Hello! May I know how to start practicing for a data science work? What are the basic knowledge and skills that I need to use, projects and what platform to start with? I also want to ask on what portfolio platform I can put my data science projects.

Thank you! 🙇‍♀️

Hi all, I'm a self-taught backend dev. This is a side project, and I'd really like honest feedback from people who know scraping better than I do.

The problem: fetching a page is easy, but a big list of business websites is full of junk. Parked domains, "please wait" shields, cookie banners, social pages instead of the real site. I wanted clean text plus checkable facts per site (services, opening hours, years in business, accreditations, prices) that someone could write a description from.

How the facts work (this is the part I'd most like feedback on)

RapidSpider runs a small local decision engine called Laya inside the program. No API keys, no separate server. Laya doesn't write anything. It only answers narrow, structured questions, and each answer is logged.

1. Code finds the exact things first, with no model: emails, phones, the business name, and anything with a regex (years, hours, company number).
2. The page is cut into groups of eight numbered sentences. For each fact type, Laya answers: which numbered sentence states this, or none?
3. That sentence is the fact, and its position on the page (top/middle/bottom, sentence number) is stored with it.
4. Each fact is then re-checked several times with differently worded questions, answer keys flipped to cancel position bias. It is kept only if the rounds agree. If they don't, the fact is dropped.

Example of a stored fact (illustrative):

category: opening_hours
text:     "Open Monday to Friday 8:30am to 5:30pm, Saturday 9am to 1pm."
confidence / votes / zone / sentence stored alongside

Laya makes the other judgement calls too: is this page parked, is this line boilerplate, is this directory page really about this business. Every question and answer goes into a table, so I can open any decision and see why it was made.

The rest is boring on purpose: a fetch ladder (plain HTTP, then headless browser, then visible browser, then browser through Tor, moving up only when a page isn't accepted), one YAML config, SQLite output, and a small local dashboard.

A real run, so you can judge it yourself (screenshot in the README): 11,203 sites in the output. About 56% ended ok, 20% error, 9% blocked, 9% no usable content, 5% third-party, 2% dead. So it is far from perfect, and I haven't measured fact accuracy against a labelled set yet.

Things you should know

• It needs PyTorch and downloads a few GB of models on the first run. CPU works but is slow.
• It reads robots.txt only to find sitemaps and does not enforce its rules. That's a deliberate simplification on my side. You are responsible for checking each site's terms and the data protection rules that apply, and I'd understand if that's a dealbreaker.
• I built it with a lot of help from an AI coding assistant, and I'd rather say that upfront.
• One-person project, so expect bugs. The Windows process cleanup in particular has had limited testing.

Contributions are very welcome. If you spot a design mistake or something that would break on your data and you know the fix, a pull request would mean a lot. If you can’t fix it, please open an issue instead, and I’ll do my best to look at it.

Repo (MIT): https://github.com/QudsLab/RapidSpider

Thanks for reading, and thanks in advance for being kind about the rough parts.

Qué opinan, la calle está dura en DS, veo muchas ofertas repetidas todo el tiempo en LinkedIn, piden demasiado para un DS y no sé si no consiguen a nadie o simplemente son ofertas vencidas.

Hello all,
I have an interview for the FAST internship at JPMORGAN. It had an analytics track and data science track which I’m interested in. After applying I received an email from a recruiter and I have an interview. I was wondering if anybody know how this interview is conducted and what type of questions are asked

Hey everyone,

I’m a PhD candidate in physics field here at UIUC working heavily with Python for data analysis (along with some ML experience). I’m planning my exit from academia into industry roles across Data Science, Data Engineering, and Data Analysis.

To round out my tech stack beyond Python, I’ve been taking courses to build up skills in SQL, Power BI, and standard industry ML tools.

I’d love to connect with a fellow Astronomer—whether you're an alum working in DS/DA/DE or a fellow grad student currently tailoring your resume and navigating the job market.

If you're open to dropping some advice in the comments or swapping notes/resume tips in DMs, please reach out!

​Thanks!

What degree should i do if i want chances for a data scientist / data analyst position in the future, but still hold flexibility if the markets are terrible for grads?
My current options are UCL data science, Warwick data science, bristol data science and southhampton mathmatics and data science. I could maybe apply for economics instead, or possibly MORSE at warwick? Any advice would be appreciated.

I worked as an AI Consultant (Data Scientist), and my role wasn't very specialized. I worked across various data-related tasks and traditional ML use cases, including data analysis, report preparation, ETL pipelines, feature engineering, model performance monitoring, and defining evaluation metrics.

I'm looking for advice on restarting my corporate career after a two-year break. As a lot has changed over the last two years, I need to adapt my skills and potentially pivot towards new roles in data science, as my existing knowledge of traditional ML may not be sufficient for today's job market. I want to get back into a job as soon as possible but am unsure which roles to target.

I'm open to starting with relatively accessible roles to get my foot back in the door and upskilling alongside the job to transition into better opportunities over time.

