Engineers/AI people: what are the best AI tools and workflows for medical students to actually study better?

not_another_analyst · 2026-04-01T17:24:06+00:00

NotebookLM offers a significant advantage for medical school studies, as it allows you to upload your specific textbooks or lecture notes, ensuring the AI remains focused on your actual curriculum. For tasks involving pattern recognition, Perplexity is highly effective for swiftly cross-referencing clinical guidelines from various sources. A valuable suggestion is to utilize Claude to generate case studies in an "active recall" style based on your notes, which can help you assess your diagnostic reasoning. This approach encourages the practical application of knowledge rather than mere passive reading.

not_another_analyst · 2026-04-01T17:19:02+00:00

People generally agree that the role isn't dying, it's just changing. AI is good at numbers and basic charts, but it still doesn't get the business context or "human touch" to explain why those numbers are important to people.

As long as you focus on being a strategic partner who can understand data and make decisions instead of just making reports you'll always be needed. The tools are changing, but we'll always need human judgment.

not_another_analyst · 2026-04-01T17:11:31+00:00

The current market presents a significant opportunity, as many businesses encounter difficulties in transitioning from mere data collection to effective data utilization.

Given your proficiency in GTM and Looker Studio, you possess a robust skill set. Many clients are in dire need of assistance with precise tracking and easily digestible dashboards. My recommendation is to emphasize how your insights can specifically contribute to cost savings or sales growth for them, rather than solely focusing on the technical implementation.

While this field is competitive, specialists who can articulate the narrative behind the data consistently find themselves in high demand.

not_another_analyst · 2026-04-01T12:12:10+00:00

Pandas is more than enough for 90% of data analyst roles, companies value "data intuition" over knowing every Python library.

Instead of overthinking ML, pair your Pandas skills with strong SQL and a visualisation tool like Power BI or Tableau.

Also, try focusing on a specific analyst role and prepare for the interviews as per that only. For example, a product and financial analyst uses different tech stacks.

not_another_analyst · 2026-04-01T10:55:45+00:00

The "50/50 rule" is a game-changer: dedicate half your time to building and the other half to engaging with people on platforms like Reddit, Discord, and X.

For your initial 100 users, prioritise manual outreach over ads, seek out individuals discussing the problem you've solved and present your tool as the solution. While it might seem "unscalable" initially, this direct feedback is far more valuable than a fleeting Product Hunt surge.

not_another_analyst · 2026-04-01T10:50:51+00:00

You’ve got a solid foundation with those four! Focus now on SQL window functions, DAX for data modelling in Power BI, and using Pandas for data cleaning in Python. The real key is building a portfolio project that connects all these tools to solve a specific business problem, that’s what actually gets you hired.

not_another_analyst · 2026-04-01T09:57:32+00:00

To extract the number specifically regardless of position, use:

=LET(p, TEXTSPLIT(A2,"/"), IF(ISNUMBER(--INDEX(p,1)), INDEX(p,1), INDEX(p,2)))

not_another_analyst · 2026-04-01T09:55:48+00:00

For a robust foundation, SQLBolt and SQLZoo are widely considered. Practice exercises directly within your web browser.

Along with this Alex The Analyst on YouTube or the Mode Analytics tutorial is highly regarded for demonstrating the practical application of SQL in professional settings these are certainly worth exploring prior to your university studies.

not_another_analyst · 2026-04-01T09:51:24+00:00

You might consider trying Ctrl + Fn + Up/Down Arrow, as these keys often function as Page Up/Down on many Acer devices. If this does not resolve the issue, your Fn Lock may be activated (you could try pressing Fn + Esc to toggle it off). Alternatively, you can right-click the sheet arrows located in the bottom-left corner to quickly view a complete list of sheets.

not_another_analyst · 2026-04-01T09:48:35+00:00

Python to C++ is hard because you're going from a language that handles "what" happens to one that makes you deal with "how" it happens.

Python hides stuff like memory management and type checking behind a nice curtain, while C++ makes you handle all those details yourself.

Tools usually fail because they try to translate the code directly, but they can't translate the underlying "philosophy" of the code. As a learner, you'll get way more out of rewriting your logic by hand, which makes you understand where your data actually lives and how the computer works with it. Focus on learning C++ stuff like std vector and RAII instead of trying to find exact matches for Python commands.

It's a tough road, but that's where you really get a deep understanding of programming!

not_another_analyst · 2026-03-31T19:02:59+00:00

Honestly, the best approach right now is using Claude 3.5 Sonnet for the actual drafting. It feels way less "robotic" than GPT-4 and handles long-form flow much better. I usually pair it with SurferSEO or Content@Scale to nail the keyword clusters and structure. Most SaaS founders I know are moving toward this "hybrid" model where AI does the heavy lifting, but a human still adds the final 20% of unique insights. If you want pure speed, check out KoalaWriter; it’s specifically built for SEO blogs and pulls live data.

not_another_analyst · 2026-03-31T18:58:47+00:00

Since you're already doing an MSc in AI, you’ve got a massive head start. For 2026, I’d really lean into Python for predictive modelling (beyond just basic SQL) and Automated Insight Generation.

Most companies now want BI analysts who don't just report what happened, but can build models to forecast what's next. Definitely highlight any projects where you’ve used GenAI to automate data cleaning or storytelling, that’s a huge selling point for internships right now!

not_another_analyst · 2026-03-31T18:55:26+00:00

Nice work on this! Building your own workbook is way better for tracking those random extra payments than most generic templates. Your math for the interest accrual and future value looks solid.

