#What is Data Validation in Excel

[mitcenter]

What is Data Validation in Excel? How to Improve Data Quality

Data validation is a crucial feature in Excel that helps users control the type of data entered into a cell. By setting specific criteria, you can ensure that only the correct data type is input, which helps in maintaining accuracy and consistency in your spreadsheets. This feature is particularly useful in large datasets where errors can easily go unnoticed, leading to inaccurate results and flawed analyses.

In this blog, we'll explore what is data validation in Excel, how it works, and how you can use it to improve data quality in your spreadsheets.

Understanding Data Validation in Excel

Data validation in Excel allows you to define rules that restrict the type of data that can be entered into a cell. These rules can include restrictions on data type (e.g., whole numbers, decimals, dates), specific values, or even custom formulas. When a user tries to enter data that doesn't meet the criteria, Excel will display an error message, preventing the entry of invalid data.

For example, if you're working with a list of ages, you can use data validation to ensure that only numbers between 1 and 120 are entered. This prevents errors such as negative numbers or extremely high values that would distort the analysis.

Steps to Implement Data Validation in Excel

Implementing data validation in Excel can be done through a few simple steps. Here’s how you can do it:

Identify Your Data Requirements: Before setting up data validation, determine what type of data you want to restrict. This could be a number, date, specific text, or values from a list.

Create a List or Criteria: If your data validation involves specific choices (e.g., a list of departments or categories), prepare the list beforehand. You can do this by typing the options into a separate worksheet or a column in the same sheet.

Select the Target Cells: Click and drag to highlight the cells where you want to apply the data validation rules. This could be a single cell, a column, or a range of cells.

Apply Data Validation Rules: (a) Go to the “Data” tab on the Excel ribbon. (b) Click on “Data Validation” in the Data Tools group.

In the dialog box that appears, under the “Settings” tab, choose the type of validation you want (e.g., whole numbers, dates, or a list).

Specify Your Criteria: Depending on the validation type selected, set up your specific criteria. For instance, if you’re working with a list, select the range where your list is located.

Set Error Alerts: Navigate to the “Error Alert” tab within the Data Validation dialog box. Here, you can create a custom message that will pop up if a user enters invalid data.

Test Your Validation Rules: Once applied, try entering data in the validated cells to ensure your rules work as intended. Adjust if necessary by returning to the Data Validation menu.

This process allows you to precisely control the data being entered, helping maintain the integrity and quality of your Excel spreadsheets.

Types of Data Validation in Excel

Excel offers several types of data validation to suit different needs. Here are some common types:

Whole Number: Restricts entries to whole numbers within a specified range.

Decimal: Allows decimal numbers within a specific range.

List: Lets users select from a predefined list of values.

Date: Restricts entries to dates within a specific range.

Time: Limits entries to times within a specific range.

Text Length: Controls the number of characters in a text entry.

Custom: Allows you to use a custom formula to define the validation criteria.

Benefits of Using Data Validation in Excel

Using data validation in Excel is essential for quality data analysis which offers numerous benefits:

Improves Data Accuracy: By restricting data entry to specific criteria, you reduce the risk of errors.

Enhances Consistency: Ensures that data follows a consistent format, making it easier to analyze.

Saves Time: Prevents the need for manual data cleaning by catching errors at the point of entry.

Guides Users: Custom error messages can help guide users to enter the correct data, reducing confusion.

Reduces Redundancy: By validating data, you can avoid duplicate entries, ensuring that your dataset remains clean and organized.

How to Improve Data Quality Using Data Validation

Data validation is a powerful tool, but it's just one part of improving data quality. Here are some additional tips to ensure high-quality data in Excel:

Use Consistent Formats: Ensure that all data entries follow a consistent format, such as dates in the same format (e.g., DD/MM/YYYY).

Regularly Audit Your Data: Periodically check your data for inconsistencies, errors, or missing values.

Leverage Conditional Formatting: Use conditional formatting to highlight cells that don't meet specific criteria, making it easier to spot errors.

Implement Drop-Down Lists: Where possible, use drop-down lists to limit data entry to a predefined set of options.

Use Data Cleaning Tools: Excel offers several tools like "Remove Duplicates" and "Text to Columns" that can help clean up your data.

Conclusion

Data validation in Excel is a powerful feature that can significantly improve the quality of your data. By setting specific criteria for data entry, you can reduce errors, enhance consistency, and ensure that your data is reliable and accurate. Combined with other data management practices, data validation can help you maintain high standards of data quality, making your analyses more trustworthy and actionable.

[thefantastickatinator]

Dropout should hire more trans women.

That said, a couple things about the data set floating around showing disproportionality in casting:

1. 7 of the top 9 (those cast members who appear in over 100 episodes, everyone else has under 70 appearances) are members of the core dimension 20 cast, aka “the intrepid heroes”. This cast has been in 7 of the 22 seasons, with those seasons usually being 20-ish episodes long (the other seasons are between 4-10 episodes long typically). That’s approximately 140 episodes for each of the main intrepid heroes cast members just for these seasons (not including bonus content like live shows). Brian Murphy has appeared 154 times, which means almost all of his appearances were on D20 intrepid heroes campaigns.

2. The other 2 in the top 9 are Sam Reich and Mike Trapp, who are both hosts of long running shows (Game Changer and Um, Actually)

3. 198 of the 317 episodes that noncis “TME” people have appeared in can be attributed to ally Beardsley alone (there is some crossover where for example alex and ally have both appeared in the same episodes). Erika ishii has been in 67 of the 317 noncis “TME” episode appearances i don’t know how much crossover there is between them but i don’t think they’ve been on d20 together so i doubt it’s more than 20. It could be as many as 250 of the 317 episodes that have either erica or ally. Both Erika and ally are majorly skewing the results for the data

4. Over 3/4 of people have no listed gender identity in the spreadsheet - most of them have 1-2 appearances, but a few have 3-4 appearances. I’m pretty sure these people aren’t included in the data at all (some of them i’m p sure are not cis like jiavani and bob the drag queen)

5. The data collector has assigned “tme” and “tma” to various cast members.

TME: transmisogyny exempt

TMA: transmisogyny affected

Now, tranmisogyny can affect trans women, trans femmes, and nonbinary people, and occasionally masculine appearing cis women.

I personally do not believe that an outside person can assign you a label deciding whether or not you experience certain types of oppression- and yet that is what the data collector has done.

I think a more accurate label would be amab/afab, or more honestly- “people i think are amab or have said they are amab and then everyone else”

6. The data does not include many of their newer shows such as Very Important People, Gastronauts, Play it By Ear, and Monet’s Slumber Party, all of which feature trans people (MSP, Gastronauts, and VIP are all hosted by noncis people)

What I think the data more accurately shows:

- Dimension 20 has a “main cast” who have appeared in the majority of episodes

- Dropout has some “regulars” who appear on the majority of their content/shows (sam has referenced multiple times that brennan is one of the first people he calls whenever someone can’t show up for something since he’s nearly always down for anything) - none of these people are trans women

Final thoughts:

I think eliminating “hosts” and the “intrepid heroes” from THIS TYPE of data set would be more appropriate because they massively skew the data when crunching the numbers for dropout shows. Especially since I can tell from the excel sheet that there are shows missing. Examining d20 sidequests and the guests on the other shows will give a more accurate representation of casting. Hosts should be analyzed separately as that’s a different casting process.

