Hi, I'm a student and I need some advice about data for food recommendation system project. I proposed to my teammate a dataset that containing the foods' ingredients with around 600 columns, each column is a single kind of ingredient which containing Boolean values (1 if the food contains that ingredient and 0 if the food doesn't contain any of that kind of ingredient). In my perspective, that kind of data design is kinda complex but really easy to process, efficient for data analyst. But my teammate say it weird, idk what is his reason, I asked him but he just said he has never seen this ki ..read more

I am new to industry and I don't seem to find a proper answer to this question. I know Data Scienctist is expected to model. Train models do Post Production Monitoring. Fine-tuning and maybe retraining. Apparently retraining involves a lot of beaurcratic hoops. Maybe some production . Data engineers would do preprocessing, ETL , building Warehouse ,SQL queries, CI/CD. Pipeline and scraping. To some extent data scientists do it. Dont feel comfortable personally but doable. Not the best coder but good enough to write psuedocode and gpt ky way out Analysts will do insights and EDA. THAT PRETTY M ..read more

I’m curious what it’s like to freelance or do contract work/consulting. I’d love to hear your experience, how you gained clients, how long it took you to replace a normal salary etc. Did you use Upwork or network on LinkedIn. submitted by /u/Uncle_Cheeto [visit reddit] [comments ..read more

Hey guys, I'm working on finishing up my data science degree, with a high possibility to go into a masters. I'm currently holding a good job in aero space with a lot of background in aero space manufacturing. I really like the job and it pays well, but I'm lost for what field or job I can incorporate my aero space experience and data science. If anyone can offer any guidance, that would be great. submitted by /u/oppapoocow [visit reddit] [comments ..read more

I currently work as an MLE(~5 YOE) focused mainly in computer vision. I'm looking towards the future and trying to figure out next steps for my career and how to up-skill. I find myself gravitating towards working on product with ML as part of core-value-added and can't see myself working on ML/DS for analytics. Web-dev has always seemed interesting to me - specifically front-end dev (Eg. React, WebGL, Three.js). I have a limited amount of time to invest in up-skilling and am debating whether there's any justification for investing the time here. Is there a niche for an ML-Engineer with front ..read more

I've been working as a DS in predictive analytics a good amount of years now, and recently I've been pushed to delve a bit more on the more data viz side, which eeeehh fortunately or unfortunately meant coding web-dev stuff. I have realized that in the web-dev side, there are a shit ton of people building and consuming boilerplates. Like for real, a mind-blowing amount of demand for such things that I would not have expected in my life. However, I've never seen anything similar for DS projects. Sure, there's decent documentation and examples in most libraries, but it's still far from the conv ..read more

Has anyone gone through the interview process, in particular the live coding part and have any insight on what I should expect or any tips. submitted by /u/LebrawnJames416 [visit reddit] [comments ..read more

Do you think that AI can help data science teams beyond just "ask data / text2sql" chatbots? I've been in DS for a while, both as an IC and now as a lead. Despite improvements in analytics tools and workflows, getting to meaningful insights is still a very labor intensive and code heavy process. It especially takes a significant time to perform exploratory/foundational analysis, which can limit the ability to tackle more specialized problems and also increases the risk of overlooking something entirely. I've been building something to address this, here's the elevator pitch: an autonomous AI ..read more

When doing one class classification using one class svm, the basic idea is to minimize the hypersphere of the single class of examples in training data and consider all the other smaples on the outside of the hypersphere as outliers. this how fingerprint detector on your phone works, and since overfitting is when the model memorises your data, why then overfirtting is a bad thing here ? Cuz our goal from the one class classification is for our model to recognize the single class we give it, so if the model manges to memories all the data we give it, why overfitting is a bad thing in this algo ..read more

Hi, I'm a first-year mathematics student, and I've been getting interested in data science lately, but I'm still a bit lost. I'm not sure if I really like it because I haven't done any projects yet. Could you recommend personal projects for me to get to know what it's like to work in this field?" submitted by /u/Low-Pack4738 [visit reddit] [comments ..read more