Data Science
Frontend in data science

Hello, I've been wondering if we need to learn html css and javascript as part of being a data scientist, because honestly I think that the data science field is so broad and there is so much to learn than focusing on frontend dev, so tell me if we really need it in the job. If so what are other alternatives that data scientists use for visualisation?

submitted by /u/saabiiii
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Data Science
Seeking Referrals and Tips for a Recent Graduate's Job Search in Data Science and Machine Learning

https://a.thumbs.redditmedia.com/yj-Tv-byYKRjMzF7uf1mUJSNocvQbsGRJgcLyHV3Nu4.jpg Is there anyone willing to refer a recent graduate? I have applied to approximately 300 positions within the fields of data science, data analysis, and machine learning, with the majority being entry-level roles. Unfortunately, I haven't received a single call yet. I would greatly appreciate any tips or referrals. I have also attached my resume and would be thankful for any feedback you can provide. Thank you in advance.

N.B. Personal information is blurred in the image.

​

https://preview.redd.it/wlcw1g218twb1.jpg?width=725&format=pjpg&auto=webp&s=b4ecaa0d76c88060e5eab117a68f8daa09a0d816

submitted by /u/sahaavi
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Data Science
Jpmorgan JPMorgan Chase & Co. AIML FAST Info Session - October 27, 2023

Who else got invited for this and is unable to access the zoom link ?

submitted by /u/Safe-Jellyfish9099
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Data Science
Computer processing requirements for Tableau and Looker Studio

Not sure where to post this. If there's a better place or just another place, let me know.

I'm interested in working with Tableau and also Looker Studio. The paid version of Tableau has a lite version of Tableau Server from what I understand, and I have someone who can set that up on their server for me.

My computer is older. I'm wondering if I should get a new one with more power. I'm not an expert on either program. I do have data sources for them. From what I understand, at least for Tableau, I can prepare a report on the local machine, and that requires more processing (meaning I might need a new computer). But if I put a report on a Tableau Server, that's using the processing on that server (meaning my old computer might be good enough). For Google's Looker Studio that's all web-based from what I see, so the processing happens on Google's servers and not my local machine? Meaning an old computer or a new computer will still get the same performance from Looker Studio.

My old computer is good enough to run Office, browse the web, play a video. If I can keep using it, great. I don't want to buy a new, more powerful computer if that computer's processing isn't actually used though. If Tableau Server or Google does the actual number crunching, I don't want to buy a new computer if the new computer won't actually impact anything there. Sure, a new computer is great, but if it's not providing any data processing improvement, then it's not worth the cost.

submitted by /u/datasciencejd
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Data Science
Been put to investigate bugs for new project but no prior exp

Hi there!

I’m working as a Data Analyst in my company and in my team we mostly use SQL and Tableau. I’ve mostly just used these two and Python (via Jupyter notebooks) on occasion to perform data cleaning /transformation for adhoc data sets.

So recently been covering for some other employee and have seemingly gotten myself into potentially being the one having to fix some potential bugs in a ML based Flask application that predicts product prices based on different conditions.

This is made up of 3 GitHub repos: The model, the data pipelines and a Jupyter notebook containing code related to KMeans.

The data pipeline and model repos contain lots of Python source files with around 1000-1500 lines per file. All in all there could be easily more than 20,000 lines of code. I know this is not a lot but I don’t have experience in dealing with such large code bases.

I don’t have background in ML or product development (I previously worked as a IT BA 2-3 years back before transitioning to a DA role after having used SQL/Tableau for a few years, there is a separate BI team in the company but I’m in a data analyst specific team).

My question is would it be common for DAs to be called to debug large complex ML web apps? I haven’t seen this in other companies previously. I would have thought this would fall on the product development teams or ML Engineers etc.

And what is the best way for me to start off getting used to the code base and understanding what everything does? The project certainly looks interesting and would make a good entry for me to a ML engineer role in the future or product development role but I’m nervous especially since my probation is ending in 2 weeks and I really don’t wanna f up. There’s no documentation or requirements documented except for a high level architecture diagram of the system.

Looking for advice thanks!

