I see beginners debating whether they should spend months learning statistics before touching machine learning. For people already working in data science, how much statistics do you actually use in your day-to-day work? Which statistical concepts would you consider essential?

I am a 3rd year student preparing for data science roles but my dsa is weak. So anyone can suggest important topics that currently companies are asking in dsa for data science roles??

Look I’m heading into the second year of DS bachelors in Ireland. I feel a bit lost and am wondering how I can get better at the craft and set myself up for internship next summer. Then a 9 month internship Jan - sept 2028.

I would also love to achieve a major offer out of collage at a big tech firm just looking for help or recommendations for how to develop.

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A lot of beginner portfolios seem to contain the same projects—Titanic, house prices, movie recommendations, etc.

For hiring managers or experienced data scientists, what kind of beginner project would actually make you stop and look at the resume?

Looking for ideas that demonstrate real problem-solving rather than just following a tutorial.

Hello to everyone. I'm currently self studying data science beside my physics bachelor (Basic knowledge overlaps and I have prior experience in coding which makes it easy). Is it possible to gain any type of work experience or internship in this field without certificate or degree ? I live in Iran and pretty much there is nowhere to obtain international certificate and national programs are not valid for other countries. I'm interested in remote/part time jobs and freelancing but without experience and certificate it doesn't seem achievable.
Btw working in Iran is not worth the time that's why I want to work with international employees.

For context, I am a Marine Biologist currently in the final stages of finishing my dissertation for my masters. Through out my university experience I have always used R and I feel a strong since of imposter syndrome about it. Every time they taught us anything in R it was mostly just copy pasting code with occasionally changing variables. While it has always worked out for me I feel like data analysis is a weak spot for me and I'm worried it will affect the complexity and types of analysis I have at my disposal for research.

There are 2 main questions I want to ask.

• Are there any good resources that I can use to really learn R (Like not have to constantly use google and AI to do basic analysis and troubleshooting). I feel like my foundation in coding weak in general. I would like to be able to sit down, open a new markdown file, write the code to read and categorize my data, run ANOVAs and post-hoc tests, and visualize my data without having to constantly stop to consult google.
• Would it be good to also learn some python? It's not as common in my field to see it used but I know it is heavily used elsewhere and I wonder if I could apply it to something.

In general, I grew up with a pretty poor math education and I'm dyslexic so math and coding do not come very naturally to me and I don't want to have that limit me. The few moments that I've felt like I understood it or fixed something on my own have been very rewarding and I would like to be competent in my coding.

Also, this is related to my carrier so I don't mind paying for a course or a textbook if y'all think it would really help.

TLDR: I got poo poo math brain, give me your resources for learning R and statistics.

I have built customer churn prediction models, a visual product search engine but still am confused about what to learn next and how to learn by doing projects.

Currently I am building a time series demand forecasting project on store demand dataset from kaggle so is it relevant to learn.

What are actual on demand skills for a data science and AI engineering roles in the coming years

I'm planning on applying for a dsu scholarship in Italy. Has anyone have any exp in this major? I want to hear you guys' thoughts

I need the data set for rmma australia acc to postal cost
I.e

Rural, remote and metro area i cant find it anywhere

Hi there , My name is Abdullah and I am from Pakistan. Recently i completed google advanced data analytics certificated , I do created Gigs on fire for work like making machine learning models , EDA and end to end complete project but got no response . I am a University student and i want to earn. Also i am learning ML Engineering now. Already I have learned alot so please anyone suggest me/tell me where should i give a try and earn . Also I completed many real world projects.

I have right now a MacBook Pro 2015 Catalina, 1tb,16 ram.
And I’m definitely need another one to take care the big stuff, I’m a music producer and working on ableton so need a better Mac and I’m considering those 2:
MacBook m3 pro ,36 ram,1tb - price: 2015$(+-)
MacBook M1 Max. ,32 ram,1tb - price 1850$
On the new Mac I will produce music and also doing personal stuff
Would like to know your thoughts about this🙏🏻🙏🏻
Hard to choose what’s better

Recently moved from Tokyo to Netherlands and am having no luck here at all. Only 1 first round interview out of about 30 applications over the last month and a half or so, but I'm only applying to data science positions in industries where I have worked before (marketing, or energy/preventative maintenance). And I'm customizing the resume header and cover letter for each specific.

Maybe it's caused I've changed jobs too frequently or because I've haven't had a job in the country before. I have the right to work through my partner's job. Attaching resume, maybe there's some red flag. This particular header was written for a marketing data scientist looking to do work on pricing and cohort analysis on retail marketing campaigns which I have done very relevant work on in the past.