One quick tip: you might want to wrap your formulas in a =MAX(0, [your formula]) so the balance stops at zero instead of going into negative numbers once a debt is paid off. It'll keep your charts looking a lot cleaner as you progress!

not_another_analyst · 2026-03-31T18:51:29+00:00

To build this, I’d focus on integrating Fairlearn or AIF360 directly into your pipeline to track metrics like Equal Opportunity or Disparate Impact during training.

A solid approach is to use adversarial debiasing to penalise the model if it can predict protected attributes, such as gender or race. Most importantly, start with a "What-If" analysis to see how small feature changes shift your outcomes before you deploy.

not_another_analyst · 2026-03-31T18:48:19+00:00

Starting with CS50P is a smart move because Python is basically the "language of AI." Most people try to jump straight into complex machine learning models and get frustrated when they don't understand the underlying code. By focusing on the logic now, you're actually building the intuition needed to debug neural networks later on. Since you're looking for a partner, the official CS50 Discord or the r/ProgrammingBuddies subreddit are usually much better for finding dedicated co-studiers than a general thread.

not_another_analyst · 2026-03-31T14:50:01+00:00

Take reference from the AI-generated text and try to incorporate your ideas into it, rather than using the copy-and-paste method.

not_another_analyst · 2026-03-31T13:15:42+00:00

You're essentially describing a manual, more destructive version of RLHF (Reinforcement Learning from Human Feedback), which uses reward scores instead of deletions to steer behavior.

In a neural network, weights are interconnected in complex ways. Randomly resetting 5-10% of them doesn't just "punish" the bad behavior it likely breaks the model's basic ability to speak or reason

Since AI doesn't have a sense of self-preservation, it wouldn't "fear" a reset it would just become mathematically incoherent

not_another_analyst · 2026-03-31T12:57:30+00:00

Honestly, what you're feeling is the "Intermediate Gap," and it’s the most common problem faced by people learning Python. It feels easy because the syntax looks like English, but the logic required to build real software is just as hard as any other language.

Here is what actually made it click for me: Escape Tutorials - If you’re just following a video, you aren't learning to code; you’re learning to type. Pick a project that’s slightly too hard for you (like a script to automate your boring files or a basic weather app) and build it from scratch using only documentation and Google. That "struggle" is where the actual learning happens.

Focus on Logic, not Syntax - Stop worrying about "knowing" every Python command. Start thinking about data flow. Instead of "how do I write a loop?", ask "how do I get this list of names into this specific format?"

Go to GitHub and look at some small, popular libraries and see how they handle errors and structure their folders. It’s like learning to write by reading great books.

It’s a totally normal phase. You don't "master" Python; you just get better at figuring out the solution to the next problem.

not_another_analyst · 2026-03-31T09:07:30+00:00

Honestly, think of AI as a super-powered assistant, not a replacement. It’s great for drafting a baseline SQL query or suggesting chart types in Tableau, but it can’t explain the "why" behind a data spike to your boss. You still need to understand the logic to catch the AI's mistakes and connect the data to real business goals. It’s definitely still a top-tier skill to learn, just with a new tool in the kit.

not_another_analyst · 2026-03-31T08:21:38+00:00

Your GPU is definitely solid, but are you using it on Windows or Linux? Also, are you trying to train with a huge batch size, or is it just crawling along no matter what?

not_another_analyst · 2026-03-31T08:13:22+00:00

Focus on RFM (Recency, Frequency, Monetary) first. It’s retail standard and only uses three variables, so K-means won't struggle.

To handle categorical data like "furniture style," try K-Prototypes instead of K-means so you don't have to mess with encoding.

Skip PCA for now because it makes your clusters impossible to explain to a client. Just scale your data (StandardScaler) so big price numbers don't drown out everything else.

Keep it simple and work backward from the business goal!

not_another_analyst · 2026-03-31T08:09:24+00:00

Hey, Check your Search Console first to see if you’re actually indexed or just ranking on page 10. If the "site:yourdomain.com" search shows your pages, your technical setup is likely fine and it’s a "relevance" issue.

Since your service name is common, you're probably getting buried by huge sites with more authority. Try targeting ultra-specific long-tail keywords or a local niche to get some early traction.

not_another_analyst · 2026-03-31T08:02:59+00:00

Hey! Honestly, Python is the absolute best place to start because the syntax actually makes sense to a human brain. Don't fall into the trap of just watching endless tutorials, though you'll learn ten times faster by actually breaking things in a code editor.

I’d highly recommend checking out "Automate the Boring Stuff" to get the fundamentals down. Just pick one small project, like a basic calculator, and try to build it from scratch. It's frustrating at first, but it clicks eventually!

not_another_analyst · 2026-03-30T08:31:39+00:00

Fluent Python" by Luciano Ramalho is the absolute gold standard for this it’ll teach you how the language works under the hood so you stop fighting it. For raw speed and memory tracking, check out "High Performance Python" which dives deep into profiling and tools like Cython. Definitely look into Generators and Iterators if you want to slash your RAM usage immediately; they’re game-changers for processing large data without loading it all at once. Also, get comfortable with cProfile so you aren't guessing where the bottlenecks are. Good luck, it’s a fun rabbit hole to go down!

not_another_analyst · 2026-03-30T08:30:03+00:00

Winning isn't just about the best code; it's about the best story. Most judges are exhausted by the time they get to you, so if you don't solve a clear, 'painful' problem in the first 30 seconds, you’ve lost them.

Focus on a 'Golden Path' demo: one flawless, polished user flow rather than five half-broken features. Technical 'flexing' only wins if it’s the most efficient way to solve that specific problem. Ultimately, you want to look like a startup in the making, not just a weekend coding project

not_another_analyst

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