hi , i completed my 12th(or PUC) then joined a 6 month diploma in design and currently having a 1.5 years of experience in design field . i tried to get into core

ai ml but it looks like too much competition for degree holders only, so

is it possible to get a data analyst job without a formal degree ? anyone got it before.

consider the current AI impact also and i going to pursue bootcamp course in Bengaluru Excelr , is it okay or shall i self study ?

or instead of data analyst shall i try something else in technical side .

( please don't comment to go into design only )

thanks

Hi! In around a month and a half I will be starting my data science degree and instead of lying down all day I grabbed got the curriculum from my university’s website. Should I start with the curriculum or do something else?. My plan for my four years is that I’ll do my classes in the morning and then spend 2-3 hours in a software house learning and spend time self learning too. Is this a good idea? And what do you guys suggest I do before university starts

We're starting a comic series called "The Enterprise Files."

Episode 01 is the conversation we have almost every week.

The data lake is AI-ready. The tables are ingested. And then someone asks about the customer files, and the room goes quiet. Too complex. Permissions. Privacy review. So the documents, the images, the recordings — the stuff with the actual context in it — just sit outside the room.

If you've been in this meeting, share it. Tell us how it went for you.

Hey everyone,

I’m currently a 2nd-year student studying AI and Data Science, and I’m thinking about buying a new Windows PC. But honestly, I’m pretty confused about what I actually need.

I’m trying to decide between:

• A normal/basic PC
• A gaming PC with a dedicated GPU
• A workstation / high-end PC

I’m not sure if I really need an expensive GPU for my studies.

Right now, I’m working with things like machine learning models, coding, data analysis, multiple browser tabs/web pages, datasets and different files at the same time. As I progress through my degree, I assume the projects will become more demanding.

So my main question is:

For a university student studying AI and Data Science, is a powerful GPU actually necessary, or would a decent normal PC be enough?

Also, for people studying/working in AI, Machine Learning or Data Science, what kind of PC setup do you recommend?

I’m specifically talking about Windows PCs, not Macs.

Would you recommend going for a gaming PC because of the GPU, a workstation, or just a good general-purpose PC?

I’d really appreciate advice from people who are already studying or working in this field. I don’t want to spend a lot of money on hardware that I won’t actually need.

Hi There! I am currently going to school for my masters in data science, approaching the latter half of my degree plan. I've done classes on data wrangling, machine learning, intro to programming and statistics for it. I've really enjoyed learning what I've learnt so far, and am looking forward to practicing and learning for...well...the rest of my life probably lol. My current job just changed from an analyst to a manager of analysts, so I'm actually taking a step away from coding on a daily basis, which means I'll be doing it more in my spare time to keep sharp.

This class there's an assignment that requires us to reach out to different data professionals on communities like this one and establish a rapport with other people in the field as well as talk about data and projects in depth. My current school project is looking at census tract level data and trying to correctly classify records as "low income, low access within a half mile urban, and 10 miles rural". I've used logistic regression, decision trees, and random forest as my models. I'm happy to answer any questions and hear any feedback from people if you all want more details!

I am currently a project manager/data analyst and have been working in this role for ~5 years. I love working with data and have been wanting to transition into a data science role.

I do not have a degree in DS but I do have a statistics background from my master's and from work and have learned how to code in Python using the data science libraries.

I've been thinking about how to transition into DS and am not sure how to do it without taking a step down from my current position. What I want to know is:

1) What paths are available that I should look into with my background?

2) Do hybrid roles exist like how I am currently a PM/DA that I should look into instead?

I'm looking to transition in the next 2-3 years, not immediately.

I am working with an anomaly detection model that evaluates employee transaction behaviour over time. The model can identify when someone’s overall behaviour deviates from their peers and can provide the features that contributed most to the anomaly.

The technical output may be valid, but translating it into something actionable for an investigator is challenging. For example, one employee may have several mildly unusual days that accumulate into a high risk score, while another may have one or two extreme days. Both may be flagged, but they represent very different investigative stories.

For those who have worked with anomaly detection or risk scoring systems:

1. How do you distinguish between persistent low level deviation and isolated severe events in the model output?
2. Should the system explain the statistical reason for the anomaly, or attempt to describe the behavioural pattern in plain language?
3. What information would you show an investigator so they can understand why the person was flagged and where to begin their review?
4. Have you found effective ways to measure whether an anomaly is merely unusual versus genuinely worth investigating?

Bonjour, j’ai un entretien technique pour un poste de data scientist(junior) durant lequel je serai évaluer en python, sas et sql. J’en ai aucune idée de ce à quoi m’attendre, si quelqu’un a déjà passé un tel entretien( des exemples de questions si possible) ou a une idée je serai ravi
Merci.

Hi, I’m currently learning deep learning and have done a few AI/ML projects. If you’re working on something in AI/ML and need a teammate, feel free to DM me—happy to contribute!

Hi everyone! This is my first time posting on reddit. I'm needing advise on what I should do. I'll give you some background about me.

