r/analytics

Well, I am a second year student at the statistics department, and I don’t really care about being a statistician, I am more into data analytics and data science tracks.

I take a lot of rigid courses in my college where proofing is the moat important thing like we don’t take normal Linear Algebra we take it with symbols in an abstract way and proofing with different methods how the properties are applied on different matrices is the main objective not just a practical Linear Algebra.

Okay, that improved my abstract thinking, but are these kind of courses really matter? Because I go to college 5 days per week I could not take any time off to improve myself on Python or SQL, I know that some courses I take are important like calculus and others, but are they really important in this rigid way if I want to be a data analyst or data scientist?

i saw this career brought up by a few people in an autistic community on reddit mention how this career has been suitable for them and all. it got me curious and wanting to look into it more, but i felt that i should also ask around here regarding the career. is it one that is indeed suitable for those with autism? i saw specifically that the job tasks itself really click well with many of those in the spectrum (pattern seeking, collecting and cleaning data, visualization, etc), and i feel it’s something i could truly thrive in, since it’s something i tend to do elsewhere already.

my one worry regarding it is if they have a lot of office politics + involve a lot of face-to-face communication with other people?

Just as the title states, I’m a senior studying neuroscience and public health. I have experience working as a clinical data managing intern and I’m currently working as a phlebotomist/lab tech. I’m graduating next year and I’m a super senior because I stupidly decided to add on my public health major thinking my neuroscience degree wasn’t relevant. I’m regretful now, because I’m realizing I could’ve made do with the experience I currently have and just gotten a job instead of sticking myself in school longer. But maybe the coursework will be worth it. I’ve applied to tons of internships, I’m getting interviews. I’ve gotten about 5-6 since November 2025, but nothing is sticking.

I used to be pretty confident in my interview skills. I applied to a position that was pretty similar to my

last internship and the whole time the guy was like

“you already have experience in this, so I’m not going to ask you that” and he was right. But I was too chicken to admit that I really liked what I was doing and didn’t mind getting more experience in the same role at a different company. Of course, I got rejected. So I’m not sure what exactly I’m doing to put these people off. I think I know deep down it’d be better for me to not take a position and have some else experience that type of position. But they paid 30 an hour😫 I was greedy😔

I just feel a bit hopeless right now. The job market is trash. One of the talent acquisition people I spoke to told me there were 700 applicants for the position. And the decided to move forward with less than a dozen. I was supposed to hear back from them beginning March and I’m here checking the candidates home page waiting on rejection since I don’t think they’ve officially sent out rejection letters.

It leaves a sour taste in my mouth. I’m not sure why these companies do that. The first company I interviewed with literally interviewed a bunch of applicants in December and less than a week later told us that the position was entirely removed…

I interviewed with another company in February and they completely ghosted me. Interviewer corrected me at the end of the interview after I said “Thank you for your time, I appreciate the opportunity, and I hope to hear from you soon.” She goes “Oh, you’ll definitely hear from us”😭 It’s been about 8 weeks since and this is supposed to be a summer internship. While the pay was good, and it was a remote position with an active internship program for the summer, they were going to require students to do a data report analysis and have us present it to the data department. I’m lowkey glad that didn’t work out, as if school isn’t already kicking my ass. Just what I need, a several hour long project to present to a bunch of randos during peak academic season💀

Welp, Im not sure on the timeline, but I don’t think I’m hearing back. They also are familiar with the old company I used to work at and because it’s a medical device company, I guess they run in similar circles? But I ended my last internship on a good note?? My old boss gave me a handwritten letter and asked me to put her done as a reference where ever.

I know the job market is crap. I just feel super stagnant. My current lab job is “patient” facing. I work at a plasma center, it’s a busy one. The economy is trash and everyone wants to get paid. We do the work of 5 different job descriptions, but our pay doesn’t reflect it. The benefits are decent and this is the first time I’ve had health insurance in 4 years. I’ve been telling myself that this is just a temporary job, but I’ve been here the last 1.5 years. Like time is moving forward, and my feet are planted into the ground, not budging and just watching the world and everyone go by. The job is safe and they’re so desperate, the turnover is high, so job security I guess. And I am grateful, but is it wrong to want better for yourself?

What can I do to fix this feeling? What can I change? I know clinical work has taken a big hit, but I’ve been applying to clinical operation internship, data analytics, data science, biostats, public health and population care, research positions, better paying lab tech jobs. I’m not sure what else to do. I know the job market is crap and I will hang onto this crap job for dear life, but how can I improve my chances of getting out?

