Data Science

... in the middle of the science lab and drew a chalk ... INCIDENTS. THE ANOMALIES. THE… CREATIVE DATA.” Pitnicker’s glasses fogged with stress. ...

... is a bit of a science experiment, with all kinds of ... NUC run everything? Get network data in (I have a WiFi ...

... beyond reasonable doubt. Referencing tracking data patterns and statistics and highlighting... Health / Registrar General data. Nuffield Trust Simpson + official data synthesis - "by... and their contribution to medical science in Britain, in fact let's...

... said: When it comes to science not everyone has access to .... But it's not. Science relies on interpretation. If data is new, scarce, or...

... been a part of computer science. I have likened computer languages... the hard part. Algorithms and data structures and math have for ...

... die werd gebruikt nog pure science fiction was. Maar tegenwoordig hebben... lijken op Data. Hierdoor is Voyager een stuk minder science fiction dan...

... artificial intelligence stops interpreting only data and begins to interpret thoughts... you like to read about science, health and how to improve... your life with science, I invite you to go...

... cuts proposed to NASA, National Science Foundation, National Institutes of Health..., Department of Energy Office of Science, Environmental Protection Agency, etc. Rollbacks..., promoting fossil fuels, etc. More Data Centers and AI using all ...

... do in there?" Rex snorted. "Science stuff," he said vaguely, already... a trace of it, comparing data from Washington, Berlin, Paris, Leningrad... it in stride, watching the data with that quiet, knowing look... harder to ignore the more data came in. Every time the...

...! It is totally not sleazy data mining! We are putting you... finishing up my obligations to science and all I say is...

... and I’ve already done the science at my own expense and... exceptional guitarist… there’s ONE fun data point…

... developer burnout. Anonymous behavior data collection for science. marketplace.visualstudio.com 마켓...

... developer burnout. Anonymous behavior data collection for science. marketplace.visualstudio.com 마켓...

4주 집중 Data Analytics Training Program � ​ 본 프... New York University Data Science, M.S. 前 IT Enterprise Data/Systems Engineer 現... University & Corporate Data Analytics Instructor ​ � 클래스...

.... BOMBSHELL paper on COVID batch data from Germany shows CLEARLY the... of the truth and real science

... uncertainties in astrobiology and planetary science. The equation is expressed as....0 (highly uncertain) No direct data; Earth suggests life can emerge...

... a sus profesiones, machine learning, data science ect. no a dar cursos...

... a lot plots and test data with far better accuracy! Constructing... follow the same old anti science stuff by decoupling specs and...

..., to provide engineers with more data about the spacecraft’s piloting capabilities... list sent by the lunar science team of surface features on....html https://www.livemint.com/science/news/artemis-ii-mission-nasa-crew-describes-far-side-of-the-moon-says-not-the-moon-i-m-used-to-seeing-11775398620330.html https://www...

... example. Some will choose data science instead of computer science, if they think...

Revealing the Science Behind the Enhanced Games The ... and modern medical science. Through rigorous medical governance, transparent data protection, and a...

... independent auditing for the binary "science apps" we execute directly from... mandatory multi-factor authentication and transparent data retention policies across the various...

So for context, I’m currently interning as a full stack web dev, but i’ve been considering data science as another path a few years ago it felt like one of the best fields to get into, but now it seems more crowded and harder to break in at the same time, companies still rely heavily on data so demand should still be there is it still worth getting into today or has it become too saturated? submitted by /u/Imaginary_Bug6202 to r/TechNook [link] [comments]

submitted by /u/Shiny-Tie-126 to r/science [link] [comments]

Data Science hype is dying, there are many Data Science grads seniors that cant get a job now. From the GES only 65.1% of NUS Data Science grads found a full time employment which is worst than Arts stream. Even then i suspect the figures are inflated as i know many Data Science grads, even those with 2-3 internships, that couldn't find work in a Data Science related field, RIP to those that jump onto the Data Science bandwagon. Their only hope now is to complete their degree and pivot out to other fields. submitted by /u/According_Pickle954 to r/NTU [link] [comments]

Hey everyone, I wanted to get some honest advice from people who are already working in data (analysts, scientists, engineers, etc.) because I’m trying to figure out the smartest path forward. My background: * I currently work in biotech (cell & gene therapy) at a pretty well-known company * My role is more technical/operations-focused, but I’ve been getting exposure to data-related work from the MSAT data science team (process improvement, working with data, some analytics thinking) We have worked on some projects but even though ive healped I couldn't call tehm my own * I have a biology background, not a formal CS/stats degree * I have taken some certs from google and stuff that from what I've heard from other professionals don't really ammount to nothing but personal knowledge. **What I’m trying to do:** I want to transition into data — ideally something like data analyst first, then potentially data scientist later. My concerns: * The market seems *extremely* saturated (seeing 300–1000 applicants per job) * Not sure if certificates actually help or if they’re just noise at this point * Unsure how much my current experience can realistically “count” as data experience * Wondering if I should focus more on analytics vs full data science (ML, etc.) * Not sure what a realistic first salary/timeline to break in looks like Questions for yall If you were in my position, what would you focus on in the next 6 months? How valuable is domain experience (biotech/manufacturing) when transitioning into data? What actually makes a candidate stand out right now (projects, experience, networking, etc.)? Is it realistic to land a remote data job early on, or should I assume I’ll need an in-person role first? Would you prioritize a master’s degree in data science, or focus on experience + projects instead? What type of projects would you say are the most impressive to recruiters? I’m willing to put in serious work, I just don’t want to waste time going down the wrong path. Appreciate any advice submitted by /u/Southern-Hospital-11 to r/DataScienceJobs [link] [comments]