Also imagine if we referred to men and women as “misogyny exempt” and “misogyny affected” when doing demographics. Or if someone did a data collection of the number of POC appearances in dropout episodes and sorted it by “racism affected” and “racism exempt” - so weiiiiird

TLDR: the data set has massive issues with its methodology and that should be considered. That doesn’t make what trans women are saying less valid.

In other words: spiders brennan is an outlier and should not have been counted

[jointhefediverse]

💫 Join the Fediverse! 💫

Greetings, fellow bloggers! We welcome you to join us in discovering, honoring, and promoting the potential future of social networking—commonly referred to as the "Fediverse."

The Fediverse, or Federation Universe, refers to a collective of online platforms that utilize the web protocol known as ActivityPub, which has set a standard of excellence in regards to both protecting and respecting users' online privacies.

There's a good chance in the past few years that you've caught wind of the fedi family's critically acclaimed Mastodon; however, there are many other unique platforms worth your consideration...

✨ Where To Begin?

Conveniently enough, from the minds of brilliant independent developers, there already likely exists a Fediverse equivalent to your favorite socials. Whether it's an opinion from the critics, or from the community alike—the following popular websites are commonly associated with one another:

Friendica 🐰 = Facebook Mastodon 🐘 = Twitter Pixelfed 🐼 = Instagram PeerTube 🐙 = YouTube Lemmy 🐭 = Reddit

It's worth mentioning, too, a few other sites and forks thereof that are worthy counterparts, which be: Pleroma 🦊 & Misskey 🐱, microblogs also similar to Twitter/Mastodon. Funkwhale 🐋 is a self-hosting audio streamer, which pays homage to the once-popular GrooveShark. For power users, Hubzilla 🐨 makes a great choice (alongside Friendica) when choosing macroblogging alternatives.

✨ To Be Clear...

To address the technicalities: aside from the "definitive" Fediverse clients, we will also be incorporating any platforms that utilize ActivityPub-adjacent protocols as well. These include, but are not limited to: diaspora*; AT Protocol (Bluesky 🦋); Nostr; OStatus; Matrix; Zot; etc. We will NOT be incorporating any decentralized sites that are either questionably or proven to be unethical. (AKA: Gab has been exiled.)

✨ Why Your Privacy Matters

You may ask yourself, as we once did, "Why does protecting my online privacy truly matter?" While it may seem innocent enough on the surface, would it change your mind that it's been officially shared by former corporate media employees that data is more valuable than money to these companies? Outside of the ethical concerns surrounding these concepts, there are many other reasons why protecting your data is critical, be it: security breaches which jeopardize your financial info and risk identity theft; continuing to feed algorithms which use psychological manipulation in attempts to sell you products; the risk of spyware hacking your webcams and microphones when you least expect it; amongst countless other possibilities that can and do happen to individuals on a constant basis. We wish it could all just be written off as a conspiracy... but, with a little research, you'll swiftly realize the validity of these claims are not to be ignored any longer. The solution? Taking the decentralized route.

✨ Our Mission For This Blog

Our mission for establishing this blog includes 3 core elements:

To serve as a hub which anybody can access in order to assist themselves in either: becoming a part of the Fediverse, gaining the resources/knowledge to convince others to do the very same, and providing updates on anything Fedi-related.

We are determined to do anything within our power to prevent what the future of the Internet could become if active social users continue tossing away their data, all while technologies are advancing at faster rates with each passing year. Basically we'd prefer not to live in a cyber-Dystopia at all costs.

Tumblr (Automattic) has expressed interest in switching their servers over to ActivityPub after Musk's acquisition of then-Twitter, and are officially in the transitional process of making this happen for all of us. We're hoping our collective efforts may at some point be recognized by @staff, which in turn will encourage their efforts and stand by their decision.

With that being stated, we hope you decide to follow us here, and decide to make the shift—as it is merely the beginning. We encourage you to send us any questions you may have, any personal suggestions, or corrections on any misinformation you may come across.

From the Tender Hearts of, ✨💞 @disease & @faggotfungus 💞✨

[thewomancallednova]

Pen Pals

Oh fuck that was beautiful

shit what was the last episode i called "the best tng episode" yet, cause it might have been dethroned

okay i also gotta say that i have madly misrepresented this episode in the past. Like, in my memory from 15 years ago this episode was "Data makes a pre-warp friend, her home planet is threatened by geology, Picard so we gotta let them die because of the prime directive, but I guess we can make an exception for Data's friend if he insists, and then everyone except Data's friend dies." And that is not at all what happens!

At least up until the "we gotta let them all die" point, because they have a discussion about the prime directive and fate and it's excellent. Geordi, Troi, Data and Pulaski are the pro-saving the planet's people side and Picard, Worf and (weakly) Riker are the anti-saving the planet's people side. And it's a really good discussion. That Picard ends by basically saying "well if we save people from volcanoes, then we'd also have to save them from slavery, and where'd that bring us?" And then Data fucking plays the recording of this ten-year old girl asking for help and everyone gets emotionally affected enough to say fuck the prime directive, let's save these people if we can. And then they do! It's fucking great!

And I love that this gently bends Picard. Like, the Picard of Symbiosis would not have gone for this and the episode doesn't outright changes him, it just nudges him ever so slightly in the right direction to make him a little better. Although yes, it is fucked up that he needed to hear that little girl's pleas for help first. But he got there anyway!

At one point Riker and Troi are walking down the corridor and Troi giggles unprompted and like honestly, yeah same girl.

I forgot Picard's a horsegirl! As if his character wasn't redeemed enough in this episode already!

I also love that Data is entirely driven by emotions here, like literally everything he does in this episode, it's all emotion. Not a "oh in this scene he is clearly happy or heartbroken" or whatever, every single fucking bit from him answering "Is anybody out there" with a "yes" is entirely emotions. Love that for him. I do find it a bit weird that At the end Picard goes all "well you are a bit closer to undersatnding huamnity by experiencing remembrance and regret." Like you wouldn't say that to a child that just had their first friendship break-up or something, right? Like, yes, it's cool that Data got to experience these things for the first time, but it's odd to frame them as steps on the path to humanity, when we would never frame them as such for any other being that experiences them. Data is a human being admit it already you cowards!

Also love that Pulaski is the one to openly acknowledge and validate it! Hell yeah character arcs!

Okay I was so cought up in everything else, but Wesley is great in this too! He has his first "command", leading a team of scientists to analyse the weird shit happening in this sector! And he's a bit insecure about commanding people who are older and more experienced than him, and then Riker gives him a pep talk, and then he gets better at it. Also loved that Wesley immediately questioned the hierarchies in place, good lad, Wes!

