#but even with that...there's still libraries and academic databases

[friendofthecrows]

One of the (many) reasons I think people need to use libraries more is that it would seriously reduce the "need" for chatgpt/similar AIs.

Sometimes a topic IS difficult to research and it CAN be hard to know where to start.

Anecdote: I know someone who, when confronted by a whole lotta irrelevance in their first several online searches, will then plug their whole, wordy, unpolished question with several synonyms per term into an AI and get a fairly clear, if flawed, summary and then they ignore that and ask it for source suggestions and seach terms to learn more about it and look up those seperately. I don't advocate for that, but it shows a usecase I think most of the "people are just using chatgpt to cheat on homework" crowd are ignoring. (And yeah, I agree, a lot of people are using chatgpt to cheat on homework, that happens too.)

Many libraries have research assistants, and even in those that don't, most librarians are more than happy to point you to good sources.

Worried the library is unlikely to have books on the topic you're researching? I've been there, given the number of recent case studies on hyper-specific forensics topics I've written for college. But you know what librarians are great at? Library science. Libraries have computers, and often libraries will have subscriptions/access to some journals and academic databases. Librarians are very, very good at using those databases, and research in general, and they're usually very enthusiastically willing to help, especially if you go on a quiet day.

Fun fact: library computers are also a fairly anonymous way to do research. Libraries might have usage guidelines for their computers to prevent things like people downloaded a massive number of viruses on them, but I promise you the librarians are not judging you. Think of them like the doctors of academia (not to be confused with PhD in Education, that's totally different). Doesn't matter what your research is, ask for help if you need it.

If your search engine is giving you nothing, or if you're stuck, go to a library.

[astriiformes]

Still working on getting everything set up how I like it on Palamedes (the new laptop) and was reminded of my favorite browser extensions everyone should know about -- Unpaywall and Library Extension

Unpaywall automatically searches a database of open-access sources to let you know if an academic article you're looking at is available anywhere for free. (And yes, I know there are other ways of getting your hands on them if there aren't open-access options, but it's an easy, convenient first step in the search!)

Library Extension is kind of similar, except it's an extension that tells you if your local library (or its Hoopla catalogue, or various online sources like Open Library) has a book available in a little box that shows up if you're looking at titles on Amazon, Goodreads, or their less evil counterparts -- Bookshop and Storygraph, respectively. It even has a button that automatically takes you to the page to request it as a hold from the library instead, which I know is amazing for my "I'll look that up later [does not look that up later]" ADHD brain.

I cannot recommend these extensions enough, and the ease and convenience of both of them have definitely helped me read more academics articles and books than I would have otherwise. Definitely check them out if you're not already using them!

[qqueenofhades]

do you have any advice for those in the very early stages of thesis-writing? currently desperately clinging to the mantra of "shitty first drafts," et al

Unfortunately, there is no place where you will more whole-assedly have to embrace the "shitty first draft" mantra than in academic writing, especially in thesis writing, especially if this is your first-ish crack at an advanced and major piece of original research. I'm not sure if this is for an undergraduate senior thesis, a MA-level thesis, or (my true and heartfelt sympathies) a PhD dissertation, but the basic principles of it will remain the same. So there is that, at least. This means that yes, you will write something, you may even feel slightly proud of it, and then you will hand it into your supervisor and they will more or less kindly dismantle it. You have to train yourself to have a thick skin about this and not take it as a personal insult, and if your supervisor is remotely good at their job (not all of them are, alas) they will know how to be tactful about it and not make it feel like a direct and extensive commentary on your private worth as a person. But you will have to swallow it and do what you can, which can include -- if you're the one who has done the research and know that's how you want to present it and/or you are correct about it -- pushing back and having a conversation with them about how you think your original approach does work best. But that will come later. The first step is, yes, to mentally gird yourself to receive critical feedback on something that you have worked hard on, and to understand that no matter how much you grump and grumble and deservedly vent to your friends and so on, implementing the feedback will usually make your piece better and stronger. That is the benefit of working with a trained expert who knows what makes a good piece of research in your particular academic field, and while it doesn't get easier, per se, at least it gets familiar. Be not afraid, etc.

If you're in the writing stage, I assume that you've moved past the topic-selection and general-research stage, but allow me to plump once more the services of your friendly local university library. You can (or at least you can at mine and probably in any decently well-equipped research university) schedule a personal consultation with an expert librarian, who can give you tips on how to find relevant subject databases, create individual research guides (these might already be available on the university library website for classes/general topics), and otherwise level you up to Shockingly Competent Research Superhero. So if you're still looking for a few extra sources, or for someone else who might be reading this and is still in the "how the heck do I find appropriate and extensive scholarly literature for my thesis??" stage, please. Go become a Research Ninja. It's much easier when you have a minion doing half the work for you, but please do appreciate and make use of your university librarian. It's much more effective than haphazard Google Scholar or JSTOR searches hoping to turn up something vaguely relevant (though to be fair, we all do that too), and it's what your tuition dollars are paying for.

Next, please do remind yourself that you are not writing the whole thesis in one go, and to break it down into manageable chunks. It usually does make sense to write the whole thing semi-chronologically (i.e. introduction, lit review, chapter 1, chapter 2/3/4 etc, conclusion), because that allows you to develop your thoughts and make logical connections, and to build on one piece to develop the next. If you're constantly scrambling between chapters and zig-zagging back and forth as things occur to you, it will be harder to focus on any one thought or thread of research, and while you might get more raw output, it will not be as good and will require more correction and revision, so you're not actually hacking yourself into increased productivity. You should also internally structure your chapters in addition to organizing your overall thesis, so it makes sense to draw up a rough outline for section A, section B, section C within the body of a single chapter. This will make you think about why the segues are going in that order and what a reasonably intelligent reader, who nonetheless may not have the specialized knowledge that you are demonstrating for them, needs to move understandably from one section to the next.

Some academics I know like to do an extensive outline, dumping all their material into separate documents for each chapter/paper and kneading and massaging and poking it into a more refined shape, and if that works for you -- great! I'm more of the type that doesn't bother with a ton of secondary outlines or non-writing activity, since that can lead you away from actually writing, but if you need to see the fruit of your research all together in one place before you can start thinking about how it goes together, that is also absolutely the way that some people do it. Either way, to be a successful academic writer, you have to train yourself to approach academic writing in a very different way from fun writing. You do fun writing when you have free time and feel inspired and can glop a lot of words down at once, or at least some words. You do it electively and for distraction and when you want to, not to a set timeline or schedule, and alas, you can't do this for academic writing. You will have to sit your ass down and write even when you do not feel like writing, do not feel Magically Inspired, don't even want to look at the fucking thing, etc. I have had enough practice that I can turn on Academic Writing Brain, sit down, bang something out, sit down the next day and turn on Academic Editing Brain, go over it again, and send it off, but I have been in academia for uh, quite a while. The good news is that you can also automate yourself to be the same way, but the bad news is that it will take practice and genuine time invested in it.

