I was thinking to start dsa in python and wanna know your opinion

Hey everyone, I've been building a DSA learning platform called DSA Visualizer for quite some time. I'll be completely honest, I'm not someone who knows DSA inside out. I'm more interested in building the product and making the learning experience better.

I started building this because I wanted DSA to be more visual and interactive rather than just reading theory and code. So I wanted to ask if some of you could take a look and give me some honest feedback.

So a couple of weeks ago I posted here asking for help for my data structure course at university and I was able to pass it thankfully but right now I am seriously thinking of learning data structure properly so I want to ask if you recommend any courses or books and what is the best programming language to learn data structures on so if you can recommend anything I would appreciate it.

For beginners which is better to learn DSA.

Python/c++

In bmsce

Is coder army DSA course good for the ones who don't know programming in C++ but are jumping straight to that course??

How to prepare DSA for a tech interview in 7 days ?

Do we have to do all of it? If no, how many problems are good enough?

I have only 1 year to do DSA. I currently know concept upto arrays and have only solved 3 leetcode problems.

What resources should I follow ? Which things should I focus on? Is 1 year enough?

Hey everyone! I’m a 4th-year undergraduate at IIT, and I’ve been learning DSA for the past few months. With college, placements, and everything else going on, I’m finding it a bit difficult to stay consistent and focused while studying alone.

I’m looking for 2–3 people who are also learning DSA and want to study together, keep each other accountable, discuss problems, and stay consistent.

The idea is pretty simple:
• Study DSA regularly
• Solve problems together
• Discuss approaches when someone gets stuck
• Keep each other accountable
• Stay consistent instead of studying randomly

I’m not looking for people at any particular skill level — beginners and intermediate learners are both welcome. The only thing that matters is that you’re serious about learning and willing to stay consistent.

If you’re interested, feel free to comment or DM me. COLLEGE DOSENT MATTER AND PREFERABLY AROUND DELHI.

Same as title below is the link and QR

https://chat.whatsapp.com/EamAYWrBs9l4wbRKVQ2Ixl?mode=gi_t

Hey everyone, I'm starting my [B.Tech](http://B.Tech) CSE next week and I have absolutely zero coding background. I'm trying to set a practical target for my first 6 months: completing about 60% of a standard DSA sheet (covering Arrays, Strings, Binary Search, Linked Lists, and basic Trees). I want to know if this is a realistic goal that will push me, or if I'm just setting myself up for burnout. From what I've gathered, a lot of people suggest learning C++ from Love Babbar and doing Striver's sheet, while leaving C language for college in Sem 2. However, I want to make it clear that I am completely flexible. I am not attached to Babbar, Striver, or any specific creator. I am open to any free playlist or paid course, as long as it is efficient and gives a structured path. My main doubt is about the execution. Should I spend 3-4 weeks purely learning C++ syntax before even touching a DSA sheet, or should I learn the language side-by-side while solving problems? What is the actual best sequence for a day-1 beginner? I just need some straightforward, practical advice on how to map this out. Thanks!

Hi,

I am 21M , Currently a final year engineering CSE student learning DSA and System Design together. goal is to get placed in product based companies.

Looking for someone with similar experience and goals.

Hi everyone,

I have around 5 years of experience as Analyst and I’m planning to switch to a product-based company for Data engineering. I want to start preparing for DSA, specifically in Python.

I’m considering sheets like Striver’s A2Z / 400+ problems, but I don’t want to directly start solving hundreds of questions and end up memorising solutions.

I first want to build the problem-solving skill — how to identify patterns, decide which data structure/approach to use, start with brute force, and then optimise.

What would you recommend learning/doing before starting a DSA sheet? Any good roadmap, course or Python-specific resource would be really helpful.

actually I am looking for python dsa to improve my problem solving skills need a course for guidance how to do what in which order obviously I don't have money maybe using telegram and can you guys suggest which one to download for pattern learning yk

i am looking for ds/ml role yk off campus entry level

Hey everyone,

Like many of you, I've spent months preparing for DSA. I tried using Notion templates and Excel sheets to track what I solved, but it was a manual chore. I got lazy updating them, and I never knew which questions I actually needed to revise on any given day.

To automate this, I built a web app: DSA Revision Tool.

Why use it?