When Wesley approaches Riker for help in Ten Forward, Riker tells his companion that it's a "family emergency" and like. I never thought about that. But Riker is like a great ersatz father for Wesley, and a much better choice than Picard (who I think gets stereotyped into the role due to his closeness to Jack and shipping with Beverly. There's even fan theories out there that actually Picard is Wesley's biological father. No idea why, but they're there). Riker's the one who pushes for Wesley to become an acting ensign, he supports him every step of the way, he saves his life in Justice, And they both have some stuff going on with their own fathers (Riker's sucks and Wesley's is dead). Picard shares a connection with Wes' mother and that one conversation about not getting into the Academy first try. Which is nice, but now that I'm looking at it like this I really wanna see more of Riker mentoring Wes.

This is kind of funny to watch post-Discovery, with Saru's backstory basically being similar to Sarjenka's in that they're both from a pre-warp planet and figure out how to communicate with a Starfleet Lieutenant Commander.

Also, this is the longest Trek episode yet, at eight weeks, it's on par with TOS's The Paradise Syndrome.

I think this is also the first notable example of the TNG crew going rogue, with them clearly violating the prime directive, having Riker beam Data down instead of O'Brien, so that only Riker would be implicated in the crime and so on.

Sarjenka really could have used a lesson in internet safety, because you are not supposed to tell strange androids your family details! It could have been Lore instead of Data!

[evidence-based-activism]

Men Underestimate and Women Overestimate Their Own Sexual Violence

Time for an excellent new (2024) article "Gender Differences in Sexual Violence Perpetration Behaviors and Validity of Perpetration Reports: A Mixed-Method Study".

What this study did:

This study asked 23 men and 31 women to "think out loud while privately completing [the Sexual Experiences Survey-Short Form Perpetration (SES-SFP) survey] and to describe (typed response) behaviors that they reported having engaged in on the SES". The researchers asked anyone who "reported no such behavior ... to describe any similar behaviors they may have engaged in". They then analyzed differences in the quantitative responses (numerical values on the SES) and the qualitative responses (written descriptions and think-aloud audio).

What this study found (broad strokes):

Men’s sexual violence (SV) perpetration was more frequent and severe than women’s

Men’s verbal coercion was often harsher in tone and men more often than women used physical force (including in events only reported as verbal coercion on the SES)

Women often reported that their response to a refusal was not intended to pressure their partner or obtain the sexual activity*

Two women also mistakenly reported experiences of their own victimization or compliance (giving in to unwanted sex) on SES perpetration items, which inflated women’s SV perpetration rate

Quantitative measurement can miss important qualitative differences in women and men’s behaviors and may underestimate men’s and overestimate women’s SV perpetration

*This phrasing is poor (in my opinion) the authors are emphasizing genuine differences in men and women's reported behavior for ambiguous situations (not just their internal intent). Specifically, women would endorse responses for behaviors that (most) people would not actually consider a form of sexual violence. For example, women often indicated that the behaviors they were reporting were all pre-refusal (i.e., the women stopped and respected when their partner said no/told them to stop). Other "seducing" behaviors (e.g., kissing/touching) were also reported by women because their partner ultimately refused. Men did not report these types of behaviors, which the authors suggest is possibly because women may be more likely to remember experiences where they wanted to engage in sex with someone who did not because this violates social norms. It's also possible that men are more likely to consider these behaviors acceptable provided they stop when refused. (Ironically this suggests that the anti-feminist hyperbole that people will start recording "normal sexual interactions" as violence ... has only affected women.)

Lots more details below the cut (I use a mix of - unmarked - quotes and paraphrasing):

Quantitative data

The overall prevalence of sexual perpetration of significantly inflated due to intentional over-sampling of likely perpetrators (particularly female perpetrators). This is reasonable because the authors are interested in examining differences among self-reported perpetrators, not in establishing incidence/prevalence rates.

Even without taking the qualitative aspects into consideration (i.e., looking only at the quantitative data), men reporting SV perpetration reported more frequent offenses than women (re-offended more often). Men were also more likely to report more severe acts of violence (per the original tactic-act, the tactic specific, and sexual act specific continua).

Differences in severity identified via qualitative analysis

Men’s verbal coercion was more often stronger; more deceptive, persistent, or intimidating; or otherwise harsher in tone (e.g., "She kept refusing to do anything with me. I remember saying to her “just cause you’re on your period doesn’t mean I can’t get head.” I then remember repeating my intentions with her and almost gaslighting her and making her feel that she must not love me."). Proportionally more men described continually asking or persisting after repeated refusals, getting angry, telling lies, making false promises, and trying to make their partner feel guilty.

Women’s verbal coercion was predominantly expressing disappointment or pouting after a single refusal (e.g., “I got upset and said whatever and rolled over the opposite way”)

Also a difference in intent that could only be identified in the qualitative data. 35% of women who perpetrated explicitly said they had not intended to pressure their partner, change their partner’s mind, or obtain the sexual activity after their partner refused (e.g., "I respected him not trying to do anything further, though, and did not attempt anything further."). No men explicitly said they had not intended to pressure their partner or obtain the sexual activity and [men] more often than women explicitly said that they had intended to (e.g., "I think it was one time where I just kept pressuring . . . Didn’t happen, but the pressure was there, that’s for sure. I definitely asked more than a couple times.")

A few of women’s SV perpetration behaviors appeared more like attempts to advocate for equity in their own sexual pleasure or to stick up for themselves in response to a partner’s coercion (e.g., "I really love receiving oral sex. But sometimes my partner ignores that and directly goes to the penetration. So, I stop him and make him do it because I also feel like being properly aroused to get a better sexual experience.")

False negatives

Some participants that did not mark any of the perpetration items still described similar experiences. Most were not coercive (e.g., asking and “respecting” a refusal, clarifying an unclear refusal) but a couple were clear false negatives. There appears to be an issue with some behaviors not clearly fitting into any of the described categories (e.g., Even the physical force SES items refer only to more extreme force (holding down, pinning arms, having a weapon).)

There were many more cases where a less severe offense was marked (i.e., coded as a true positive for perpetration but for incorrect offense in severity analysis). Specifically, men reported only verbal coercion but then described physical behaviors, so the tactic report was incorrect or incomplete (e.g., "We were experimenting with different things and I did not necessarily ask for their consent before putting my finger in their butt." was coded by one man as verbal coercion).

False negative may have occurred, in part, because behaviors that were themselves no different than those performed in consensual sex were not adequately captured. This is a problem given that previous qualitative research has also found that initiating or going ahead with penetration without asking or following a refusal is a common SV perpetration behavior used by men (i.e., this type of behavior may be recorded as either a false negative or a less severe offense in quantitative scales).

When women reported verbal coercion only, but then described initiating sexual acts without asking, they almost always initiated non-penetrative sexual acts in contrast to men who more often described penetrative sexual acts without asking.

The SES may underestimate use of physical force and, especially, men’s rape and attempted rape.

False positives

Some participants reported perpetration on the SES that their description showed was not forceful, coercive, or engaged in without consent or following a refusal. Men explained that they did not engage in the behavior, misread or misinterpreted the SES question, or clicked the wrong response. Some women reported these same problems, but two "were reports of victimization or giving in to unwanted sex" (i.e., mistakenly reported victimization as perpetration).