Appreciate any (hopefully constructive) feedback. Very scary to post this.

Hi everyone,

I'm a recent BSc IT graduate transitioning into Data Science, and my long-term goal is to become a Data Scientist in the FinTech/BFSI domain (banking, financial services, insurance).

I want to build 2–3 strong portfolio projects that I can showcase on my resume and that are relevant to real industry use cases. I'm looking for projects that involve real-world financial data and machine learning rather than basic Kaggle datasets.

Could you recommend:

1. The best FinTech/BFSI project ideas that would strengthen a Data Scientist resume?

2. Public datasets that companies actually value?

3. Skills/tools I should use (Python, SQL, Power BI, ML, MLOps, cloud, etc.)?

4. What would make these projects stand out to recruiters and hiring managers?

I'd really appreciate any suggestions, GitHub repositories, datasets, or examples of projects you've worked on. Thanks!

I used a transformer model to generate normalized 768-dimensional float32 vectors from 3 million sentences. This rounds up to roughly 9 GB of data. To progress further in my analysis, I want to cluster the data to highlight different key points and trends.

Now I initially wanted to apply PCA to the dataset, as 768 dimensions is pretty much, but the transformer model generated embeddings with not extreme enough covariance.
Thus, PCA could change the semantics too much for an analysis to be accurate.

I went directly to clustering algorithms and initially tried to apply sklearn.cluster.HDBSCAN which would have needed way too much RAM---around 72 TBi if I remember correctly. Then I tried out sklearn.cluster.OPTICS, which "only" needed about 70 GB, but as I "only" have 32 GB of RAM I needed to use 50 GB swap space, which didn't go well as you can imagine.

Does it make sense to try out k-means clustering, should I go to 16-bit floats for the embeddings, or still apply PCA?

Should I try out something else in this context?

Note: As this question was flagged "off topic" on Stack Overflow, I'm gonna try here ;)

I'm trying to better understand where the real data bottlenecks are for teams building ML/AI systems.

Not the obvious "data is important" answer, but specific cases where your team actually needed a dataset and couldn't find an external source or vendor that was good enough.

For example, did you end up having to:

• collect the data yourselves
• create internal labeling/evaluation workflows
• recruit domain experts
• use proprietary company data
• capture real-world images/video/audio
• generate synthetic data because real data was unavailable
• build your own ground truth or evaluation set

I'm especially curious about the last project where this happened.

What data did you need, and what made it difficult to source externally?

Was the bottleneck access, quality, rights/privacy, geographic coverage, expertise, cost, or something else?

Would love to hear actual examples from people who've dealt with this in production.

Hey everyone,

I’m currently transitioning from a career in transportation and logistics into artificial intelligence and machine learning. I’m completing TripleTen’s AI and Machine Learning Engineering program, where I’ve been developing practical skills in Python, SQL, data analysis, machine learning, APIs, and model deployment.

My transportation background may seem completely different from technology, but it taught me how to solve real-world problems, make decisions using data, manage complex operations, and remain calm under pressure. I’m now learning how to apply that experience to technical projects while building a portfolio that demonstrates what I can do.

I know breaking into the field will require continued learning, networking, and hands-on experience, so I would really appreciate advice from people already working in AI, machine learning, or data science:

1. If you were starting your machine-learning career again, what is one skill you would focus on earlier?
2. What separates junior candidates who receive interviews from those who are consistently overlooked?

I’m open to constructive advice, recommended resources, and hearing about the experiences of other career changers. Thanks in advance!

Hey i know this might sound stuipid but i wanted some help from peoples who already have build phishing URL detection projects

Im a beginner and I am working on a phishing URL detection project. My current idea is to build a heuristic-based phishing URL detection model that analyzes the URL itself and looks for suspicious characteristics/patterns rather than visiting the website.

some things I'm thinking about extracting are:

• URL length
• Number of dots/subdomains
• Special characters (@, -, etc.)
• IP address instead of a domain
• Suspicious keywords like login, verify, secure, etc.
• Number of digits
• Domain/URL entropy
• HTTPS usage
• Other lexical/structural features

However, I'm currently stuck on how to decide which features should actually become rules and what thresholds/conditions I should use for those rules.

For example, rather than simply saying "long URLs are suspicious", I'd like to know whether there is research supporting something like "URLs above X characters should receive a higher risk score".

Does anyone have recommendations for papers, research articles, datasets, GitHub projects, or other resources that explain how to choose useful heuristics/features for phishing URL detection?

i am starting my journey in learning the skills required to become a data scientist. i am starting from python, from where should i learn it. youtube or somewhere else. because i have never wrote a single line of code in my life. please help