There are thousands of people learning data science, machine learning, and AI. The resources are everywhere, and many of us spend months or even years building strong skills and working on meaningful projects. But when it comes to getting the first opportunity, it suddenly feels extremely difficult. Many entry-level roles ask for experience, which creates a strange situation for fresh graduates trying to enter the field. I’m currently pursuing MSc in Data Science and I’ve spent a lot of time developing my skills and building solid projects. I’m genuinely passionate about solving problems with data and continuously improving my knowledge. I wanted to ask people already working in the industry: What actually helped you land your first role in data science? What do companies really look for in candidates starting out? Also, if anyone knows about entry-level opportunities or internships or would be open to offering a referral, I would truly appreciate it. submitted by /u/Personal-Address-587 to r/jobs [link] [comments]

My 9 year old daughter, has always been the kid who asks why about everything. She’s currently in that phase where she’s obsessed with animal secrets; basically, she wants to know what they do when humans aren't around. Since we’re doing a heavy unit on biology this semester, I wanted to move past just reading textbooks and actually let her run her own field study. We set up a smart coolfly bird feeder as our primary observation spot, and it has completely shifted her perspective on what science looks like. She used to just use her iPad for games, but now it's basically her field laptop. Instead of just watching birds eat, she’s started running actual experiments. She spends about 20 minutes every afternoon reviewing the saved clips in the app and logs specific behaviors in a Google Sheet on her Chromebook, tracking things like which species are the bullies at the perch and how they react to different weather patterns. To round out our outdoor tech lab, we have integrated several other tools that make her nature study feel more like a high tech investigation. We have a smart weather station set up so she can track how barometric pressure and humidity directly affect which birds decide to show up at the feeder each day. Does anyone else have tips for these kind of projects for kids? I’m looking for more ways to use tech to collect real world data! submitted by /u/Tweetle_cock to r/biology [link] [comments]

submitted by /u/Purple_Dust5734 to r/ScienceOdyssey [link] [comments]

With all the AI stuff going on I don’t see how my skillset can be useful in the future. It’ll be a couple languages like R, python, Java, and machine learning, deep learning, data structures and so on. submitted by /u/Rezlem- to r/CollegeMajors [link] [comments]

Im an industrial engineer whos gonna graduate by the end of the month. Ive been studying data science from the past 6 months (took ibm data science speciality, jose portilla's udemy course machine learning for data science masterclass, python, sql) Im currently lost on what steps to take next I sat down with a data scientist today and tried to ask for advice, he told me he doesnt even think that data science will stay, its gonna be replaced by AI. Especially the machine learning algorithms and classification methods (trees,boosting,etc) they aret being built from scratch anymore Im totally lost now and dont know what next steps to take and what to learn next. Should i pursue business analysis/data analysis/what courses to take/what skills to learn, and you see how my brain is exploding submitted by /u/Hellsword27 to r/DataScienceJobs [link] [comments]

Calling all data scientists, ML engineers, AI researchers, and anyone working in the data/AI ecosystem — I’m hoping to get honest insight from people in the field. I’m currently deciding my career direction, and Data Science has been one of the main areas I’ve been considering. But with the rapid rise of automation, LLMs, and AI-driven tools, I keep hearing discussions about data science roles shrinking or becoming obsolete. This has made me question whether it is still a reliable long-term path. I want to understand whether Data Science is still worth entering in 2026, or whether the field is becoming too automated for stable career growth. Are companies reducing traditional DS positions, or are the roles simply evolving into something more technical, such as ML engineering, AI engineering, data engineering, or AI-focused product roles? If the field is changing, I would also appreciate guidance on which skills someone starting in 2026 should prioritize to remain relevant by 2030 and beyond. I’m also interested in a realistic view of opportunities both in India and abroad. Is Data Science still stable and in demand worldwide, or is the market becoming saturated and uncertain? Any genuine insight or experience would be extremely helpful as I try to make an informed long-term decision. submitted by /u/Every_Flight_9308 to r/careerguidance [link] [comments]

