#data science

[reasonsforhope]

If you're feeling anxious or depressed about the climate and want to do something to help right now, from your bed, for free...

Start helping with citizen science projects

Explainer: what is citizen science?

Public participation in science is increasing, and citizen science has a central part in this. It is a contribution by the public to researc

What's a citizen science project? Basically, it's crowdsourced science. In this case, crowdsourced climate science, that you can help with!

You don't need qualifications or any training besides the slideshow at the start of a project. There are a lot of things that humans can do way better than machines can, even with only minimal training, that are vital to science - especially digitizing records and building searchable databases

Like labeling trees in aerial photos so that scientists have better datasets to use for restoration.

Or counting cells in fossilized plants to track the impacts of climate change.

Or digitizing old atmospheric data to help scientists track the warming effects of El Niño.

Or counting penguins to help scientists better protect them.

Those are all on one of the most prominent citizen science platforms, called Zooniverse, but there are a ton of others, too.

Oh, and btw, you don't have to worry about messing up, because several people see each image. Studies show that if you pool the opinions of however many regular people (different by field), it matches the accuracy rate of a trained scientist in the field.

--

I spent a lot of time doing this when I was really badly injured and housebound, and it was so good for me to be able to HELP and DO SOMETHING, even when I was in too much pain to leave my bed. So if you are chronically ill/disabled/for whatever reason can't participate or volunteer for things in person, I highly highly recommend.

Next time you wish you could do something - anything - to help

Remember that actually, you can. And help with some science.

[sagotto]

I'm excited to draw more datas !!!

He's becoming easier to draw

[signalcli]

Trading Crypto Isn’t Gambling — It’s Your Ticket to True Financial Independence

“Crypto trading? Isn’t that just gambling?” You’ve probably heard this skepticism more than once — usually from the friend who considers scratch-off lottery tickets a solid investment strategy. But let’s be serious (just briefly!). While cryptocurrency trading — especially leveraged futures — might initially seem like playing slots in Vegas, it’s actually nothing of the sort. Done properly, crypto trading is strategic, calculated, and your genuine pathway to financial independence.

Gambling vs. Strategic Trading

First, let’s clear up this common misconception. Gambling relies purely on luck. The house always wins, right? But crypto trading isn’t about luck — it’s about understanding market dynamics, trends, indicators, and managing risk meticulously.

In crypto futures trading, you don’t randomly pick an asset and hope for the best. You analyze. You strategize. You base your trades on data-driven signals derived from sophisticated technical indicators. This approach transforms speculative bets into educated predictions.

Spot Trading Explained

Spot trading is straightforward: you buy and hold actual crypto assets, hoping their prices rise. It’s similar to buying stocks. Your profits (or losses) directly correlate with the asset’s market price. It’s relatively simple, less risky, but limited — you profit only if prices go up.

Futures Trading Demystified

Futures trading is more dynamic. You don’t own the crypto directly. Instead, you enter contracts that allow you to speculate on price movements — up or down. This flexibility is crucial because it lets you profit regardless of market direction.

Add leverage to the mix, and you can amplify potential returns from smaller price movements. Yes, leverage also increases risk, but with proper risk management strategies — small positions, high leverage, and ample margin — futures trading can be highly profitable without reckless exposure.

Earning Money: Spot vs. Futures

With spot trading, your strategy is limited: buy low, sell high. Easy in theory, but challenging in volatile markets. Plus, most cryptocurrencies — around 90% — lose value rapidly. You can hold major assets like Bitcoin (BTC) or Ethereum (ETH) to mitigate this risk, but the approach requires patience and time.

In contrast, futures trading lets you profit whether markets are rising or falling. In a market where most crypto assets decline in value, futures trading uniquely positions you to capitalize on these downturns. Shorting assets that are plummeting in value, coupled with leverage, can dramatically boost your profitability.

The True Power of Futures: Profiting on Declines

Remember the statistic: approximately 90% of all cryptocurrencies lose significant value within a year. In the futures market, this isn’t just a warning — it’s an opportunity. You can strategically short declining assets and multiply your earnings through leverage. This powerful combination is unavailable in spot trading, providing you a distinct advantage.

Choosing Your Path: Practical Advice

Still unsure? Let’s simplify:

Prefer stability and simplicity? Consider spot trading major assets or mining.

Want active profits and flexibility? Futures trading offers quicker, potentially higher returns, especially when leveraging strategic short positions.

Navigating Risk: Small Bets, High Leverage

Risk management is vital. Successful futures traders use small positions with substantial margin reserves, enabling flexibility and significantly reducing liquidation risks. You won’t “bet the farm” — you strategically allocate small trades, comfortably manage risks, and steadily build profits.

Why Trading Crypto Isn’t Gambling

Trading crypto futures isn’t gambling — it’s sophisticated financial planning. Using advanced indicators, historical data, AI-driven signals (such as those from reputable providers like SignalCLI), and disciplined risk management strategies, you’re making informed, calculated decisions — not random bets.

Financial Independence Through Crypto Trading

Financial independence means having the resources and freedom to live life on your terms, free from typical financial constraints. Crypto futures trading can genuinely offer this level of freedom — if done correctly. With discipline, education, and the right tools, futures trading becomes your path toward genuine financial independence.

Conclusion: Your Ticket to True Financial Freedom

Crypto futures trading in 2025 isn’t gambling. It’s your strategic, calculated pathway toward financial independence. With meticulous planning, disciplined risk management, and informed decisions powered by advanced AI and unified indicators, you have the tools to trade confidently and profitably.

Ready to turn crypto trading from a misunderstood gamble into your ticket to financial freedom? Your future starts now.