I’ve been considering graduate school. But I’m genuinely so burnt out. I’ve worked 2 jobs the entirety of my academic career. Since 16 honestly and I’m 22 now. I haven’t even been on the earth that long. I don’t mind working the rest of my life. It’s all I have ever know. But being able to work 1 steady job that pays well, that has all my benefits, and lets me take days off as needed is all I need. I just need to slug by in life. Clearly everyone does, and we’re all fighting for our lives for corporations that couldn’t care less about us💀

Hello! I'm currently in a degree program for IT Management, but I was thinking of switching to Data Analytics as it seems like it might be more up my alley. Another option I'm considering is Software Development. Now would be the time, as I've mostly done gen ed and classes that are in all 3 degree programs.

I want to get an idea of what you guys do each day. I'm not sure how much of my experience would be transferable, to be honest.

I've worked in payroll previously(5 years). Currently, I'm an office supervisor (3 years). Lots of Excel usage, pivot tables, if/then functions etc. No experience or classes in SQL, however.

I appreciate any info! Thank you~

I’m a Comp Sci major minoring in Data Science, and the internship hunt has been brutal. I’ve applied to over 100 roles and landed none. From talking to friends who actually got positions, it seems like they either had a "hook" through someone they knew or already had previous intern experience. As someone without those connections, it feels almost impossible to crack the market right now.

I want to use my downtime to hone my skills and make my resume look better for entry-level and internship roles. I know people say certificates aren't useful for senior roles, but I'm strictly looking for things that carry weight at the beginner level. To be clear, I’m looking for certificates, not professional certifications. Any recommendations for what actually looks good to a recruiter when you have zero experience?

For context I have a bachelors in psychology from a school in NYC. I have no plans of continuing in the field of psychology or mental health hence why I am here. I recently applied to a masters program in information systems with a specialization in data analytics at a CUNY school to which I got accepted to. Now I am fully aware that a masters does not guarantee me anything but I will be attending with the hope and confidence that I will end up doing data analytics in some way shape or form. I also do plan on doing some internships with the hope of getting hired doing entry level work. Hopefully early during my masters.

With all that said my question is am I over my head here? This program caters to individuals with no technical experience whatsoever and it is a program that will prepare you to be a data analyst. I saw some of the courses and some offered are object-oriented programming, programming for analytics and data visualization just to name a few. Am I making a good choice here? Any help is greatly appreciated.

What is the nicest or most creative way you have seen someone use Markdown formatting in a Reddit post?

Hey everyone,

I have my Capital One Senior Data Analyst Power Day interview coming up and was wondering if anyone here has gone through it recently. I’d greatly appreciate any insights.

Thanks!

A lot of AI-BI tools are starting to push toward semantic understanding rather than just dashboards.

Platforms like ThoughtSpot, Looker, Power BI (Copilot), Qlik, QuickSight, and Sigma all seem to be moving in that direction. On the other side, newer tools like Julius AI and Lumenn AI feel built around this idea from the start, using dataset context, metadata awareness, and LLM reasoning to explore data without heavy manual querying.

It makes me wonder what’s enabling this shift under the hood. Are these tools increasingly relying on metadata-aware data layers (like dbt Semantic Layer, Cube, AtScale, Omni) and LLM capabilities to understand datasets and generate insights? If so, where do the bottlenecks show up, inconsistent metrics, weak metadata, hallucinated joins, governance issues, or trust in AI-generated answers?

If this matures, the shift in BI could be pretty big, moving from manually building dashboards to AI-driven exploration, with analysts focusing more on validation, metric design, and decision support.

Curious how others are seeing this, are these tools actually improving trust in analytics, or just moving the bottleneck from SQL to metadata quality?

I have 10+ years experience in Data engineering/analytics/machine learning. Actively looking part time roles. Any help will be greatly appreciated.

PS: genuine only please.

As low-value cards ranging from 2 to 6 become concentrated within the deck, a noticeable decline in the dealer’s bust probability occurs, leading to more stable hand completion. This phenomenon stems from the mandatory hit rule under 17, where lower-value cards function as a systemic buffer, reducing the risk of busting while facilitating progression toward the target score. In practical operations, such biased data segments often prompt a more conservative adjustment in bet sizing or an elevation of stand thresholds to manage probabilistic risk. Within the analytical framework of Oncastudy, what specific data indicators do you rely on to determine the optimal timing for strategic response when such low-card clustering creates a dealer-favorable environment?