I'm an AI engineer who oversees hiring at my company. The gap between what candidates show and what gets them hired is honestly depressing. What job postings say: PhD or Master's preferred 5+ years ML/DL experience Publications a plus Expert in PyTorch, TensorFlow, scikit-learn What actually gets people hired: Can you clean messy data without complaining? Can you explain your model to someone's VP who doesn't code? Can you ship something in production? Do you know SQL well enough to not break things? Are you pleasant to work with? IMO, most "data science" jobs are 70% data engineering. The modeling is maybe 20% of the actual work. If you can't wrangle APIs and build pipelines, you're going to struggle. Kaggle portfolios might hurt you. Hiring managers see "Kaggle competitions" and think "this person optimizes for leaderboards, not business problems." Show me something that solved a real problem, even a tiny one. The PhD requirement is mostly BS. Companies write "PhD preferred" because they think that's what serious roles need. Then they hire the person who actually shipped something. Entry-level doesn't really exist anymore. When postings say "3-5 years," they mean it. The "we'll train you" era is over. What actually works: End-to-end projects (problem → data → model → deployed result) GitHub with real code, not just notebooks Proof you can work with engineers Blog posts or anything showing you can explain technical stuff to humans Referrals (still 80% of how people actually get jobs) So, if you're applying to 100+ jobs with no response, it's probably not your skills. It's that you're showing academic credentials when companies need proof you solve business problems. The market sucks right now. But the people getting hired are the ones who can demonstrate impact, not just knowledge. Am I wrong? What's your experience? What's actually working for people landing DS roles? submitted by /u/AskAnAIEngineer to r/DataScienceJobs [link] [comments]

submitted by /u/ElectricalIons to r/recruitinghell [link] [comments]

I’ve been working in data science for the last ten years, both in industry and academia, having pursued a master’s and PhD in Europe. My experience in the industry, overall, has been very positive. I’ve had the opportunity to work with brilliant people on exciting, high-impact projects. Of course, there were the usual high-stress situations, nonsense PowerPoints, and impossible deadlines, but the work largely felt meaningful. However, over the past two years or so, it feels like the field has taken a sharp turn. Just yesterday, I attended a technical presentation from the analytics team. The project aimed to identify anomalies in a dataset composed of multiple time series, each containing a clear inflection point. The team’s hypothesis was that these trajectories might indicate entities engaged in some sort of fraud. The team claimed to have solved the task using “generative AI”. They didn’t go into methodological details but presented results that, according to them, were amazing. Curious, nespecially since the project was heading toward deployment, i asked about validation, performance metrics, or baseline comparisons. None were presented. Later, I found out that “generative AI” meant asking ChatGPT to generate a code. The code simply computed the mean of each series before and after the inflection point, then calculated the z-score of the difference. No model evaluation. No metrics. No baselines. Absolutely no model criticism. Just a naive approach, packaged and executed very, very quickly under the label of generative AI. The moment I understood the proposed solution, my immediate thought was "I need to get as far away from this company as possible". I share this anecdote because it summarizes much of what I’ve witnessed in the field over the past two years. It feels like data science is drifting toward a kind of pseudo-science where we consult a black-box oracle for answers, and questioning its outputs is treated as anti-innovation, while no one really understand how the outputs were generated. After several experiences like this, I’m seriously considering focusing on academia. Working on projects like these is eroding any hope I have in the field. I know this won’t work and yet, the label generative AI seems to make it unquestionable. So I came here to ask if is this experience shared among other DSs? submitted by /u/Raz4r to r/datascience [link] [comments]

I am currently studying Data Science and really fell in love with the field, but the more i progress the more depressed i become. Over the past year, after watching job postings especially in tech I’ve realized most Data Scientist roles are basically advanced data analysts, focused on dashboards, metrics, A/B tests. (It is not a bad job dont get me wrong, but it is not the direction i want to take) The actual ML work seems to be done by ML Engineers, which often requires deep software engineering skills which something I’m not passionate about. Right now, I feel stuck. I don’t think I’d enjoy spending most of my time on product analytics, but I also don’t see many roles focused on ML unless you’re already a software engineer (not talking about research but training models to solve business problems). Do you have any advice? Also will there ever be more space for Data Scientists to work hands on with ML or is that firmly in the engineer’s domain now? I mean which is your idea about the field? submitted by /u/FinalRide7181 to r/datascience [link] [comments]

A lot of ppl misunderstood and think that data science equals tech and cs, and they jump on the data science bandwagon,thats far from the truth. Data Science!= tech and CS. Instead u will be learning more hardcore math in Data Science rather than tech and CS subjects. Its pay isnt really that high either, and its definetly nowhere near the pay of true CS and tech roles. Truth is data science is nothing more than a cash cow by universities to lure and bait students in, only for students to be disapointated once they get into that course. submitted by /u/Excellent_Copy4646 to r/SGExams [link] [comments]

A data scientist discovered that Hamas's death statistics are impossible because the death rate is inclining at the exact same rate all the time. submitted by /u/ApostateProphett to r/PoliticalCompassMemes [link] [comments]

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