Enjoying the content? Awesome! Please support this project by sending USDT (BSC / BEP20 network) to: 0x7241275b9D37CcF0621480fD408CFf401762c485 Your contribution of $5–$10 helps keep these articles free and accessible to everyone. Thank you for your support!

[raindoor]

In 2018 I participated in a machine learning hackathon despite having no machine learning or data science experience. What attracted me to it was the Natural Language Processing aspect of machine learning. Anything that has to do with languages and computing (such as computational linguistics) has an irresistible draw for me, even though - and especially because - I've never done it in any of my software development jobs. It is very far from my area of specialization. My only foray into computational linguistics so far has been experimentation with the word2vec Python package. That was true at the time of the hackathon, and it is still true 7 years later. Why? That's a long and not particularly interesting story.

But I had an idea for an application, so I thought, why not try to go and implement it during the hackathon. What do I have to lose?

My second reason was that it was an all-women hackathon, so I didn't have to worry about the judgement of tech bros. I could build an application as embarrassing as I wanted. Turns out, as far as that criteria is concerned, I succeeded in spades.

On Sunday, at the end of the weekend-long event, when the teams presented their results, I had an eye opener: at a machine learning hackathon - as opposed to a regular one, where people build apps - people did NOT build apps. Rather, they wrote Python and R scripts and presented the results of the analysis performed by those scripts. Most of their presentations consisted of slides of the results. Me, on the other hand, I knew how to write software applications, so that's what I did. In that weekend, I built an application with a simple front-end that displayed some "interesting stuff" I found out via my word2vec experimentation.

By "interesting" I mean interesting to me, but apparently not to the members of the jury, nor to anyone else in the room. At this hackathon, unlike any others I've been to, both the participants and the jury were able to assign points to each project, and then the points were announced publicly. (By the way, I don't recommend this practice. It would be enough to announce the first 3 winners, but it does no one any good to announce the points scored by non-prize winners and to each team to know their exact ranking.) To no one's surprise, my project was last. It didn't surprise me either, because, as I said above, I had no experience in machine learning. I was more or less the only one with a functioning app (minimalistic though it was), but apparently that was really beside the point.

Later I realized that my app, in truth, did not fall into machine learning category at all: experimentation with word2vec was by far not sufficient to qualify for this field. Realizing it made me laugh. If anything, the jury members were too generous to give it any points!

Thinking about it today for "no reason at all".

[therapatical]

re: annoyance at how AI has ruined the reputation of the field I love dearly, but this was the type of shit that got me into data science:

like. a shit ton of incredibly hard-working, amazing scientists had to do a LOT of work in order to figure out how the hell to analyze and display this type of data in a way that made sense to the human brain. the sheer amount of work that went into these two graphs is not properly understood just by looking at it

these are gene expression graphs using single-cell genomic RNA sequencing data (the top one is mouse, bottom is human)

each cell in your body has the same genome⁽¹⁾. but you're not using all of your genes in every single one of your cells (that would be disastrous lol). so, in any given cell in your body, the only genes being transcribed (copied into messenger RNA) and turned into proteins are the genes that that specific cell type uses. and since messenger RNA (mRNA) is what those genes get copied into before being made into protein, you can get a pretty good guesstimate about which genes are actively being used in which cells, by simply sequencing the mRNA in that cell⁽²⁾

so with single cell genomics, instead of sequencing the RNA from a chunk of tissue (or blood or whatever) where you get aggregate results for a bunch of cells all at once (with no way to differentiate which transcript came from which cell; imagine if you just emptied out the cells and put them all in one vat), you are instead sequencing everything that was inside each cell individually.

if you've got 1,000,000 cells in a piece of tissue, this is effectively like doing sequencing for 1 million different samples

how the fuck do you visualize that? you've got different levels of gene expression⁽³⁾ for each of those cells. maybe one cell has a bunch of Gene A, but another cell has like half as much. if you represented that on a graph, you'd basically have a 1 million dimensional object

so you need to collapse those dimensions into 2 or 3 dimensions. that's what's being done here. they collapsed all that information into a couple of dimensions. each dot is 1 cell, given a fancy mathematical score to determine where it sits on those plots (the UMAP axes don't mean anything comprehensible to the human mind) based on how similar it is to other cells, creating clusters.

but like!!!! that's data science!!! this is data science!!! AI was created out of this field, yes, and you can probably see how given the similarities with this type of data calculating. but AI is not the SOLE thing within the field of data science. it wants to market itself that way but it is far from it

⁽¹⁾technically somatic mutations are a thing and lots of your cells have them but shhhhhh shhhhhhhhhhh

⁽²⁾since mRNA isn't copying the entire genome; each mRNA is just 1 gene. so if you need to make a new hemoglobin protein, you'll go to your DNA recipe book and write down (transcribe) your recipe for hemoglobin onto your mRNA notecard, and then walk over to your kitchen (ribosome) to go cook up that hemoglobin protein

⁽³⁾measured by counting how many transcripts ["reads"] were found in each cell; you've got multiple copies of different genes floating around at any given moment. you can write down the same recipe on multiple note cards multiple times, since you're not changing anything about the original recipe book in order to do that lol. so if you need to make a bunch of copies, you can

[dinosaureleanor]

My favorite hobby is extrapolating data in wildly inaccurate ways.

For example I left home this morning and there were three (3) chickens in my yard (pic for reference) (for context, I own exactly 0 chickens)

I returned home and there were four (4) chickens in my yard.

By the end of the year I estimate around 80 sextillion chickens in my yard (approx 1% of the mass of earth).

How am I going to feed them all???

[hexagr]

Why do we study science? Well, since we can't study magic itself—because magic is invisible and hard to detect—we choose to study the fields closest to magic: math, physics, biology, computer science, and so on.