Same pipeline breaking pattern for the third time in a row. Pipeline runs clean, dashboard is wrong in the morning. Someone upstream changed a field type, or a column started going null, or a new field showed up that nobody told us about. The existing checks (dbt tests, Great Expectations) only caught it after the data was already sitting in the warehouse. Two days of bad rows before anyone noticed.

I got tired of it.

We stuck a screening step between extract and load. Basically an API call that looks at the payload before it touches the database. Sends back PASS, WARN, or BLOCK depending on what it finds.

source → screen → PASS → load to warehouse
               → WARN → load + flag
               → BLOCK → dead letter queue

It checks for the usual stuff — null rates spiking, type mismatches (a field that was always numeric now has strings mixed in), schema changes (new fields, missing fields), duplicate rates, outlier counts. 18 checks total, single pass, comes back in under 10ms so it doesn't slow anything down.

The part that surprised me: the big wins aren't the obvious failures. Those you'd catch eventually. It's the slow drift. Null rates creeping up 2% per week. A field that's 97% numeric and 3% string. Nobody notices until it's been wrong for a month and your ML model has been training on garbage.

We baseline the schema on first run (SHA-256 fingerprint of field names + types), then compare every batch after that. Null rate baselines use exponential moving average so they adapt gradually. If your data legitimately changes over time, it doesn't keep firing. But if something jumps overnight, it catches it.

The tradeoff with EMA baselines: if your data has been broken for long enough, the baseline learns the broken state. Haven't fully solved that one yet. Manual reset works but it's not great.

Runs on Cloudflare Workers, so no data hits disk. Everything is in memory, only stores aggregate stats (fingerprints, null rates, type distributions).

Anyone else doing quality checks pre-storage? Most tooling I've seen is post-load. Curious if there's a pattern I'm missing or if everyone just lives with the "fix it after it breaks" approach.

주말 피크 타임마다 보너스 지급이나 정산 처리가 유독 늦어지는 트랜잭션 지연 현상이 반복적으로 관찰되고 있습니다. 이는 단순 과부하보다는 운영 인력이 적은 틈을 타 사용자의 심리를 자극하고 자금 흐름을 통제하려는 의도적인 설계로 보입니다. 시스템 측면에서는 트래픽 급증 시에도 결제 우선순위를 유지하는 자동 큐 분산과 실시간 이상 징후 탐지 기술이 필요합니다. 혹시 다른 플랫폼 운영 환경에서도 이런 인위적인 지연 패턴을 감지하여 차단하고 있는 구체적인 사례가 있을까요?

접속 시마다 그래픽 옵션이나 UI 설정이 초기값으로 돌아가는 현상이 여러 운영 환경에서 반복 관찰됩니다. 이는 단순한 코드 누락보다 클라이언트와 서버 간의 상태 동기화 설계가 후순위로 밀려난 구조적 문제로 보입니다. 세션 종료 시 데이터 기록의 무결성을 검증하는 프로세스를 강화하여 시스템의 지속성을 확보하는 대응이 요구됩니다. 이런 데이터 휘발성 문제를 원천적으로 방지하기 위해 설계 단계에서 가장 놓치기 쉬운 포인트는 무엇일까요?

Zentra construction, week 1 completed.

My observation: analysts at fintech startups keep asking the same questions regarding finances all the time. Revenue report, transactions, profit margin? Every single time they ask, someone has to go into the database to find the answers.

How about making the system smarter?

I built a solution that uses Claude AI to give answers to financial data questions in natural language. However, there's one important feature: the system learns! So, when the user asks "How much was our Q1 revenue?" right now, he receives the answer, while tomorrow he gets the exact same answer from memory under 50 milliseconds!

This week I worked on developing the engine. The engine connects to your database (reads only), however, the user does not interact with the database – he interacts with the engine, which is intelligent enough to remember everything.

However, the caching layer is the true game changer. While most other solutions recreate an answer each time, consuming all of your API costs, we cache everything! Your database updates only when it needs to, but we know which data is fresh and which one has become stale. Fast, accurate, and saving on AI API costs!

It's week 1 of much more to come. Currently, we have implemented only the backend part of it. The UI will go live next week.

Any suggestions are appreciated!