As such, this means developing a writing schedule and sticking to it, and figuring out whether you work best going for several hours without an interruption, or if you set a timer, write for a certain time, then allow yourself to look at the internet/answer texts/fuck around on Tumblr, and then make yourself put down the distraction and go back to work for another set period of time. (I am admittedly horrible at putting my phone away when I should be doing something else, but learn ye from your wizened elders, etc.) You will have to figure out in which physical space you work best, which may not be a public coffee shop where you can likewise get distracted with doing other things/chatting to friends/screwing around on the internet/doomscrolling/peeking at AO3, and to try to be there as often as possible. It might be your carrel in the library, it might be your desk at home, it might be somewhere else on campus, but if you can place yourself in a setting that tells your brain it's time to work and not look at WhatsApp for the 1000th time in a row, that is also beneficial.

Finally, remember that you do not have to produce an absolutely world-beating, stunningly original, totally flawless and perfect piece, even in its final form. Lots of us write very shitty things when we're starting out (and some of us, uh, still write very shitty things as established academics), and you do not have to totally redefine your entire field of study or propose a groundbreaking theory that nobody has heard of or anything like that. A lot of academic work is small-scale and nuanced, filling in spaces on the margins of other things or responding or offering a new perspective on existing work, and it's best to think of it as a conversation between yourself and other scholars. They have said something and now you're saying something back. You don't need to be so brilliant that everyone goes ZOMGZ I HAVE NEVER HEARD OF THAT BEFORE; by its nature that happens very rarely and is usually way out on a limb (extraordinary claims require extraordinary evidence, etc); you just need to continue the dialogue with a reasonably well-constructed and internally plausible piece. So if you think of it that way, and understand that a shitty first draft will usually develop into something that is good and valuable but not SHOCKING NEW REVELATION clickbait hype, you will take some of the pressure off yourself and be more able to shut up that perfectionist voice in your head. However, all of us have some degree of imposter syndrome and it never entirely goes away, so you'll have to manage that too. Etc etc as before, it doesn't vanish altogether, but it gets easier.

And last but not least, though I'm sure I don't have to say this: for the love of fuckin' god, do not use ChatGPT. Even the genuinely shittiest paper in the world that you still worked on researching, organizing, and writing with your own brain is better than that. Trust me.

[historicity-was-already-taken]

Do you have any advice for vetting sources on I/P? I just got a book at a local bookstore and was wondering how I should go about determining any biases?

Great question. I addressed this in my I/P Reading List. I'm pasting below my section on the methodology and source vetting process I followed while compiling this list:

In compiling this list, my process and methodology were as follows: I first wrote down all the relevant works I could think of off the top of my head, all hailing from my fields of Biblical Studies and Modern Jewish History. I then reached out to my friends in Modern Middle Eastern History, Conflict Studies, Jewish History, etc for recommendations, lists, and insight; one of my friends, an ABD PhD candidate in Modern Middle Eastern History, was particularly helpful in helping me understand Israeli historiography, and pointing me towards the best works in the field of Arab Nationalism. After taking their recommendations, I conducted a series of google searches using “‘relevant search term’ site:.edu” to ensure that I received results from only academic domains. From there, I read syllabi for university courses, and examined comprehensive exam reading lists. After that, I searched the catalog of the New York Public Library using any and all relevant search terms, and I also conducted targeted searches on Amazon. By the time I finished, I had a 50 pages filled with book titles. My next step was to divide the list into categories, deleting irrelevant titles and repeats as I went along. After dividing the books into categories, I put each title through a rigorous fact checking process. I checked the publication material for each book to ensure that it was either a. published by an academic press with a built in peer review process, and/or b. written by an academic historian—either a faculty member, or someone with an MA or PhD in history. If I was still unsure after that step, I searched the title in the University of Maryland’s database system, and read the academic (meaning, peer reviewed) book reviews of the title. From there I either kept it on the list or removed it. As I approached the late-Ottoman period, I became extra-critical of relevant titles. At this point, I made sure to read book reviews from at least two academic journals in different fields/subfields to ensure not only the text’s legitimacy, but its ability to hold firm against the scrutiny of scholars in multiple fields. I tended to remove a work if the word “polemic” appeared in the reviews. This said, many of the works on this list, particularly, but not exclusively, in the Post-1948, Arab Nationalism and the Modern Middle East, Conflict Overviews, and Historiography Narratives, Memory and Theory categories will be slanted, or biased. Considering the topic at hand, this reality is both inevitable and perhaps necessary. I do advise you, however, to read any books you select from these categories critically. The vast majority of these books are academic histories (simply put, I don’t trust popular historians with this topic), so if you are not accustomed to that type of writing, be sure to read the Introduction of whichever books you select very carefully, and understand that you are reading to learn—not necessarily to enjoy. In terms of my categorization…it is imperfect and becomes admittedly fuzzy once we get into the late Ottoman Period. I’m not even 100% comfortable with some of these categories, but alas, if there is one thing I learned from Library Science it is that categories are both terrible and inevitable in the organization of information.

[dawnfelagund]

Hello!! A while ago, I read your article on the transformational + affirmational values in the Tolkien fandom while I was researching for my thesis :P And just now I’ve very randomly stumbled upon your blog and I saw your post about your new article with JTR (congrats!!), and I realised, “wait hold on, I know this person”. So that’s vv cool!

I feel like the rest of this ask is more the kind of thing people do over email, but, well, chance brought me here, so… I’m wondering if you, as a published (!!!) Tolkien scholar, have any advice you’d be willing to share with someone (me!) who wants to get their work published as well? I’m also a bit lost about what the best, reputable journal for something Tolkien-related would be. Off the top of my head, I can think of Mythlore, JTR, and Tolkien Studies — except I’m very confused about whether Tolkien Studies is peer-reviewed or not… Any help at all would be appreciated!

What a fabulous question, and I LOVE to see Tolkien fans interested in presenting and publishing their work. I started as fan before I was a fan-and-scholar, and I currently keep a foot in both pools, and I think it's important to recognize that many Tolkien fans are discussing ideas that are not currently part of the scholarship. Many of us are engaging with the texts deeply and in ways that align well with scholarly work, and dare I say that many of us know the more obscure texts (like the HoMe, for example) better than many Tolkien scholars who are coming from the more academic side.