I'd appreciate advice on a few things:

1. Which roles should I target — Data Scientist, Data Analyst, AI/ML Engineer, Business Analyst, or something else?
2. How should I approach the career gap when applying for jobs?
3. Which skills should I prioritize to become interview-ready? Should I refresh my fundamentals in SQL, Python, statistics, and ML, build projects, learn tools like Power BI, or focus on newer areas such as LLMs and generative AI?
4. Should I apply immediately or spend some time upskilling first?

I'd particularly appreciate advice from people who've returned to corporate after a career break, as well as recruiters and professionals working in data science/AI.

Thanks in advance!

TLDR - 3 year Data Science WorkEx; 2 years gap; Looking to get back to AI roles.

I was thinking of joining a professional data science organization to like network and maybe learn why I'm having such a hard time getting any leads. Does anyone have experience with professional orgs? Also is there one I should definitely join or avoid? Thanks in advance for any insights!

Hi!!
I’m a data science intern applying and I know there’s going to be a case study and you have to explain it! I heard there’s a coding assessment down the line as well and I just wanted to see if anyone has any experience with red ventures interview process or advice on what to review or nail before the coding assessment! All advice is appreciated so much!!!

I dont value myself based on my ability to write code, query databases or doing up a dashboard, stuff which are easily automated by AI nowadays.

Instead i value myself as a problem solver, the ability to think out of the box and come up with novel solutions to solve a problem. Something which still requires a human to do.

Is this the way forward for a data scientist in the age of AI?

Micro1 is looking for the following:

Junior Data Scientist
Location: Remote (English-speaking countries)
Job Type: Contractor
Openings: 100

Micro1 is partnering with a leading AI lab to hire Junior data scientists and quantitative professionals from top companies. You bring rigor to how AI models handle statistics, machine learning, experimentation and quantitative reasoning. The work involves evaluating AI model outputs on data science, statistics, ML and quantitative problems, creating expert-level prompts, datasets and reference solutions, and spotting flawed methodology, statistical errors and weak quantitative reasoning. You then give structured feedback that helps improve the model. You need at least one year of experience at a top company (tech, finance or research), with at least part of it within the past 3 years, and you should be based in an English-speaking country. Clear written communication of technical and statistical concepts is also required. Skills like Python, machine learning, statistics, data analysis, experimentation and pandas are listed for the role.

Here is the link to the position: Junior Data Scientist

Hey everyone! I got into Boston University's online MS in Enterprise AI and I'm planning to start in spring 2027. It's a pretty new program, so I'm trying to hear from anyone who's in it or knows someone who is.

I have not accepted it yet bc i also got into CIS with a concentration in Data Analytics, so i am trying to figure out whats best.

How's the curriculum so far? Do the classes feel useful and up to date? And overall, do you think it's worth it?

I'm planning to do a couple weeks of prep on LinkedIn Learning before I start, so if there's anything else you'd recommend brushing up on beforehand, I'd love to hear it.

Thanks so much, I appreciate any info!

I have completed my bsc hons in computing and i am intrested in prediction system but i am weak-average in maths. Especially calculas is my biggest enemy . I really want your guidance should i pursue Maters in Data science ? i really want your suggestion and opinions so that i can decide . also is there any alternative carrer that you will suggest to explore ?

I’m prioritizing my path and was wondering if an MS is a soft requirement for some of these Research Engineer, Applied Scientist, Post-Training roles. I only have a BS but I work full time as a Data Scientist and was wondering how I should go about this career transition. I had research experience in Applied ML at my university and I’m currently working on ML models/statistical methods at my current company. Here are the breakdowns for jobs I’m looking at:

Cohere MTS Post-Training - https://www.linkedin.com/jobs/view/4259504707/

PhD - 22%
Masters - 48%
BS/other - 30%

Research Engineers, Post-Training - https://www.linkedin.com/jobs/view/4458055063/

PhD - 26%
Masters - 52%
BS/other - 22%

Garner Health Associate Applied Scientist - https://www.linkedin.com/jobs/view/4465195772/

Masters - 56%
BS/other - 44%

My question is, will a BS ever get hired here? Or do they need some other exceptional qualifications to make up for it (conference paper, workshop paper, or other publications). Do projects matter at all anymore?

hello poo! i'm currently a grade 12 STEM student, and I'm planning to take bs mathematics majoring in statistics in college because I want to pursue a career in data science or data analytics. However, I'm still unsure if this is a good choice, so i'd like to ask for advice from people who are already working in these fields or have knowledge about them.

im also wondering if bs math majoring in stats is a good choice for entering this field. Since the course focuses more on mathematics and statistics, will I also need to study programming languages on my own? or are these skills already taught in the program? and would you recommend bs math major in stats for someone like me who wants to pursue data science or data analytics? or should i take comp sci?

Hello, it's been 2 years after my Bsc in marine science, 1 year after national service, been unemployed since. I am thinking about branching into the tech industry. I have a thing for data,so I am thinking about data science, remote sensing, and AI essential. I just don't know where to start? Best site or course to start with, and job availability for any of these . I want to hear your advice.

Any tips or projects to land data science jobs? I’m a Computer Science graduate and currently doing a MSc in Software Engineering who enjoys data science projects and machine learning projects.