Notably, three out of the four men with false positives reported other instances of SV perpetration on the SES whereas two of the four women with false positives did not report other perpetration and, therefore, inflated women’s perpetration rate.

Taken together, our analysis of false negatives and false positives suggests that the SES likely underestimates men’s SV perpetration and overestimates women’s perpetration.

This doesn't even account for instances reporting no intent to perpetrate (as described above). But the fact that many women reported no intent may further support the conclusion that women overreport or are more likely to remember and report because their coercion violates social expectations

Verbalized thought processes

In general, most participants appeared to understand and interpret the SES as intended

But there was evidence that the distinction between attempted and completed acts on the SES may be unclear for some respondents (e.g, one woman said "I also don’t understand what they mean by “tried.” Like does this mean that . . . You simply spoke to them, and they said no? Does this mean that you were engaged in an act and they pushed you off? Or does this mean that something disrupted you? So, this question doesn’t seem very clear to me.")

Second, participants used different items on the SES to report having used a specific category of tactic that is not mentioned in the measure. For example, some participants described kissing and sexually touching their partner without asking to try to arouse them and reported this as verbal tactics to obtain non-penetrative sexual contact. This may have underestimated attempted and completed sexual coercion (because the intent was to engage in penetrative sex). It may also have overestimated non-consensual non-penetrative sexual contact category (the most frequent category for female offenders) since research also finds that partners often use nonverbal cues including kissing and touching to communicate about sexual interest.

There was also confusion about the meaning of “getting angry” or "showing displeasure". Some participants (particularly women) indicated these could refer to internal feeling as opposed to external expression or be a “normal human reaction to . . . feeling rejection” that does not necessarily include a purposeful attempt to manipulate.

Other problems: (1) confusion on if intoxication only applied to alcohol, (2) too many tactics listed in a single question resulting in confusion, (3) participant frequency estimates were rough estimates likely contributing to a significant underestimation problem, (4) participants wouldn't endorse items that specified "without consent" even if they later described coercive behaviors suggesting different phrasing may be needed, (5) participants reported shock at the severity of the tactics asked about, which may indicate SV is not normalized among non-perpetrators or may indicate that less severe tactics are not being captured

Concerning (4) above: Other research indicates that while conceptually narrower, asking about behaviors done after someone resisted or indicated “no” (i.e., post-refusal persistence) results in higher rates of self-reported SV perpetration than asking about behaviors done without consent or when the other person did not want to.

Citation: Jeffrey, Nicole K., and Charlene Y. Senn. “Gender Differences in Sexual Violence Perpetration Behaviors and Validity of Perpetration Reports: A Mixed-Method Study.” The Journal of Sex Research, Feb. 2024, pp. 1–16. DOI.org (Crossref), https://doi.org/10.1080/00224499.2024.2322591.

[lazyyogi]

My Upcoming Research Study: The Effects of Yogic Breathing on Chronic Sinus Symptoms

This week I have been putting together a presentation that I will be giving about my new research project. It's a study of a yogic breathing technique called Bhramari Pranayama as an adjunctive treatment for people with chronic nose and sinus issues.

Essentially the premise is this:

Our sinuses produce nitric oxide, which then in turn effects our nasal airway and our lungs. The nitric oxide can improve nasal airflow, up-regulate mucociliary clearance, and enhance anti-viral immune activity. Additionally, humming actually temporarily increases the amount of nasal nitric oxide released by about 15 fold. Therefore my study is intended to examine the effects of this pranayama technique that utilizes humming on patients with chronic nose and sinus symptoms.

Typically for patients with these symptoms, we start them out on a steroid nasal spray (flonase) as well as sinus irrigations (something like a neti pot).

I always hear from people outside the medical field about how no one studies these things--non-pharmacologic interventions, the beneficial effects of non-proprietary supplements, or other alternative medical options. People often think that if big pharma (or someone expecting to profit) isn't paying for a study, it cannot happen.

This really isn't true likely 90-99% of the time. The problem isn't funding. I'm conducting a prospective randomized control study with human subjects in order to evaluate the benefits of yogic breathing for patients--something that if found to be helpful will bring in no additional monetary profit for anyone. How much will my study cost? $0.

But do you know what it did require?

Two things: interest and opportunity.

Firstly I, a resident physician, had an idea. I learned about nasal nitric oxide and thought it was cool. I read about how humming has a bolusing effect by transiently increasing nitric oxide output by 15x. And then I recalled that there are pranayama techniques that utilize humming.

With my interest piqued, I spoke with one of my bosses, an attending physician at an academic medical center. He's the head of our Rhinology and Skull Base Neurosurgery division and he is cool as hell. He's all about healthy lifestyle and benefiting patients as much as possible. He loved the idea immediately.

And lastly we roped in a medical student. Med students are very helpful with doing the grunt work of collecting the data into spreadsheets, running the statistical analyses and such. Sometimes they bring some excellent ideas of their own as well. In return for their work, med students are often given a significant portion credit upon publication of the study and this allows them the opportunity to add some scholarly publications to their CV. I don't really need more publications under my name, but they do.

My point with sharing all of this is that people often claim there are health benefits to doing or imbibing certain things but that they'll never be studied because there's no money to be made. And it may be true that private companies such as those in the pharmaceutical industry may not have such interest; their existence in a capitalistic economy relies on profitability. But this is part of why academic institutions are so important--because learning and discovery is part of the essential mission there. Profit doesn't dictate their avenues of research.

When it comes to the study and validation of alternative/complimentary medicine, the focus really needs to be on raising awareness and interest. Talk to your doctors, nurses, physician assistants, etc. The good ones listen. The younger they are, the more likely they are to be open-minded about it too (the older ones are hit or miss--some are so cool and some are very old school).

Just some errant thoughts this week as I work on my slide deck.

LY

[ettawritesnstudies]

in the tags on a recent post you said your day job is "mind numbingly simple" do you know if that's common of chemical engineering jobs?

(i am currently pursuing a chemical engineering degree and honestly don't know that much about chemical engineering jobs. but i would not mind a simple job that gives me mental capacity left to write at the end of the day)

So it strongly depends on the kind of engineering work you end up going into and any job will vary in complexity on a day to day basis but with a chemical engineering job you have a lot of different options!

Specifically I'm a R&D Applications Engineer/Technical Customer Service in a polymer science role for a big international corporation so I'm working with existing products in a company and figuring out how to make them work for customers who are having issues.

What this looks like on a project to project basis is that we get an email from the customer or the plant outlining the problem and what kind of material they're sending us to test, I design the experiments we need to do to validate all the variables and properties, and then I spend a few weeks in the lab churning out data, then plug it into an Excel spreadsheet, crunch the numbers, throw that in a PowerPoint, and send it off to the relevant personnel.