So Tolkien fans who want to become involved in Tolkien scholarship absolutely should. You have the ideas, the knowledge, and the skills!

There are a few steps that I'd recommend for the Tolkien fan-turned-scholar:

1. Familiarize yourself with the published scholarship. In fandom, we tend to engage with other fans, their fanworks, and their meta, and of course, we spend a lot of time in the books. While Tolkien studies has not traditionally been a field that leans heavily on familiarity with the published scholarship, that is changing. There is growing awareness that scholarship is being published that is rehashing ideas that have already been written, or published works are leaning too heavily on outdated literature, so I expect that journals will be increasingly rigorous in their expectations here.

Thankfully, of the four peer-reviewed Tolkien studies journals, three of them are open-access (only Tolkien Studies is not). Even if I do a search through Google Scholar or my alma mater's library, I always visit these three as well and do a keyword search, as anything published in them, being open-access, is really going to be seen as doing a bare minimum of due diligence: Journal of Tolkien Research | Mythlore | Mallorn

Of course, the state of academic publishing is still unfriendly to independent scholars, though it is better than it was ten years ago. Some publishers let you check out a small number of articles per month. I have luck finding academic books in Archive.org. If you live near a good public library system or a college/university library, interlibrary loan may be an option for books and articles. (Note that this is US-centric.) If you can find contact information for the author of an article, they will often share it with you. Scholars and academics want their work read and cited. They do not benefit from the status quo in academic published. (With three peer-reviewed articles and three book chapters to my name, I have netted a total $0 for my publishing efforts!)

Belonging to fan communities that tend to welcome scholarly approaches is another resource, as members who have access to academic databases and libraries are often willing to help find hard-to-source materials. @silmarillionwritersguild is one; I have definitely asked for help myself on our Discord server! If there are others, please recommend them!

2. Present your work at a Tolkien conference. This can occur at the same time as Step 1 and certainly isn't required, but beginning to attend and present at Tolkien conferences will give you access to feedback from other scholars and help you to refine your work for publication. There are a number of hybrid events that are primarily fannish and so tend to be friendly, approachable venues for your first presentation: Oxonmoot, Mythcon, and Mythmoot (which also has smaller regional moots) are three that I would recommend for first-time presenters coming from fandom. Local Tolkien societies may also put on occasional conference-like events, which are increasingly hybrid. When the SWG's newsletter editor (*cough* me ...) remembers to include it, we do a monthly round-up of calls for papers and proposals in our Around the World and Web section of our weekly email newsletter. This would include the events listed above, as well as more academic-oriented conferences, as well as calls for proposals for book chapters, journal special issues, etc. (Which reminds me that I need to post the June roundup ...)

We just did a three-part series as part of our Master Class column called "So You Want to Present at a Tolkien Conference?" that covers writing a proposal, putting together a paper and presentation, and actually giving the presentation.

3. Publishing your work. As noted above, there are four peer-reviewed journals with a heavy Tolkien studies focus: the Journal of Tolkien Research, Mythlore, Mallorn, and Tolkien Studies. Of course, journals with a broader focus (such as scifi/fantasy or British authors) might be viable as well, as would journals of other disciplines that may overlap with your focus (e.g., fan studies, narratology, film studies, etc.) And there are generally several calls for proposals each year for chapters in anthologies with a specific focus (e.g., I am currently working on a paper for an anthology on women and Tolkien). Robin Reid is a retired academic and Tolkien scholar and an incredible advocate for new and fan scholars. Her Substack publishes CFPs as she becomes aware of them.

Other advice and resources, please add as a reblog or comment! But, to wrap up, I want to say one more time to Tolkien fans who want to present or publish their work:

We, as fans, are talking about ideas right now that scholarship has not yet dreamed of.

We, as fans, engage consistently with the texts in deep and detailed ways.

We, as fans, are not less or inferior to those scholars who took a more traditional academic route. We have important contributions to make to the field of Tolkien studies! You can do this!

[specialagentartemis]

hi artemis! a friend I made during artfight who doesn't have a tumblr account asked me to pass a question on to you!

they're wondering if you know of any good resources for researching more modern history. This is what they were interested in specifically:

"i am trying to think of somewhere i could find information about when cars started appearing in songs and i am absolutely blanking on how i could find things about this

i know the library of congress has a LOT of stuff [and a really good system for filtering the search results] but… i don't even know what i would enter into the search bar sjdhgj. cars?? songs?? the spread of motor vehicles??"

i know this isn't your specific area of expertise but i thought you might still have some thoughts, or be able to point them to someone else who could help. also, they asked me to tell you that you're very cool and they like your posts :) (i concur)

Aww, thanks! <3

Hmm unfortunately, this is not really a topic I know anything about. This is a combination topic/time period I don't study, and a method (corpus analysis) I haven't really done.

But I can talk about how I would go about it, because starting research from scratch is a specialized skill and it is Daunting.

First, I go to Wikipedia on my topic--let's try the Wikipedia page for "Lyrics." The "Academic Study" subheading has no references--pity. Ignoring most of the article text, I scroll down to the references. Scanning them, they're not that helpful, but "Further Reading" suggests Moore, Allan F. (2003). Analyzing Popular Music. That looks potentially useful! File that away.

All right, now I go to academic databases. My own university's library database and JSTOR are my first options. Let's try some keywords--technology in popular music. Hm, no, that's mostly turning back books and articles about the technology used to make and disseminate popular music, not references to technology in the lyrics. Wrong tack. Try technology popular music lyrics. Little bit better--it turns back The Bloomsbury Handbook of Rock Music Research, that sounds promising. The Bloomsbury Encyclopedia of Popular Music of the World, sounds like it could be a useful reference too. Still, this turns back a wide variety of things, none of them about cars. All right, "technology" was too broad a concept. We want to know about cars. Try cars popular music lyrics and automobiles popular music lyrics.

Aha! Here's some good stuff! "Sentiment analysis of popular-music references to automobiles, 1950s to 2010s" (Wu et al. 2022); "Experience economy in the making: Hedonism, play and coolhunting in automotive song lyrics" (Askegaard 2010); Driving Identities: At the Intersection of Popular Music and Automotive Culture (McLeod 2020); "Communicating cars: television, popular music and everyday life" (Alam 2020, a chapter within the book Race, Taste, Class and Cars). Now this is what we're talking about!

And reducing my search terms to cars in popular music, I turned up a book called Popular Music and Automobiles. Jackpot!

