

Unfortunately I am no expert in security so I can’t give advice on what to do instead, I just know about undoing blurring because I messed around with image processing techniques last fall.

Maybe look up destructive censoring? You want to destroy the sensitive data, not transform it. It should not be recoverable.

I try to use the boxes filled in to try and cover the pixels completely, and then I (maybe unnecessarily, idk) take a screenshot of the image and send that. I don’t want to chance that the original image is recoverable when sent out, even though I’m pretty sure that’s not easily done. But I think designing the feature for sending edited images such that it contained the original image in its data would be a terrible idea. I doubt major companies do that but there is likely a lot of metadata in files other than images that you might send out.

He did bro so bad homie made another video just to apologize.

[vasundhara-infotech]

What is NLP? What is the Role of NLP in AI?