The mind numbingly simple part is the standing in lab running through tests because it's hands-on labor that requires very little thinking once you've established your parameters. I usually just put on an audiobook or a podcast to kill time. The design of experiment can get somewhat complex and you have to be very good at time management if you have multiple projects with time sensitive lab components going on at once, but the number crunching has never required anything more complicated than 10th grade algebra. I'm not doing much chemical formulation either, just following established recipes and procedures within my company, but I'm learning more specific stuff as I go.

Now I'm only a year out of college and I've never had an internship or anything that WASN'T in a non-lab setting, so I can't speak to how something like a Process Engineer spends their time. I knew I didn't like being out on the plant floor because it's often Loud and Dirty and Sensory Overload so I tried to avoid applying for those roles. You learn a lot more about the production that way though, just not my cup of tea. You also have chemical engineers who design entire chemical plants and control systems (which is very very cool and important but I was bad at those classes lmao). Some also go the biochemical angle and get into pharmaceuticals but medicine scares me.

That's just my personal take but I encourage you to talk to your professors and upperclassman and see what they have to say! Career fairs >>>>>>> linked in for getting anywhere in this hell of a job market if your school has them and I hope you have a better time of it than I did during the COVID times. 🫡

[askvectorprime]

Dear Vector Prime, have you ever spent time with and helped Nightbeat crack a big case?

Dear Sympathetic Sleuth,

Why yes, on many occasions. I've found that we are a most excellent team—though on occasion while assessing the evidence, he must remind me of important details such as "time flows from past to future" and "the culprit probably isn't a multiversal singularity".

Most recently, he asked for my support for the "Matter of the Missing Metroplex Messages" case. Hoist and Grapple had completed the blueprints to integrate Metroplex into Autobot City, but those plans had suddenly vanished from Teletraan-I without a trace. Optimus Prime put Nightbeat on the case, and he interviewed everyone who'd accessed Teletraan in the previous week, including Sideswipe ("I don't really know what a blueprint is. Is that a new kind of rubsign?"), Sparkplug (valid alibi—he’d been playing with young Daniel at the time), and Cliffjumper, who only noted in his paranoid way that some of Teletraan's keys felt "grimier" or "not smooth like usual".

After many frustrating, fruitless days of searching, Nightbeat called me in, hoping that my time powers might help resolve the puzzle. Borrowing Nightbeat's magnifying glass and his fedora, I went back in time to when the data was last known to be present on the computer, and I saw nothing out of the ordinary—except for tiny red and blue smudges on Teletraan's keys. Empty-handed and feeling insecure about my detective skills, I returned to the present and showed Nightbeat the photograph I took.

Nightbeat's optics flared behind his visor, and with a big grin, he told me that he'd solved the case. Walking over to Teletraan, he bonked the console firmly—and the console winced! It detached from the rest of the Ark, transforming into Mainframe, who had spray-painted himself with a nice (and cheap, as Cliffjumper later noted) golden sheen to match Teletraan-I. For all the good he was at being a computer, he was not adept at spray-painting himself at first. I had helped after all!

Mainframe explained that he didn't want to leave the Ark; it was quieter than the bustling Autobot City and wanted all of his friends to stay too. Optimus, who was both kind and just, sentenced Mainframe to six months of community service at Metroplex in assistance to Hoist and Grapple, in addition to six months of daily maintenance of Teletraan-I, which he had hidden away in storage. In the end, everyone won: Mainframe learned to appreciate Metroplex while being allowed to live at the Ark, Nightbeat got to brag about solving one more case, and as thanks, I got to keep his fedora!

[benny-notes]

Shin Sekai Yori and the Fear of the Unknown

Shin Sekai Yori by A-1 Pictures (2012 – 2013) was the latest feature in my anime exploration. The visuals of the show were excellent, especially the way that the animators played with light. Scenes set at dusk with dynamic skies and silhouetted features captured the look, but more importantly the feel of twilight. Scenes at night and in caves captured the way that space closes in on you when light wanes adding drama and suspense to the story.

Shin Sekai Yori is more than just pretty pictures. The series comments insightfully on deep issues including class dynamics, power and what it means to be human. Another prominent theme addressed in the series is fear – specifically fear of the unknown. There are some dangers that are easy to understand. Tigers are deadly, driving above the speed limit increases risk and certain places at certain times have higher risk for crime. However, when violence erupts in places, times and at the hands of those we don’t expect primal fears activate as society tires to understand why something so horrible could happen.

The anxieties surrounding unexplainable violence in real life Japan are mirrored in Shin Sekai Yori. Episode 4 reveals that the start of the disruption in social order after people were discovering to use their cantus was sparked by “Boy A”, a young child who went on a killing spree, breaking into homes and committing brutal crimes. This a reference to the crimes committed by Shōnen A, a young murderer in actual Japan in the late 1990’s (Arai, 2000). Both events shook society and in the universe of Shin Sekai Yori strong action was taken eventually culminating genetically modifying humanity to have an automatic physical response that prevents them from harming others.

Possibly more terrifying than unexpected violence is when the actions that we take to control the situation are not completely effective. In Shin Sekai Yori, some children, known as Fiends, are immune to their genetically imposed killing instincts. SSY’s main character Saki learns the truth about Fiends when she is told the story of a young boy who is obsessed with dark ideas. One day the boy goes on a massacre. His lack of restraint coupled with the rest of societies physical limitation from harming him is a worst case scenario of an out of control threat. Eventually the killer is defeated, but even stronger measures are taken to attempt to eliminate the chances that someone will develop into a Fiend. Anyone even remotely displaying troublesome traits will be eliminated. It mostly works, but the show struggles with the idea of the cost of these measures. How many innocent lives are lost in the pursuit of public safety? What is the acceptable margin of error when trying to prevent a catastrophe?

This dilhemma exists in the real world too. One example is how artificial intelligence and machine vision are already being used to identify possible school shooters. Max Zahn’s ABC News article Can Artificial Intelligence Help Stop Mass Shootings? discusses the use and reaction to these types of systems. Fortunately a key difference exists between these systems and the pre-emptive action taken by society in SSY. The systems being used today only flag actions as a response to an attack already happening. The article mentions that some are concerned about innocent people being flagged as threats. Someone’s life being tainted by a false identification is a valid concern. However, the problem isn’t that the data is being collected or analyzed in an automated way, it’s society’s reaction to the data that is collected. Instead of using the surveillance data to punish, it should be used to intervene and help people before the turn to violence. The solution isn’t punishing an individual, it’s creating systemic fixes.

References:

Arai, A. (2000). The "Wild Child" of 1990s Japan. The South Atlantic Quarterly, 99(4), 841-863.

Zahn, M. (2023, February 2). Can Artificial Intelligence Help Stop Mass Shootings? ABC News. https://abcnews.go.com/Technology/artificial-intelligence-stop-mass-shootings/story?id=96767922

[xaltius]

The Dos and Don’ts of AI & ML in Digital Marketing

Artificial intelligence (AI) and machine learning (ML) are revolutionizing the digital marketing landscape, offering unprecedented opportunities for personalization, automation, and optimization. However, like any powerful tool, AI and ML must be wielded wisely. This blog outlines the dos and don'ts of leveraging these technologies effectively in your digital marketing strategies.