Tbh research is trying various keyword strings, and then sifting through a dozen irrelevant result for every relevant one. Identifying relevant-looking books and articles from titles and abstracts is also a skill. JSTOR is good but arbitrarily limited; Google Scholar is pretty good; Library of Congress has records of nearly everything but is in my experience kind of overwhelming unless you know precisely what you're looking for. University library databases are the best, if you have access to any.

However, once you've hit on something specific, the LOC is really, really good in this way: you can go to the page for the book you found--here's "Popular Music and Automobiles"--and then look at the subject tags. Automobiles--Songs and music--History and criticism is its own subject tag! You can see what other books have that subject tag!

... looks like, including Popular Music and Automobiles, it's only four. (and one of those is also McCloud's "Driving Identities," lol.) But, hey. That's two more books about the topic you didn't know about before!

These are the tactics I use when I want to research something new! Hope some of this was helpful.

[crystal-wingeddragon-spikes]

Talking about Art reference and some source.

In this age of AI Art and corporate Art industry, I think it is more important than ever to cite your inspiration and reference, to both works and creators, than to call Ai art soul less or not art.

Before AI Art, Pinterest is also one of the greatest "art/reference middleman" that hoard all reference and disconnect artists from the useful source to learn instead of cherry-picking information that might be wrong or lacking context.

Before ChatBots scraping data and spew out recipe telling people to add bleach into egg mix, we have websites choke full of stolen recipes that came with pointless made up life story to add as many unnecessary keywords to Search Engine Optimization, written by unpaid interns.

What comes to mind is the "wolf skull" that is actually a badger skull and used wildly as a tattoo reference.

Or an art student who study horse muscles, and they mistakenly give the horse human muscle somewhere, also wildly used as a reference.

And the worst of all, tumblr, that feel like the last big website that allow me to curate my own user experience has notoriously awful search function (still not as bad as twitter). I couldn't to find the source for both incident, even if I am mostly sure I reblogged it.

Also, beside the inaccuracy, I want people to think of it as less, "I don't want to use AI Art (or use it as a reference) because they are worse." and more of a:

"We are losing our respect and connection to people who search and publish information. Because of all of these middlemen, Google, Pinterest, and now AI tools, who love to obfuscate information source they took from someone's hard work."

"We are losing out on chances to connect to each other and build community based on shared goals."

"We are losing out developing respect of knowledge, critical thinking skills, and curiosity because we are under a false premise that all knowledge is easily available/easily created."

"We are losing our chance to decided to be someone who provide information and teaching instead of consuming and learning all the time. Unlike what the internet search engine and Chatbot, want to convince you, knowledge is hard-earned and not always available."

This is some art source I used:

Eh, this is a coral identification guild I used, because why not.

This one is from Australia, that I did not use, but truly appreciate how through it is.

Made into a very good, familiar website format:

I beg everyone to make The Internet a good place to share information and argue with each other in good faith again.

[defectivegembrain]

Also like. I got this book out of the uni library. Initially because a couple of the recommended readings are in it but it looks generally very interesting. It's called "The Power of Words: Essays in Lexicography, Lexicology and Semantics; In Honour of Christian J. Kay". Christian J. Kay was a professor of English language and the director of the Historical Thesaurus of English, which is a big database that lots of scholars use. I read the introduction and found this interesting sentence:

"Christian's love of collating and categorising things goes back to her Edinburgh childhood, when she was never happier than when putting buttons or sweets into piles according to colour, shape or size."

Now I wanna be clear I'm not armchair diagnosing anyone, this isn't really about her specifically. But like, more often I've seen this kind of behaviour described when talking about autistic children, and often in that context it's described as "inappropriate" or "purposeless", seen as something to be "fixed". What a contrast to the description here, which is a fond anecdote in a larger text that describes its subject in an admiring, maybe even reverent way. It's a whole book of essays in her honour. And not in a memorial sense or anything, she was still alive at the time.

It's like never listen to anyone who calls categorising type play "purposeless", because fucking look what kind of purpose that can have. The people saying shit like that need categories to function, we all do. Like, of course you shouldn't have to compile a thesaurus or become an honoured academic to justify your autistic behaviour, but it's just really interesting how differently the same behaviour can be construed in different contexts like this.

[transmutationisms]

How do you find the time to read all your book recs?? Also would you mind talking about your process for researching specific topics :)

i generally only make rec lists for things i have enough familiarity with to navigate the literature so, you have to keep in mind those lists are sometimes literally a decade+ of cumulative reading on my end. i do also sometimes include texts i haven't read in their entirety, or occasionally even ones i've only come across in footnotes but still think are foundational or relevant enough to warrant a rec.

as to my research process: there's no single answer here because the sort of research i do will depend on what questions i'm trying to answer. usually if i'm starting to look at a topic completely from scratch, i'll ask someone who publishes in that area what the major recent works are, then scan a few of them. i might 'snowball' those texts (read the works they cite in their footnotes) but, that strategy has limited utility because it only goes backward in time and sometimes a recent or uncited text can be incredibly valuable. so there's a fair amount of bumbling around in the secondary literature at this point. some academic journals maintain bibliographies for their subfields, which are not comprehensive but can be useful; i usually also do a certain amount of keyword fuckery in my library's database. sometimes i waste a lot of time at this point chasing leads that turn out to be irrelevant, or i discover that a question i was chasing is really better tackled from an entirely different direction. shit happens.

at some point i usually reach a stage where i need to look at some primary sources, because i'm oriented enough in the major issues to identify spots where previous researchers haven't made full use of historical records, or may be interpreting them in a way i disagree with. so, what exactly i'm looking for now really varies. sometimes i just want to read the primary texts that another historian is commenting on: for example, the last few months i was trawling through the french national library's archives to see what people were saying in print about a specific historical figure between about 1778 and 1862. other times i might want population data or land records: births, deaths, cholera infections, records of church property sales, &c. depending on, again, what sorts of questions you're asking, anything might have useful information to you: postmortem personal auction catalogues have given me some mileage, along with wills and personal correspondance. i have a committee member who collects and analyses postcards often being sold for pennies at flea markets out of people's grandparents' attics, and another who has an ongoing project looking at a zillion editions of a specific children's book printed in the late 19th century. along the way, as i look at primary sources, i will typically go back and forth to more secondary literature, as i find new topics that might be relevant or help me contextualise what i'm looking at. i can't ever really plan these things out systematically; i just follow what looks promising and interesting and see where it leads me.

