u/disforwork

maybe you can check out this data science learning path from interview query? it covers topics and questions that typically come up during your interviews, so you can base your career prep on its structure.

Business programs teach R because their professors are old-school stats nerds who've been using it forever and the syntax is actually decent for pure statistical analysis without needing to understand broader programming concepts. Data science programs push Python because it evolved from CS departments and is way more versatile (ML, web dev, automation) even if it requires more coding fundamentals to get started. The industry heavily favors Python these days (just look at job postings), but R still has its niche for quick statistical work and fancy visualizations - plus ggplot2 is objectively better than matplotlib and I'll die on that hill. If you're prepping for a data analyst interview, this has a solid breakdown of common questions and concepts to expect.

Amazon’s SQL assessment usually has a mix of multiple-choice and coding questions, mostly on JOINS, window functions, and aggregations. Whether it is proctored depends on the role. Some say it is, some say it is not. Expect medium-difficulty queries that test efficiency and edge cases. If you want to prep, try solving SQL questions on Leetcode or similar platforms. You can also check out this for a breakdown of common topics and example questions.

Bro, congrats on making your first sale! That's literally more than 90% of side projects ever achieve - you're already crushing it! With your experience and the fact you've actually shipped something that makes money (unlike the rest of us dreaming about our "revolutionary app ideas"), you're at a God-tier crossroads.

If your side project has actual product-market fit, I'd absolutely double down on it - maybe spend 3-6 months seeing if you can grow that bad boy into something substantial before selling your soul to FAANG. That said, grinding DSA and system design is never wasted time in our hellscape of an industry and could be your backup plan while you work on scaling your product to the moon. Whatever you decide, don't undervalue that entrepreneurial streak - companies will simp HARD for devs who understand how to build products people actually throw money at.

A few months ago, I've built a study tool that converts my messy research notes into organized mind maps and flashcards.

If you guys are looking for some more AI-Project ideas, I linked a good resource from Interview Query!

I think the focus is mostly on strategy, team leadership, and business impact, but some companies still include case studies or high-level technical discussions. You probably won’t have to code, but you might need to walk through past projects and explain decision-making. There might be behavioral questions on managing teams, handling stakeholders, and driving data initiatives. Some throw in a case study to assess problem-solving. If you want to prep, looking at data strategy case studies and leadership frameworks could help. You may want to check this one for case studies.

For a more updated one, Lucid’s final round is usually a mix of coding (DSA), system design (if applicable), and behavioral questions. They care a lot about communication, so make sure to explain your thought process clearly. You might also get some product-related questions, especially if the role involves full-stack or frontend work. If you haven’t already, brushing up on medium LeetCode problems and common behavioral questions will help. You can also check out this for a breakdown of what to expect.

A lot of off-cycle internships aren’t posted in the usual places, so checking individual company career pages can help. Some research labs and university-affiliated programs also have rolling applications, especially in CS-related fields. If you’re open to smaller startups, Wellfound (formerly AngelList) sometimes has good listings. You could also try networking through professors or alumni. Some opportunities don’t make it to job boards. Hope you find something solid! This guide also breaks down different paths for getting into data roles, which might help you find alternative opportunities

AlgoExpert is decent if you like structured video explanations and curated problems, but it’s not the best if you struggle with coding questions and need more hand-holding. It’s better for people who already have a decent grasp of fundamentals and want to polish their skills. Since you got it for free, might as well try it and see if it clicks for you. If it feels too overwhelming, mix it with more guided resources like CS50 or interactive Python tutorials. You might also find this guide helpful for practicing data science-specific coding questions.

Check out places like Khan Academy, Duolingo, or Coursera if you’re into education. OpenAI, Hugging Face, and Stability AI are interesting if you like AI but want something outside big tech. There are also climate-focused companies like Rivian, BlocPower, or smaller startups in clean energy. If you’re into social good, nonprofits like Code for America or the Wikimedia Foundation might be worth a look. Maybe browse Built In’s impact companies list for more ideas, or check out this for a broader list of companies hiring in data science.

I learned most stuff from this DSA course

Hi, I recommend checking out this website with peer practice sessions. You'd get matched to another user for a mock interview sessions, which could probably help you with this. Good luck!

Typically, it's SQL. In most interviews, the primary focus is on assessing your foundational Python skills. Interviewers typically want to see that you can confidently write scripts and basic functions to manage data, like transferring it between SQL databases, Excel spreadsheets, or displaying it on a dashboard.

I've read this blog before about Data Analyst interview questions and it shows questions like Bigrams, Negative indexing, and Sequence unpacking.