The Dos:

Do Define Clear Objectives: Before implementing any AI/ML solution, clearly define your marketing goals. What are you trying to achieve? Increased conversions? Improved customer engagement? Specific objectives will guide your AI/ML strategy and ensure you're measuring the right metrics.

Do Focus on Data Quality: AI/ML algorithms are only as good as the data they are trained on. Prioritize collecting clean, accurate, and relevant data. Invest in data cleansing and validation processes to ensure the reliability of your AI-driven insights.

Do Start Small and Iterate: Don't try to implement everything at once. Begin with a specific use case, such as automating social media posting or personalizing email campaigns. Test, refine, and iterate on your approach before scaling up.

Do Prioritize Personalization: AI/ML excels at personalization. Leverage these technologies to create tailored content, product recommendations, and offers for individual customers based on their behavior, preferences, and demographics.

Do Embrace Automation: AI can automate repetitive tasks, freeing up marketers to focus on strategic initiatives. Identify areas where AI can streamline workflows, such as ad campaign optimization, content curation, or customer service interactions.

Do Focus on Transparency and Explainability: Understand how your AI/ML models work and ensure they are transparent and explainable. This is crucial for building trust and addressing ethical concerns.

Do Measure and Analyze Results: Track the performance of your AI/ML-driven marketing campaigns and analyze the data to identify areas for improvement. Use data to inform your decisions and optimize your strategies.

Do Stay Updated: The field of AI/ML is constantly evolving. Keep up with the latest advancements, new tools, and best practices to ensure you're maximizing the potential of these technologies.

The Don'ts:

Don't Treat AI as a Magic Bullet: AI/ML is a powerful tool, but it's not a magic solution. It requires careful planning, implementation, and ongoing management. Don't expect overnight results without putting in the effort.

Don't Neglect Human Oversight: While AI can automate tasks, it's essential to maintain human oversight. Human judgment is still crucial for strategic decision-making, creative development, and ethical considerations.

Don't Over-Rely on Automation: While automation is beneficial, don't over-automate to the point where you lose the human touch. Maintain a balance between automation and human interaction to ensure a personalized and engaging customer experience.

Don't Ignore Ethical Implications: AI/ML raises ethical concerns about data privacy, bias, and transparency. Be mindful of these issues and ensure that your AI-driven marketing practices are ethical and responsible.

Don't Forget About Data Security: Protecting customer data is paramount. Implement robust security measures to safeguard your data from unauthorized access and breaches.

Don't Be Afraid to Experiment: AI/ML is a field of experimentation. Don't be afraid to try new approaches, test different algorithms, and learn from your mistakes.

Don't Underestimate the Importance of Training: Proper training is essential for effectively using AI/ML tools and understanding their capabilities and limitations. Invest in training for your marketing team to ensure they have the skills they need to succeed.

Digital Marketing & AI Certification Program: Your Path to AI-Powered Marketing Mastery

Want to become a sought-after digital marketing professional with expertise in AI and ML? Consider enrolling in a Digital Marketing & AI Certification Program. These programs provide comprehensive training on the latest AI/ML tools and techniques, preparing you to leverage the power of these technologies in your marketing strategies. You'll learn how to:

Develop and implement AI/ML-driven marketing campaigns.

Analyze data and generate actionable insights.

Choose and use the right AI/ML marketing tools.

Address ethical considerations related to AI/ML in marketing.

Conclusion:

AI and ML are transforming the landscape of digital marketing, offering unprecedented opportunities for growth and innovation. By following these dos and don'ts, marketers can harness the power of these technologies effectively and responsibly, driving better results and achieving their marketing goals. The future of digital marketing is intelligent, and it's powered by AI and ML.

[justinspoliticalcorner]

Dan Pfeiffer at The Message Box:

Despite minimal evidence, a full-bore effort is underway to make Democrats think Kamala Harris is losing the election. This effort is abetted by preternaturally anxious Democrats expressing their concerns on social media. I have written a lot recently about the vibe shift in Democrats after Kamala Harris’s nomination. I don’t think the data validates such an extreme shift in emotions. However, I won’t shame anyone for being on edge these last two weeks. The stakes are enormous. Reproductive freedom, health care, democracy, and the planet are on the line in an election that could be decided by the weather in a random suburban Wisconsin county. Long time readers of this newsletter know that I am not exactly a glass-half-full kinda guy. I tend to hang out on the dark side, but I will resist that temptation. How one feels over the next two weeks is a choice. There is nothing cool or savvy about predicting a loss. More importantly, thanks to you-know-who, I believe that hope is a powerful force. If you want to be hopeful over the next few weeks, I will give you some reasons — borne of data and experience — to justify an optimistic approach.

Before you read another word, let me offer some critical stipulations:

I am not making a prediction (I’m never doing that again after 2016);

I truly have no idea what will happen (Neither does anyone else);

I acknowledge that the polls might have moved slightly in Trump’s direction over the last few weeks; and

The political environment and the Electoral College favor Trump on paper.

When writing for the Internet, you are incentivized to tell people what they want to hear. I try to resist that temptation, but I have upset many of you over the years with negative takes — especially after the Biden-Trump debate. However, the national political discourse lacks arguments for why Harris will win. So, with those painful caveats in mind, here’s the optimistic case for Kamala Harris.

2. Harris Has a (Slightly) Easier Path to 270

The Electoral College has a Republican bias. According to the New York Times polling average, five of the seven battleground states are within less than a point and no candidate holds a lead in any state greater than two points. There is no easy path to 270, but Harris’s is slightly easier. If she wins Pennsylvania, Wisconsin, Michigan and the second congressional district of Nebraska, she will get 270 electoral votes and be the next President of the United States. [...]

3. Trump is Not Closing Strong

There is a reason why Kamala Harris used the debate to invite people to attend or watch a Trump rally. It’s the same reason why Harris is now playing video clips of her opponent at her rallies. Trump is a disaster on the public stage. And he’s getting worse as the election nears.  [...]

4. Harris Runs the Better Field Operation

Kamala Harris has invested a tremendous amount of time and money into building a massive field operation to persuade undecided voters and turn out the base. Just last weekend, Harris campaign volunteers made five million phone calls to voters — five million calls in one weekend! The Trump campaign has largely outsourced their field operations to Elon Musk and other outside entities. 

Dan Pfeiffer makes an excellent case for being optimistic towards Kamala Harris’s chances of winning the Presidency.

[govindhtech]

AI Frameworks Help Data Scientists For GenAI Survival

AI Frameworks: Crucial to the Success of GenAI

Develop Your AI Capabilities Now

You play a crucial part in the quickly growing field of generative artificial intelligence (GenAI) as a data scientist. Your proficiency in data analysis, modeling, and interpretation is still essential, even though platforms like Hugging Face and LangChain are at the forefront of AI research.