another thing to consider is that the primary sources sometimes tell me useful information directly in their capacity as material objects. what type of paper is used, what personal or library stamps appear on the cover, who's the publisher, how many editions did it go through, are the print and typeset jobs sloppy, where was this copy found or preserved? these sorts of details tell me about how people reacted to the text, its author, and the ideas within, which can be a valuable part of whatever investigation i'm trying to conduct. sometimes i end up chasing down information on a publisher or the owner whose personal library a book or piece of ephemera came out of; there are people who research processes of preservation, printing, &c in themselves, which has yielded some fascinating studies in recent decades.

at some point, if it's a research project i'm trying to communicate to other people, i will switch to writing mode, where i try to organise ^^ all of that in my head, and form a coherent narrative or argument that i think is worth making. this might be revisionist in nature ('people have argued before that such and such was x way or historical actors thought about it like y, but what i have here indicates we should actually understand it in the context of z') or it might be more like, "hey, i found this thing i don't think anyone knows about!" or anything else. again, the way you put together a research project will vary so widely depending on what you're researching, and why, and why you think it matters and to whom.

also, i should emphasise that what i've written here isn't necessarily something that happens on a strict or compressed timeline. i'm working on a dissertation, so for that topic, i do have reasons i want to complete parts at certain times, unfortunately. but i also have research projects that i just chip away at for fun, that i've had on various backburners for literally years, that i might sometimes write about (eg, on here) without necessarily ever planning to subject them to the hegemon of academic publishing. i think knowledge dissemination is great and to that end i love to talk to people about what i'm researching and hear about their stuff as well. but, i also think research projects can be fun / rewarding / &c when they're completely for your own purposes, untimed, unpublished, &c &c. i guess i'm just saying, publishing and research conventions and rules sometimes have purposes (like "make it possible to publish this as a book in the next 5 years") but don't get so hung up on those rules that they prevent you from just researching something for any number of other reasons. there are so many ways to skin a cat 📝

[mistchievous]

you work in a library! you're living my dream! 💜 do you have a thoughts/opinions on what you would like people to know about libraries? (or top 5 fave authors if not)

Oh God. Please don't ask for my favorite anything. I'm terrible at making lists like that. 😂

But I love working in a library! It's such a chill space!

I think the main thing I'd want people to know is that libraries are more than just books. Like, obviously books are important (and if your library doesn't have a book on hand, I pretty much guarantee they can get it for you via ILL), but they generally have so much more to offer. And I don't mean e-books, though those are a thing too! You should look into your local libraries' resources and even the resources of library systems around you and around your county/state/etc. Plenty of places have online resources and databases that you don't even have to live in the area to access.

And also, they're FREE community spaces. You can go there and just be without anyone expecting anything of you. You don't need to buy a coffee or justify your existence. Need wifi? Go to the library. Need to use a computer? Go to the library. Need a study room? Go to the library. Need to make copies or fax? Go to the library. Need a large meeting room for a performance or gathering or anything at all? Go the library. Does your library system have maker spaces? Go use sewing machines, 3D printers, and more. One of our branches even has a professional recording studio that you can book and use for free, and it provides recording equipment and premium software.

Wanna stream movies or tv shows without having to pay or pirate, check a site like www.justwatch.com which will tell you if library provided resources like Hoopla or Kanopy have them available allowing you access with just your library card number. (Or just go check out the DVD!) There are often expensive databases available for free as well. Like Westlaw which many people in law pay through the nose for. Or Freegal where you can download music for free. Or Tutor.com which allows you free live professional academic assistance. My system has well over a hundred different databases for all age groups. Kids, teens, young adults, adults, and seniors.

Not to mention, libraries offer community programming for all age groups as well. It's not just Book Club, though we have those too. We do story times for kids. Crafts, STEM programming, performances by local and national performers (especially in the summer), etc. We show movies using large projector screens, have video game and TTRPG programs, and offer prizes for programs such as our Summer Reading Program to encourage people to read. We also have a lot of cultural and historical local archives that house information and photographs and the like that can't be accessed anywhere else in the world.

This answer is getting long, and I've really only scratched the surface, y'all. Libraries are important spaces, and they're one of the few truly free and open community spaces still available. It's infuriating when you have people who think we're obsolete and can be replaced by paid services like Audible and Kindle Unlimited. It makes me want to scream.

Support your local libraries, okay? They matter.

[thistelltaleheart]

Hi! maybe an odd thing to ask but since you commented that you'd written about the subject, do you have any good papers/articles (your own included perhaps) to recommend reading about cellular memory? it's a fascinating topic to me and all the stuff I can find is behind a paywall or so badly inconclusive

Absolutely! My paper was actually on music x cellular memory in the heart (something I forgot until I went to look up the articles I used!). My paper is... less than stellar LOL. There are many better-written articles out there on it, but I'd be happy to send you a copy if you really want.

Here's the relavent article I used:

www.ncbi.nlm.nih.gov/pmc/articles/PMC2882105/ (cellular memory all over the body)

And here are some I found using Google Scholar (free as far as I can tell). Some of these didn't even exist when I was writing!

http://www.alternativemedicinenis.com.au/Organ%20Transplants%20and%20Cellular%20Memories.pdf

https://www.sciencedirect.com/science/article/abs/pii/S0306987719307145

https://www.namahjournal.com/doc/Actual/Memory-transference-in-organ-transplant-recipients-vol-19-iss-1.html

If you have access through your library or a school, I'd recommend looking up articles via a more reliable research search engine (Google Scholar is pretty good, but it's not Academic Search Complete or a university library database). This is still a semi-new phenomenon without exact concrete proof, but there have been too many documented cases, not to mention the discovery of cellular memory, for this to go ignored. It's absolutely fascinating!

[practicumblog]

Working in Adult Services today. I learned a lot!

Supervisor: Hui Zhang & Kenya Johnson

Student: Jessie Martinez

Location: Pasadena Central Library, 1201 Jeff Ginn Memorial Dr, Pasadena, TX 77506.

Date and Time: June 13 [1PM - 5PM] (4hrs).

Today was one of my shorter shifts, and I was able to intern with the adult services department just to dip my toes in their jobs. My dream job is to work in an academic library, so I want to at least have some experience working with adult patrons. I started out in the workshop department and the librarian in charge of that space showed me how to work the 3D printer, cricket machine, and engraving machine. She had me help her make some 3D printed bookmarks which was fun. After that, I worked with the librarians at the front desk who showed me how to make a library card (I made myself an account as I still didn’t have a library card after working there for a whole week!). They also showed me how they processed money from the registers upfront. Next, I worked with Will who showed me how to work the micro-film machine. He also gave me a tour of all of the different databases offered by Pasadena Library. Finally, I worked with two librarians in the back who showed me how they come up with ideas for programing. I am terrible with faces, so I did not realize that I went to undergrad with one of the assistant librarians who I now work with!