TL:DR; A data analyst with no experience in product development put in charge to fix bugs for a large ML web app, looking for tips on how best to understand the code base and perform testing especially when there’s no documentation available for this app besides a high level architecture diagram. Also, wondering if it’s common for a data analysts to be asked to debug large ML web applications (flask based).

submitted by /u/Fine_Night_
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Data Science
Take your Streamlit apps to the next level with GPT-4

My post on how to level up your Streamlit app development using GPT-4 is now live on the Streamlit blog! 🔥

If you're interested in designing, debugging, and optimizing Streamlit apps with OpenAI's GPT-4, this one's for you!

🔗 https://blog.streamlit.io/take-your-streamlit-apps-to-the-next-level-with-gpt-4/

submitted by /u/DataChaz
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Data Science
jre on Eclipse isn't activating automatically

https://b.thumbs.redditmedia.com/xL2rtNX0opaf_NR2175uCS7ebobJ0JIyNMEEJAy6_hg.jpg Hello everybody, my problem is that when I create a new Java project on Eclipse, I always have to Build path, configure... to add JRE library manually, so is there any method to solve this problem, to create automatically jre library into new project?Thanks for your time!

https://preview.redd.it/o4ybtr78ltwb1.png?width=154&format=png&auto=webp&s=dccb64d38243ef84ab93ea0f586a53ef6fde5791

submitted by /u/HBlueberry05
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Data Science
Companies that hire entry level

Hello!

I am currently in a masters data science program and have about a year left, I am also pursuing an MBA as part of a dual degree. This is a huge transition from my undergrad and my work experience which is all related to behavioral health.

Are there any companies that will hire entry level data scientists with a masters but no work experience? I have looked a little for an internship, haven't found anything but would be open to that as well. I am in New Mexico and not looking to relocate right now but I certainly would in the future, but remote work would be ideal for me for a couple years.

As I stated before, I have some time before graduation but I don't want to be scrambling after graduation to find something. Any suggestions or places to look would be awesome. (Also, working for the national labs/ DOE/DOD here is not an option right now as I am pretty sure I would not be able to obtain a security clearance.)

​

Thanks for any and all suggestions!

submitted by /u/sjagigi15
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Data Science
Should I take a Data Engineer job

I'm a fresh econometrics BSc graduate, and I was offered a 3-month-long data engineer internship that pretty much guarantees a long-term junior position afterwards.

Thing is, I kinda hate data engineering. I hate working with unclean data and I don't find the process of creating/maintaining data pipelines appealing at all. What I really want to do is business/data analytics with machine learning/statistical regressions.

Normally, I would just take the job offer because it somewhat aligns with my career path, be grateful for it, and put it on my resume for the foreseeable future, but...

I only sent out like 30 resumes in the span of a few weeks, so getting this job felt quite easy to be honest. It just makes me think that I'm much more valuable on the market than I had initially believed, but then again - I don't know how much of this is just luck and if it's a replicable feat at all.

Don't get me wrong, I feel very grateful for getting a good offer this quickly in such a competitive environment, and this isn't supposed to be a "ha, look at the great Me, peasants" type of post. I just genuinely don't know what I should do.

In 2 years I will graduate with a master's in data science and business analytics. When that happens, I will definitely only look for jobs that fully align with what I want to do.

But until then, I guess my question boils down to: how nicely will 2 years as a data engineer look on my resume applying for data science jobs? Should I risk it for a better job offer for now or should I play safely and get what they offer?

I guess there is an option of being an asshole, pretending that I'm fully onboard, but actually being ready to ditch them at a moment's notice. It's something I'd like to avoid, though, unless you - likely better-versed in how the job market works - think it's the best option.

Sorry for a long post and thanks for any help in advance.

submitted by /u/Azatras
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Data Science
Computer science

Is the specialist "department" in computer science is necessary in job or i can graduate with any computer science department?

submitted by /u/Ok-Yak-7465
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Data Science
What should I do?

Hi, I’ll be graduating soon (Ireland) and would like sons advice on good first steps.

I have a 4.0 GPA in mathematics and statistics, experience in R, Python and SQL. I’ve done an international internship in tech deployment.

Most opportunities in Ireland are graduate analytics programs with consulting companies, I feel like these would be really under stimulating for me.

What would you do if you were me?

submitted by /u/Typical-Strength-583
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Data Science
Australia: Government vs Private Graduate Programs

Hi all,

I am a student at a Go8 uni studying a Bachelors degree in data analytics and will be graduating at the end of 2024. I am in my early 20s.

I currently work for a local Australian Government department in the digital space and will continue working in this position until graduation. I study part-time and work part-time.