Although GenAI systems are capable of producing remarkable outcomes, they still mostly depend on clear, organized data and perceptive interpretation areas in which data scientists are highly skilled. You can direct GenAI models to produce more precise, useful predictions by applying your in-depth knowledge of data and statistical techniques. In order to ensure that GenAI systems are based on strong, data-driven foundations and can realize their full potential, your job as a data scientist is crucial. Here’s how to take the lead:

Data Quality Is Crucial

The effectiveness of even the most sophisticated GenAI models depends on the quality of the data they use. By guaranteeing that the data is relevant, AI tools like Pandas and Modin enable you to clean, preprocess, and manipulate large datasets.

Analysis and Interpretation of Exploratory Data

It is essential to comprehend the features and trends of the data before creating the models. Data and model outputs are visualized via a variety of data science frameworks, like Matplotlib and Seaborn, which aid developers in comprehending the data, selecting features, and interpreting the models.

Model Optimization and Evaluation

A variety of algorithms for model construction are offered by AI frameworks like scikit-learn, PyTorch, and TensorFlow. To improve models and their performance, they provide a range of techniques for cross-validation, hyperparameter optimization, and performance evaluation.

Model Deployment and Integration

Tools such as ONNX Runtime and MLflow help with cross-platform deployment and experimentation tracking. By guaranteeing that the models continue to function successfully in production, this helps the developers oversee their projects from start to finish.

Intel’s Optimized AI Frameworks and Tools

The technologies that developers are already familiar with in data analytics, machine learning, and deep learning (such as Modin, NumPy, scikit-learn, and PyTorch) can be used. For the many phases of the AI process, such as data preparation, model training, inference, and deployment, Intel has optimized the current AI tools and AI frameworks, which are based on a single, open, multiarchitecture, multivendor software platform called oneAPI programming model.

Data Engineering and Model Development:

To speed up end-to-end data science pipelines on Intel architecture, use Intel’s AI Tools, which include Python tools and frameworks like Modin, Intel Optimization for TensorFlow Optimizations, PyTorch Optimizations, IntelExtension for Scikit-learn, and XGBoost.

Optimization and Deployment

For CPU or GPU deployment, Intel Neural Compressor speeds up deep learning inference and minimizes model size. Models are optimized and deployed across several hardware platforms including Intel CPUs using the OpenVINO toolbox.

You may improve the performance of your Intel hardware platforms with the aid of these AI tools.

Library of Resources

Discover collection of excellent, professionally created, and thoughtfully selected resources that are centered on the core data science competencies that developers need. Exploring machine and deep learning AI frameworks.

What you will discover:

Use Modin to expedite the extract, transform, and load (ETL) process for enormous DataFrames and analyze massive datasets.

To improve speed on Intel hardware, use Intel’s optimized AI frameworks (such as Intel Optimization for XGBoost, Intel Extension for Scikit-learn, Intel Optimization for PyTorch, and Intel Optimization for TensorFlow).

Use Intel-optimized software on the most recent Intel platforms to implement and deploy AI workloads on Intel Tiber AI Cloud.

How to Begin

Frameworks for Data Engineering and Machine Learning

Step 1: View the Modin, Intel Extension for Scikit-learn, and Intel Optimization for XGBoost videos and read the introductory papers.

Modin: To achieve a quicker turnaround time overall, the video explains when to utilize Modin and how to apply Modin and Pandas judiciously. A quick start guide for Modin is also available for more in-depth information.

Scikit-learn Intel Extension: This tutorial gives you an overview of the extension, walks you through the code step-by-step, and explains how utilizing it might improve performance. A movie on accelerating silhouette machine learning techniques, PCA, and K-means clustering is also available.

Intel Optimization for XGBoost: This straightforward tutorial explains Intel Optimization for XGBoost and how to use Intel optimizations to enhance training and inference performance.

Step 2: Use Intel Tiber AI Cloud to create and develop machine learning workloads.

On Intel Tiber AI Cloud, this tutorial runs machine learning workloads with Modin, scikit-learn, and XGBoost.

Step 3: Use Modin and scikit-learn to create an end-to-end machine learning process using census data.

Run an end-to-end machine learning task using 1970–2010 US census data with this code sample. The code sample uses the Intel Extension for Scikit-learn module to analyze exploratory data using ridge regression and the Intel Distribution of Modin.

Deep Learning Frameworks

Step 4: Begin by watching the videos and reading the introduction papers for Intel’s PyTorch and TensorFlow optimizations.

Intel PyTorch Optimizations: Read the article to learn how to use the Intel Extension for PyTorch to accelerate your workloads for inference and training. Additionally, a brief video demonstrates how to use the addon to run PyTorch inference on an Intel Data Center GPU Flex Series.

Intel’s TensorFlow Optimizations: The article and video provide an overview of the Intel Extension for TensorFlow and demonstrate how to utilize it to accelerate your AI tasks.

Step 5: Use TensorFlow and PyTorch for AI on the Intel Tiber AI Cloud.

In this article, it show how to use PyTorch and TensorFlow on Intel Tiber AI Cloud to create and execute complicated AI workloads.

Step 6: Speed up LSTM text creation with Intel Extension for TensorFlow.

The Intel Extension for TensorFlow can speed up LSTM model training for text production.

Step 7: Use PyTorch and DialoGPT to create an interactive chat-generation model.

Discover how to use Hugging Face’s pretrained DialoGPT model to create an interactive chat model and how to use the Intel Extension for PyTorch to dynamically quantize the model.

Read more on Govindhtech.com

[stagnate-03]

Your Guide to Success in Quantitative Research: 8 Practical Tips

Quantitative research plays a crucial role in fields like social sciences, business, healthcare, and education. It provides numerical data that can be analyzed statistically to identify patterns, relationships, and trends. However, excelling in quantitative research requires more than just crunching numbers.

1. Start with a Clear Research Question

The foundation of any successful research is a well-defined research question. This question guides the entire study, determining your methodology, data collection, and analysis. Ensure that your research question is specific, measurable, and aligned with the purpose of your study.

For example, instead of asking, "How do students perform in school?" a clearer question might be, "What is the relationship between study hours and academic performance in high school students?"

Tip: Before starting, spend time refining your question. This will save you time and effort during the research process.

2. Choose the Right Research Design

Quantitative research can take many forms, including experiments, surveys, and observational studies. Choosing the right design depends on your research objectives and the type of data you need. Are you testing a hypothesis?

Tip: Match your research design with your objectives to ensure you’re collecting the right kind of data.

3. Use Valid and Reliable Instruments

The tools you use to gather data—whether they’re questionnaires, tests, or measuring devices—must be both valid (measuring what you intend to measure) and reliable (producing consistent results over time).

Tip: If you’re developing your own instrument, pilot it first with a small group to check its validity and reliability. If using an existing tool, review past studies to confirm it works well for your research population.

4. Select an Appropriate Sample Size

A common mistake in quantitative research is working with a sample size that’s too small, which can lead to unreliable or inconclusive results. On the other hand, excessively large samples can waste resources. To avoid these pitfalls, conduct a power analysis to determine the optimal sample size for your study.

Tip: Use tools like G*Power to calculate the right sample size based on your research goals and the expected effect size. This ensures your findings are statistically significant and applicable to a larger population.

5. Ensure Random Sampling for Representativeness

Your findings will only be meaningful if your sample represents the broader population you’re studying. Random sampling ensures that every individual in the population has an equal chance of being selected, reducing bias and increasing the generalizability of your results.