I have to admit that this was my favorite day at the library. I felt much more comfortable with assisting the adult patrons of the library. In the kids room, I am always telling children not to scream or not to run, and I always feel that I am being too rude. Out in adult services, however, it was much more relaxed. I was even able to help a patron format a Word document. As a writing tutor, this made me very happy as I do this every day at my current job. All-in-all, I am happy to work where ever I can get a job, but I have my fingers crossed that it is with adults or teens!

Photo above: One of the librarians I work with, Will, taught me how to use a micro-film viewer. It was super fun and interesting. I always pass one of these on my way to the writing center at my real job, so its nice to know what it is now! I had always wondered what it could be. Now, I just need to figure out why the library I work in has a VCR!

[codingbrushup]

Top 10 Data Science Tools You Should Learn in 2025

Best Tools for Data Science are evolving fast, and if you want to stay ahead in 2025, it’s time to upgrade your toolkit. Whether you’re just starting out or already deep into data projects, using the right tools can make your work smoother, smarter, and a lot more fun. With powerful no-code platforms, AI-driven automation, and cloud-based collaboration, the Future of Data Science Tools is all about speed and simplicity. So, whether you’re brushing up your skills or diving into new ones, these Must-Have Tools for Data Scientists are your ticket to staying competitive this year.

1. Python — Still the King of the Jungle

If you haven’t started with Python yet, 2025 is your cue. It’s powerful, readable, and has libraries for nearly everything. Tools like Pandas, NumPy, and Scikit-learn make it your go-to for analytics, modeling, and more. Python is basically the heartbeat of the Best Tools for Data Science ecosystem — and yes, that’s the first mention (just four to go!).

2. R — For the Love of Stats and Visuals

R is like that friend who’s always great with numbers and loves making beautiful plots. It’s perfect for statistical analysis and data visualization. Plus, if you’re into research or academic work, R might just be your best buddy. In the world of Popular Data Science Tools, R continues to hold its own, especially when paired with RStudio.

3. Jupyter Notebooks — Your Data Diary

Jupyter makes it fun to play with code and data in real-time. You can document your thinking, share notebooks with others, and even run visualizations inline. Think of it as your interactive coding journal. It’s easily one of the Top Data Science Tools 2025 and continues to be a favorite for experimentation.

4. SQL — Old But Gold

You can’t really skip SQL if you’re serious about data. It’s been around forever, and that’s because it works. Databases power everything — and being able to query them quickly makes SQL a non-negotiable tool. Every data scientist needs it in their toolkit — it’s a staple in any list of Must-Have Tools for Data Scientists.

5. Power BI — Dashboard Like a Pro

Want to impress your team with interactive dashboards? Power BI is Microsoft’s ace in the business analytics world. It’s user-friendly, integrates well with other Microsoft products, and is super powerful. Among the Data Science Software 2025, Power BI is shining brightly as a great tool for storytelling with data.

6. Tableau — Turning Data into Visual Gold

If you’re a visual thinker, Tableau will win your heart. Drag, drop, and make stunning dashboards in no time. It’s a favorite in the Best Tools for Data Science collection (that’s two now!). Business teams love it, and so should you if you’re serious about communicating insights clearly.

7. Apache Spark — For Big Data Firepower

When your dataset is way too big for Excel and even Python starts to lag, Spark comes in to save the day. Apache Spark lets you handle massive amounts of data in a distributed computing environment. It’s fast, powerful, and a favorite in the world of Future of Data Science Tools.

8. Git and GitHub — Version Control Like a Boss

Messy code history? No more. Git lets you keep track of every change, while GitHub is your team’s central code-sharing spot. It’s not just for developers — every modern data scientist should know Git. You’ll find it featured in every list of Learn Data Science Tools resources.

9. Google Colab — Cloud Notebooks Made Easy

Google Colab is like Jupyter, but in the cloud, and with free GPU access! You don’t even need to install anything. Just log in and start coding. It’s part of the Best Tools for Data Science toolkit (we’re at three now!) and great for remote collaboration.

10. AutoML Tools — Because Smart Tools Save Time

Why code every model from scratch when tools like Google AutoML, H2O.ai, and DataRobot can automate the heavy lifting? These platforms are evolving fast and are key players in the Future of Data Science Tools. Embrace automation — it’s not cheating, it’s smart!

Final Thoughts — Brush Up, Stay Ahead

The tools you use can define how far and how fast you grow as a data scientist. Whether you’re focused on big data, beautiful dashboards, or building machine learning models, knowing the Best Tools for Data Science (we’re at four!) gives you a serious edge.

And hey, if you’re ready to really power up your skills, the team over at Coding Brushup has some fantastic resources for getting hands-on experience with these tools. They’re all about helping you stay sharp in the fast-changing world of data science.

So go ahead and start experimenting with these Top Data Science Tools 2025. Mastering even a few of them can supercharge your data career — and yes, here’s that final SEO magic: one more mention of the Best Tools for Data Science to wrap it up.

[fatfemmefreaquency]

one of the issues though is that post-secondary institutions (in North America, at least—I don’t know much about academia outside of canada & the US, except for a bit about the UK australia & NZ) don’t have any incentives to make learning sustainable

heck. lately even good pedagogy & actually teaching students well has taken a massive hit, and it was never really a focus here to begin with

for decades now educational research has demonstrated that exams and rote studying for exams does not lead to retention of information: students that study for exams don’t actually learn the material

if universities cared about actually educating students we would never have been using exams to assess learning and give grades in the first place

but giving an exam, especially a multiple choice exam, is easy to do—especially on a large scale. running labs, grading papers, teaching seminars with a lot of discourse & student participation means (a) more classroom hours per course and (b) more office work for professors and (c) generally a lower student to prof ratio

unfortunately in academia in the countries I mentioned above (the ones I know most about) almost all universities/ colleges are run like businesses

and businesses are concerned with one thing above all else: making money. doesn’t matter what they do to make money—the cheaper they can make the product or service they’re selling (while charging more for it) the better

and more work for instructors and smaller class sizes costs money—money a business doesn’t want to expend

honestly I don’t blame students for cutting corners**

things are getting progressively worse in US & canadian higher ed, due to a lack of public funding for universities and a massive corporate culture problem. late stage capitalism is reducing universities to diploma factories rather than actual spaces for learning and inquiry

i totally agree with you OP that learning should be sustainable & not drudgery/ hard labour

but i think the chances of that happening have been just about obliterated by how capitalism has shaped academia