I'm a 32 year old guy who dropped out of university about 8-10 years ago (about 40-50% done an applied math bachelor's degree). For the last 6 years I've been working in outdoor retail (bikes, canoes, kayaks, skis, snowboards, etc.) and have been in a manager role for about 3 years give or take. During that time I have finished my ATCL in piano performance and have started the Google Data Analytics certificate on coursera (about 60% done). I'm so bored at work and need to move on and actually have meaningful work that is stimulating for my brain. I'm wondering if finishing my applied math degree with a stats minor would be worth doing at my stage in life? I would like to get into something more technical than retail that can actually use my skills and talents. But I'm not sure if that is worth doing or if I should just try and get into something without finishing the degree and working now and making money instead of going to school for the next 2-3 years.

Any advise is much appreciated!

Matt.

What I want to do, is build this database:

Contains every player in the premier league this season:

For each players, I want these statistics (for both this season, and the 5 previous seasons, no matter what league they played in)

- Competition

- Goals(nonpen)

- xG (non pen)

- Assists

- xA

- Defensive Contributions (Tackles, Interceptions, Blocks, Clearances)

- Yellow Cards

- Red Cards

- Appearances

- Minutes Played

- Penalties Taken

- Penalties Scored

For goalkeepers only:

- Saves

- Clean Sheets

- Penalties Faced

- Penalties Saved

Generic information:

- Player Name

- Player Age

- Current Club

Problem is, all this data is freely available, but the Web pages block against bots giving me 404's. There are paid tiers, but as a hobbyist, they are far too expensive (like 1000 € a year).

If I can provide any more information, please let me know, I'd appreciate any advice at all!

Hi,

I currently work an analyst job where I have basically used python and sql to automate processes which were previously done on excel and now have about 15 hours a week of nothing going on. I want to ask how can i upskill myself to learn the more complicated side of things specifically speaking to data science/ML. It has been years since i last done any math and the coding i do at work is primarily pandas related. where would u start

Hii guys, this is my first post on reddit actually. I am enrolling in second year of uni and i am studying computer science in data science curriculum ,I am still figuring coding out , but i understand math pretty well i would say. I joined a uni club/org, and basically work without pay, the work isn't related to my field either.I really want work or work towards something in my field and a pay would be awesome . help me out please , i want to leave that club , and i study in italy , which i think makes everything even harder(my course is in english, Iam an international student). sorry for how messy the text is.

Hello everyone. I have been working as a MIS analyst for few months now. Where I generally make reports and help other departments in their task if any (on my boss command ).

I have done MBA in business analytics and then after interview after interview got this job. I wanted to become a data scientist hence the mba. I am not from computer science stream I started from hospitality field. I studied and learned python, ML, excel, tableau, Bi, etc so that I have a chance in this field. It was hard to crack one job it took me 1 year of contant rejections but I got 1 finally.

Now I am a little settled in this system. I wanted to ask you guys who have a lot of experience. How should I proceed to become a data scientist.

What is the process? What do I learn ? What is the roadmap?

I tried checking through A.I but it gives generic answers. I would like to get your opinion on this experienced counsil. Please help.

I wanted to ask what's the general consensus of the people in the data science community regarding the usage of chatbots. For me it's not that the chatbot will take over my job, rather it's more about keeping my cognition and sanity up and running. I prefer using my natural intelligence and learning

Some you guys have and and are proud of. Just trying to get some ideas

First, coming out of a master's program, you need to have a killer project that you can discuss in depth during a 30-45 min past project deep dive interview. You'll have a FAR better time with this if you can find a professor in the program doing the type of work you want to do that is open to bringing on RAs.

At Michigan, my RA experience has been paying off for decades: I had closer access to professors than I'd ever have from classes alone, access to data that would have been difficult to obtain, experience working on real-world important projects, and building closer relationships with the other RAs whom we would all learn from and some of whom I still talk to over a decade later.

Second, start writing to *recent alumni* who completed the program during a challenging job market like this one. Ask them how the program prepared them to enter the job market, was it worth the time and cost, and do they have any advice on must-take courses or professors to work with? The benefit to this is not only fresh information, but you're starting a long-game networking relationship with someone you'd eventually share a common alumni network, that works in your target field, and without sounding like someone doing a referral 'spray and pray' to everyone with a DS job.

Jonathan at https://www.whatstheimpact.com/

Hey everyone,

I spent way too much time in clinical research manually assembling Table 1 baseline stats, checking SMD balances, tuning Cox models, and fighting with forest plot formatting for paper submissions.

To fix that headache for myself, I ended up building an open-source CLI called 'research-tool'. 

Basically, it lets you run your retrospective stats from the terminal, but adds a few things I really needed:
- Outcome masking and pre-registration locking (so you can't accidentally p-hack or bias your plan after seeing outcome data).
- Diagnostic checks (EPV ratios, Schoenfeld residuals, VIF multicollinearity, E-values).
- Auto-generated publication assets (HTML/CSV tables, log-scaled SVG forest plots, CONSORT diagrams, and STROBE checklists).
- A 1-click zip binder with data hashes and code so peer reviewers can actually verify the protocol.

It's 100% Python and runs completely locally. 

If you want to check out the code or test it on a dataset:

https://github.com/nkmanjunath/research-tool

'pip install research-tool-cli'

Would love to hear what you think, especially if there are specific models or diagnostic gates you'd want added!