Tip: Use random sampling methods (e.g., simple random sampling, stratified random sampling) to ensure your data is as representative as possible.

6. Minimize Bias in Data Collection

Bias can creep into any research process, affecting the accuracy and fairness of your results. To reduce bias, carefully design your data collection process. For example, avoid leading questions in surveys and standardize how data is collected across all participants to prevent interviewer or observer bias.

Tip: Blind or double-blind studies can help minimize bias, especially in experiments where participants or researchers might be influenced by expectations.

7. Analyze Data Properly with the Right Statistical Tools

Once you’ve collected your data, the next step is analysis. Choosing the right statistical tests is essential to interpret your findings correctly. Descriptive statistics (like means and frequencies) give a broad overview, while inferential statistics (like t-tests, chi-squares, or regression analyses) help determine whether your findings are statistically significant.

Tip: If you’re unsure which test to use, consult a statistician or use resources like statistical decision trees to guide your choice based on your data type and research questions.

8. Interpret Results with Context and Caution

After analyzing your data, it’s tempting to jump to conclusions. However, quantitative research is not just about the numbers; it’s about what those numbers mean in context. Always interpret your results in relation to your research question and the existing body of knowledge.

Be cautious when generalizing your findings, especially if your sample size is small or non-representative. Additionally, consider the limitations of your study—were there any confounding variables, measurement errors, or external factors that might have influenced your results?

Tip: Be transparent about the limitations of your study. Acknowledging them strengthens the credibility of your research.

Conclusion

Mastering quantitative research requires attention to detail, a solid understanding of statistical methods, and a commitment to rigor throughout the process. By following these 8 practical tips—starting with a clear question, choosing the right design, using valid instruments, selecting the appropriate sample, minimizing bias, analyzing correctly, and interpreting results carefully—you’ll be well on your way to conducting successful and impactful quantitative research.

Read more: https://stagnateresearch.com/blog/how-to-excel-in-quantitative-research-8-essential-tips-for-success/

Also read: Project Management Service Company

data processing in research services

[roseliejack123]

From Newbie to Ninja: Ace Java Programming with Our Expert Course!

Are you prepared to start a thrilling adventure into the world of coding? Look no further! Our expert course at ACTE institute is here to transform you from a coding newbie into a Java ninja! With benefits, information, and high-quality education, this course is the ultimate stepping stone to enhance your Java coding skills. Let’s dive in and explore what makes our expert course the right choice for you.

Education

Dedicated Instructors: Our course is facilitated by highly experienced and passionate instructors who are experts in the field of Java programming. They’re not only well-versed in the subject matter but also possess exceptional teaching skills, ensuring that you receive the guidance and support needed to excel in your learning journey.

Interactive Learning Community: Learning doesn’t have to be a solitary endeavor. Join our vibrant community of like-minded learners, where you can connect, collaborate, and learn from each other. Engage in discussions, seek help, and share your experiences to enhance your learning even further.

Certification: Upon successful completion of our expert course, you’ll receive a prestigious certificate that validates your expertise in Java programming. This certificate can be showcased to potential employers, demonstrating your commitment to professional growth and your ability to thrive in the ever-evolving world of coding.

Information

Course Structure: Our expert course is structured in a logical and progressive manner, ensuring a smooth learning journey from start to finish. Each module is carefully designed to build upon the previous one, allowing you to grasp complex concepts effortlessly. The course comprises of video lectures, interactive coding exercises, quizzes, and hands-on projects to keep you engaged throughout the learning process.

Prerequisites: While no prior programming experience is required, having a basic understanding of computer science fundamentals will be beneficial. Familiarity with concepts like variables, loops, and conditionals will give you a head start. However, even if you’re a complete beginner, our course will guide you step-by-step, making sure no one gets left behind.

Benefits

Accelerated Learning: Our expert course utilizes cutting-edge pedagogy techniques that ensure a fast and effective learning experience. With our carefully curated curriculum and hands-on exercises, you’ll be able to grasp complex concepts in a fraction of the time it would take with traditional learning methods.

Comprehensive Skills Development: Our course offers a holistic approach to Java programming, covering all essential aspects necessary for becoming a proficient developer. You’ll gain in-depth knowledge of Java syntax, object-oriented programming principles, data structures, algorithms, and design patterns.

Practical Experience: Theory alone is not enough to become a Java programming ace. Our course emphasizes practical application, providing ample opportunities to apply your newfound knowledge through hands-on coding exercises, real-world projects, and coding challenges. This hands-on experience will boost your confidence, allowing you to tackle real-life coding scenarios with ease.

Industry-Relevant Content: Our course is designed by industry experts who have an in-depth understanding of the current trends and demands of Java programming. Rest assured, you’ll be equipped with the most up-to-date knowledge and skills required to excel in the competitive programming landscape.

In conclusion, our expert Java programming course is your passport to becoming a coding ninja. With a host of benefits, comprehensive information, and high-quality education, our course at ACTE institute equips you with the skills and knowledge needed to excel in the Java programming world. Don’t miss this opportunity to embark on a transformative learning journey.

[iabac5115]

Certified Data Scientist: Mastering the Journey with IABAC

By becoming a certified data scientist, you open yourself up to a world of possibilities in the ever-changing world of technology. The International Association of Business Analytics Certifications (IABAC) plays a crucial role in this field by providing a recognized platform for professionals to validate their skills.

At the heart of data science is extracting valuable insights from huge amounts of data. Being certified by IABAC not only showcases your proficiency in handling complex data but also demonstrates your dedication to excellence. The certification process involves rigorous training and evaluation, ensuring that you are well-prepared to tackle practical challenges.

The Certified Business Analytics credential is a  recognized achievement that covers the complexities of both data science and business analytics. This certification indicates that the holder has a solid grasp of how to use data to drive business growth. It signifies not only technical expertise but also the capability to match data strategies with overall business goals.

The IABAC's Business Analytics Certification is an impressive achievement that highlights the importance of using analytics strategically to guide business choices. Individuals who possess this certification have a unique blend of technical skills and business knowledge, making them extremely valuable to organizations seeking data-oriented solutions.

The journey towards certification starts by building a strong foundation in data science principles. Aspiring candidates explore various topics like statistical analysis, machine learning, and data visualization. What makes IABAC stand out is its focus on practical application. The certification process includes hands-on projects, allowing individuals to apply their knowledge in real-world scenarios.

Certified data scientists who complete their training gain a prestigious credential and become valued members of a global community of experts. At IABAC.org, we offer networking opportunities that allow professionals to connect, collaborate, and stay updated on the latest industry trends.

[kylahtillotson]

How I am gathering meaningful data

I am gathering my data using excel. I find this an easy way to stay organized and keep all my data together. I have categories for different pieces of content I want to observe. This allows me to track the number of likes better and allows me to see what content is the most engaging. To gather meaningful data you need to have validity and timeliness. You need to be able to back up what you find with a time stamp. You also want to make sure the data you are collecting is relevant when supporting your research question.