(and this has really accelerated in the last decade with schools refusing to hire more tenure track professors and also shifting towards more online learning. we KNOW that larger class sizes and less face-time with instructors means students learn slower and learn less. but schools don’t care: they care about costs)

**footnote under the read more**

**let’s be clear: i think it’s…not smart to choose genAI because chances are you’ll get caught and either expelled for academic dishonesty OR just get a failing grade. chatgpt and all other genAI are really fucking noticeable still in terms of how they write academic work

if you’ve really gotta cheat consider finding 3-5 articles that say what you want to say and then cobbling together a paper or answers out of their sentences—then go through and rewrite every sentence that you don’t want to use as a direct quotation in your own words (for the love of god do not use a thesaurus for this—your prof WILL notice that. just use your own words and reorder the sentence to the best of your abilities)

this is still more work than relying on chatgpt etc but it’s MUCH harder for plagiarism filters to detect (or for an instructor to notice)

this is plagiarism. you’re stealing other people’s ideas and passing them off as your own rather than coming to an original idea

like i mentioned: it’s a little more work than using chatgpt or other genAI but it’s much easier than writing a paper where you make your own argument (good news—this is like a baby step to that, and will make it easier to write papers of your own)

writing a paper like this is quick and relatively easy, still. in a pinch you can tool around in your library’s database of academic articles for about an hour or two and then copy and paste stuff for an hour-ish and then spend another couple hours reworking the syntax of the paper and be done with it

sure it’s not a paper that chatgpt wrote in 5 minutes for you—one that would likely get you in a world of trouble that you don’t need

but what this does is massively cut down on the mental effort and time that goes into writing a paper—in my experience this takes the process down from 10-30 hours to about 6-8 hours AND you likely get a pretty decent grade

i didn’t use this method much personally, but whenever a friend told me they were struggling to keep their grades up/ pass classes i told them to do this

i feel zero shame about it. we went to a school with more than 40 thousand students and the average undergraduate lecture sat 280 students (often with just one professor & 2-3 TAs). students were never really supported to do good research OR to learn in general

it’s hard to do your best work when you’re handing it in to a professor who isn’t paid to do their best work either—or to support you in your learning

i completely understand & agree with the backlash against students using chatgpt to get degrees but some of you are out here saying "getting a degree in xyz means pulling multiple consecutive all-nighters and writing essays through debilitating migraines and having severe back pain from constantly studying at your desk and chugging energy drinks until you get a kidney stone and waking up wishing you were dead every day, and that's just part of the natural process of learning!!!" and like. umm. i don't think that any of us should have had to endure that either. like maybe the solution for stopping students from using anti-learning software depends on college institutions making the process of learning actually sustainable on the human body & mind rather than a grueling health-destroying soul-crushing endeavor

[houseofassignment]

The Science Behind Plagiarism Detection: How Premium Tools Ensure Originality

Hey writers! Have you ever thought about how plagiarism detection is done? With so much similar content on the internet, how can a tool detect the originality of the content?

For those who often say, "write my assignment for me," understanding this process becomes even more crucial. Plagiarism is copying someone else’s work without proper attribution and is a serious offense in various fields.

Plagiarism can have severe academic and professional repercussions and can even lead to legal action in some cases. It can be intentional, where someone knowingly copies another's work, or unintentional, due to carelessness or a lack of understanding about proper citation.

Various tools are available online to detect the originality of the content. Let’s learn how exactly the plagiarism detection tools work here!

How Plagiarism Detection Tools Work?

Plagiarism detection tools use innovative computer programs to check if a piece of writing has been copied. They compare the text with a vast collection of sources, such as websites, articles, and books, to find any matches.

If you’re looking for help, services that say "write my assignment for me" often utilize these tools to ensure the originality of the content they produce for students.

Key Techniques Used in Plagiarism Detection

1. By the method of Text Matching

This is the simplest way to find copied work: the software searches for phrases, sentences, or paragraphs that are exactly or almost exactly the same in both the submitted text and the database it's being checked against.

2. By using the Fingerprinting Technique

This technique creates a unique "fingerprint" of the text based on various characteristics, such as word frequency, sentence structure, and punctuation patterns. These fingerprints are then compared to identify similarities between documents.

Plagiarism detectors use algorithms to compare these chunks with millions of sources, including:

Online articles, blogs, and websites

Academic papers and research journals

Books, PDFs, and digital libraries

3. By Doing Citation Analysis

The next technique is citation analysis. This technique checks for proper citation and referencing within the text. It can identify instances where citations are missing, incomplete, or inaccurate.

4. By Performing Semantic Analysis

This more advanced technique goes beyond simple text matching and looks for similarities in meaning and context between the submitted text and the reference database. This can help identify paraphrased plagiarism, where the wording has been changed, but the underlying ideas are still copied.

Advanced tools use Artificial Intelligence (AI) and Machine Learning (ML) to detect even cleverly disguised plagiarism. AI helps in:

Identifying reworded or paraphrased content

Detecting synonyms and grammatical changes

Understanding context and sentence structure

What is The Importance of Originality?

The important question is why it is mandatory to use a plagiarism tool or what is the importance of originality? Well, originality is very important in academics, research, and content creation. It shows that you can think for yourself, be creative, and add something new to your field.

When students seek help, like "write my assignment for me," it's essential that the work is original and not plagiarised. Plagiarism, which is simply copying other people's work, is the opposite of originality and can have serious consequences.

Plagiarism detection tools help people and organisations make sure that work is original and gives credit to the right sources. This keeps academic and creative work honest and trustworthy.

Why Are Premium Plagiarism Checkers Better Than Free Ones?

There are many tools available on the Internet that offer both free and premium services. While free tools can detect basic plagiarism, they often have limitations.

On the other hand, premium plagiarism checkers provide you with some additional benefits over free ones.  Here’s why premium tools stand out:

1. Larger Database Access

Premium plagiarism checkers scan your work against a huge number of sources, including academic papers and other high-quality publications, to make sure it's original.

2. More Accurate Results

Even if something is changed, sophisticated AI tools can still figure out that it was modified.

3. Better Paraphrasing Detection

Premium plagiarism checkers use advanced tech and huge databases to find even reworded copied content - something free tools often miss because they're not as powerful.

4. Grammar and Citation Suggestions

Premium plagiarism detection tools frequently incorporate additional features that enhance the writing and research process.

These features commonly include grammar checks, which identify and suggest corrections for grammatical errors and stylistic issues, and citation recommendations, which assist users in properly attributing sources and adhering to citation styles.