I will engage in a big 4 vacationer program in data analytics consulting this year and am very grateful to have been exposed to these opportunities.

My main priority is acquiring the best training and experience to accelerate my career progression as fast as possible. I am a bit of a workaholic as is and I have no issue working long hours.

Would it be more beneficial to go into a private sector or a Government grad program given my priority?

I have not come from a particularly wealthy family and it has always been my dream to end up on Wall Street or the like in a high managerial position (I know it's completely unrealistic but I'm still going to try as hard as I can). I am not fortunate enough to rely on nepotism for career opportunities and will have to focus on technical skills and relationship management/development.

If anyone could provide any advice on this it would be greatly appreciated.

If anyone is aware of any other more applicable subreddits in answering this question, please comment.

Kind regards and all the best.

submitted by /u/throwaway78293762
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Data Science
How to get started as a Data Analyst?

Ive been looking for Data Analyst jobs on indeed in Los Angeles but I cant really find any jobs.

Should I be using a different website?

submitted by /u/Nervous-Newt848
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Data Science
Major in CS+DS or CS+Stats

Title basically explains it. I’m a freshman looking to go into Data Science and probably eventually ML. CS+DS seems like it’d be a bit easier and maybe a little more interesting for me, but would it be worth it to switch to CS+Stats in terms of job marketability and also just what I need to know and learn to go into this field?

submitted by /u/xx_geraltofrivia_xx
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Data Science
The Challenge of Deduplication in Recommender Systems

Hey fellow data enthusiasts!

Ever faced challenges managing vast user history in recommendation systems at scale to avoid duplication recommendation to end users. I've just penned an article on the significance of deduplication and how Bloom filters can be effectively used around the same

I explore: ✅ Challenges in managing vast user histories ✅ Solutions & trade-offs in efficiently capturing user interactions ✅ How scalable Bloom filters and Cuckoo Bloom filters can be pivotal

https://medium.com/@sundeepkumars/4b2f558e9f57

Would love to hear your thoughts, feedback, or experiences related to this. Let's elevate our recommendation game! 🔍🚀

submitted by /u/sundeepkumars
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Data Science
Has anyone successfully used Chernoff faces in any type of analysis (not including what they are and how they convey information)?

I was reading through the supplemental material at the end of Blindsight and discovered that Chernoff faces are real. The in-story explanation that human brains are hardwired to read faces and the amount of information that can be encoded into them is suitable for higher dimensional (~18) data, especially given that the subconscious is more adept at processing complex problems than the conscious mind, is interesting enough of a concept, so discovering that it's real makes it even better.

I'm interested to see if they have been utilized before and if they still are in certain industries or niches (outside of customer satisfaction surveys and Wong-Baker scales). The fact that it can encode a high number of dimensions has piqued my interest to see if they can be used successfully and how difficult they are to interpret, for both analysts/statisticians and non-technical parties (stakeholders).

submitted by /u/WadeEffingWilson
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Data Science
Hpe hackker rank

Hey guys I just got hpe DS undergrad intern first round hackkerrank tech assessment.

I only have taken swe part for the OA, and not sure what to expect from DS hackkerrank.

Can anyone who has taken HPE OA please tell me what kind of questions you get.

submitted by /u/sfdssadfds
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Data Science
ediscovery Data Analyst

I’ll be interviewing for this position next week. Does anyone have experience In This particular department and can offer a few bits of advice?

submitted by /u/aarmobley
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Data Science
MS in DS - help focus learning track

I started a MS in data science last year. I’ve finished most of the required core classes, and now I need to take a couple independent studies and do my thesis project (all of these can be related or completely unrelated). I’m really having problems deciding on what to focus on here…there are so many stats and ML topics that I find so interesting, but I just can’t learn it all!

My main interests right now are unsupervised clustering, dimensionality reduction/working with high dimensional data, neural nets, and spatial analysis. Even just narrowing my list down to these subjects was difficult! Is it better to know a little about multiple subjects at this point, or specialize on 1-2 topics? Any advice on what you think would serve me the best in the long run would be much appreciated!

submitted by /u/Head-Hole
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Data Science
When do you select features to use for your model?

My issue is about doing EDA before or after feature selection. For example. say I have a dataset with tons and tons of features. Am I expected to analyze each and every feature in the dataset before choosing features or can I choose features that "may" matter based on logic and examine them there?

submitted by /u/Frosty_Pitch9052
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