5. No Word Limit

While free plagiarism checkers may restrict users to scanning only a limited number of words, premium plagiarism detection tools can scan entire documents regardless of their length.

Best Premium Plagiarism Detection Tools

Here are some top premium plagiarism checkers that students and professionals use:

1. Turnitin

This premium plagiarism tool is widely used in universities. These tools compare text against extensive academic databases and provide detailed similarity reports, making them invaluable for maintaining academic integrity.

2. Grammarly Premium

The next plagiarism tool is Grammarly. It is highly recommended as it detects both grammatical errors and plagiarism in the same document.

Not only this, it also offers AI-based writing suggestions. Plus, Grammarly is best tool for citation recommendations.

3. Copyscape

Another plagiarism tool is Copyscape. If you are a blogger or website owner then this plagiarism tools will work best for you. It helps to detect copied content by highlighting them. Thus, gives you the idea of the copied content.

How to Avoid Plagiarism?

Avoiding plagiarism is easier than you think! Follow these simple tips:

1. Use Proper Citations

Proper citation is essential for academic integrity and legal compliance. It acknowledges intellectual property, enables verification, and fosters scholarly conversation. Failing to cite sources can result in severe consequences.

2. Paraphrase Correctly

Express the same concept using different words, ensuring the original meaning remains unchanged.

3. Use Plagiarism Checkers

To make your blog or article authentic and original, use a plagiarism detection tool before submitting them. You can use either free or premium plagiarism checkers available online.

4. Take Notes While Researching

To prevent unintentional plagiarism, taking notes meticulously and rephrasing them into your own words is crucial. This practice ensures that your work remains original and avoids potential plagiarism accusations.

Benefits of Premium Plagiarism Detection Tools Over Free Ones

While free plagiarism detection tools can be helpful for basic checks, premium tools offer a range of advantages that make them more effective and reliable.

Larger Databases: Premium tools typically have access to much larger and more comprehensive sources databases, increasing the likelihood of finding matches for copied content.

Advanced Algorithms: Premium tools often employ more sophisticated algorithms, including semantic analysis and machine learning, to identify even subtle forms of plagiarism.

Detailed Reports: Premium tools provide more detailed and comprehensive reports, highlighting specific instances of plagiarism and providing information about the source of the copied content.

Integrations: Premium tools often offer integrations with other software and platforms, such as learning management systems and content management systems, making it easier to check for plagiarism within existing workflows.

Support and Training: Premium tools often come with dedicated support and training resources to help users get the most out of the software.

Conclusion

Plagiarism detection is a mix of AI, big data, and advanced matching algorithms. Premium tools provide better accuracy, wider database access, and advanced AI-based detection. Whether you're a student, researcher, or writer, using plagiarism checkers can help you maintain originality and avoid academic penalties. Always strive for originality and give credit where it’s due!

[data-analytics-masters]

Top Tools Used in Data Analytics in 2025 – A Complete Guide for Beginners

Data is everywhere. When you order food, shop online, watch a movie, or use an app, you are creating data. Companies use this data to understand people’s behavior, improve their services, and make better decisions. But how do they do it? The answer is data analytics.

To perform data analytics, we need the right tools. These tools help us to collect, organize, analyze, and understand data.

If you are a beginner who wants to learn data analytics or start a career in this field, this guide will help you understand the most important tools used in 2025.

What Is Data Analytics

Data analytics means studying data to find useful information. This information helps people or businesses make better decisions. For example, a company may study customer buying patterns to decide what products to sell more of.

There are many types of data analytics, but the basic goal is to understand what is happening, why it is happening, and what can be done next.

Why Tools Are Important in Data Analytics

To work with data, we cannot just use our eyes and brain. We need tools to:

Collect and store data

Clean and organize data

Study and understand data

Show results through graphs and dashboards

Let us now look at the most popular tools used by data analysts in 2025.

Most Used Data Analytics Tools in 2025

Microsoft Excel

Excel is one of the oldest and most common tools used for data analysis. It is great for working with small to medium-sized data.

With Excel, you can:

Enter and organize data

Do basic calculations

Create charts and tables

Use formulas to analyze data

It is simple to learn and a great tool for beginners.

SQL (Structured Query Language)

SQL is a language used to talk to databases. If data is stored in a database, SQL helps you find and work with that data.

With SQL, you can:

Find specific information from large datasets

Add, change, or delete data in databases

Combine data from different tables

Every data analyst is expected to know SQL because it is used in almost every company.

Python

Python is a programming language. It is one of the most popular tools in data analytics because it can do many things.

With Python, you can:

Clean messy data

Analyze and process large amounts of data

Make graphs and charts

Build machine learning models

Python is easy to read and write, even for beginners. It has many libraries that make data analysis simple.

Tableau and Power BI

Tableau and Power BI are tools that help you create visual stories with your data. They turn data into dashboards, charts, and graphs that are easy to understand.

Tableau is used more in global companies and is known for beautiful visuals.

Power BI is made by Microsoft and is used a lot in Indian companies.

These tools are helpful for presenting your analysis to managers and clients.

Google Sheets

Google Sheets is like Excel but online. It is simple and free to use. You can share your work easily and work with others at the same time.

It is good for:

Small projects

Group work

Quick calculations and reports

Many startups and small businesses use Google Sheets for simple data tasks.

R Programming Language

R is another programming language like Python. It is mainly used in research and academic fields where you need strong statistics.

R is good for:

Doing detailed statistical analysis

Working with graphs

Writing reports with data

R is not as easy as Python, but it is still useful if you want to work in scientific or research-related roles.

Apache Spark

Apache Spark is a big data tool. It is used when companies work with a large amount of data, such as millions of customer records.

It is good for:

Processing huge data files quickly

Using Python or Scala to write data analysis code

Working in cloud environments

Companies like Amazon, Flipkart, and banks use Apache Spark to handle big data.

Which Tools Should You Learn First

If you are just getting started with data analytics, here is a simple learning path:

Start with Microsoft Excel to understand basic data handling.

Learn SQL to work with databases.

Learn Python for deeper analysis and automation.

Then learn Power BI or Tableau for data visualization.

Once you become comfortable, you can explore R or Apache Spark based on your interests or job needs.

Final Thoughts

Learning data analytics is a great decision in 2025. Every company is looking for people who can understand data and help them grow. These tools will help you start your journey and become job-ready.

The best part is that most of these tools are free or offer free versions. You can start learning online, watch tutorials, and practice with real data.

If you are serious about becoming a data analyst, now is the best time to begin.

You do not need to be an expert in everything. Start small, stay consistent, and keep practicing. With time, you will become confident in using these tools.