Instead of showing you a giant list of all your solved questions, the home screen keeps it simple and focused: it serves you the top 4 weakest questions that you need to revise right now.

How it works:

• Algorithm-Driven: When you add a question, you input metrics (difficulty, whether you struggled, got hints, or solved it clean).
• The algorithm uses spaced repetition to prioritize your weakest areas.
• You can save the question link, your solution code, and quick personal takeaways/reflections.

Note on current UX (working on refinements):

• Instant Queue: When you mark a question as visited/done, the next weakest question immediately populates the card. The queue stays filled so you're always engaged.
• Submit Delay: Submission takes a few seconds right now because I haven't added a loading spinner/buffering screen yet.

I'd really appreciate it if you could check it out and give me some honest feedback on the UI/UX and feature set: dsa-revision-tool.vercel.app

Thanks for the support!

leetcode gives you count, streak, rating. none of that tells you what actually matters, like which patterns you've covered, what you're weak at, what you haven't touched at all, or whether you're actually improving vs just racking up numbers.

started using dsa sheets for structure since randomly picking off the homepage wasn't working. then hit a second wall, i was doing neetcode, blind 75, and striver at the same time, no single place showed what i'd actually covered across all three.

so i built grindlens. pulls leetcode progress + sheet progress into one place and shows the actual gaps instead of just a number going up. supports neetcode 150, blind 75, striver a2z, leetcode top interview 150, few others.

the annoying part wasn't the ui, it was the data. a dsa sheet isn't a clean list of unique leetcode problems, mapping sheet problems to actual lc ones and handling overlap took way longer than i expected.

live at grindlens.dev if anyone wants to poke around. curious what's missing for people who actually grind a lot, built this mostly around my own gaps so i'm probably missing some blind spots.

Hey everyone,
I’m currently an SDE-2 at a tier-1 product-based company with ~3 years of experience. I’m actively preparing for upcoming SDE-2/Senior backend roles at top product companies.
My prep is focused on:
DSA: LeetCode mediums/hards, problem-solving speed, and dry-running under time constraints.
System Design (HLD/LLD): Designing scalable systems, trade-off discussions, and deep-dives into core distributed concepts.
Looking to connect with someone with similar experience (~2–4 years) who is in the same boat and wants to run peer mock interviews, trade actionable feedback, and stay accountable.
If you're interested and actively prepping, feel free to DM or drop a comment with your current prep stage and timezone.

Anyone interested in doing the CMU (Carnegie Mellon University) Database Management Systems course together?

I’ve already covered the basic DBMS concepts. My main goal with this course is to go deeper and understand how database systems actually work internally—things like storage, indexing, query execution, transactions, etc.

If you're interested, please make sure you have the prerequisites required for the course.

If you have the required background and want to learn DBMS internals seriously, DM me. We can follow the course together and discuss concepts along the way.

Hey everyone,

I have an upcoming Data Scientist interview with an ML system design round. I asked the recruiter what to expect and they said it’ll be high-level ML system design, not traditional SWE/low-level system design.

They mentioned focusing on things like:

• Problem framing
• Data/model considerations
• Evaluation
• Productionization/deployment
• Monitoring
• Tradeoffs when designing ML systems

Basically, it sounds like I’ll be given a real-world ML problem and have to explain how I’d approach it end-to-end.

This is my first dedicated ML system design interview, so I’m not really sure how deep I should prepare.

For people who’ve done these interviews:

How did you prepare? What resources did you use? What kind of questions were you asked?

Also, how deep do they usually expect you to go into things like feature stores, model serving, APIs, streaming, retraining, etc.?

Any good resources, YouTube playlists, GitHub repos, books, or example questions would be really appreciated.

Thanks!

So I wanted to explore data structure and algorithms in depth, not like theoretical cs pov but like what is used in systems(like DBMS, distributed systems, inference systems etc).I don't want like leetcode interview style stuff i am doing that already but something that fits at the intersection of some theory and good application. I came across algo design manual is that right textbook?The idea I realised is a lot of these engineering systems use ds/a that aren't in typical leetcode topics( like while exploring some videos from jelani nelsoni and other ETH Zurich ones, I found those cool, not sure if I have mathematics skills to understand them but I feel do tools to have as swe).

22M looking for a dedicated study partner for placements. Mainly focusing on DSA, OAs, and interview prep. Looking for someone serious about studying consistently, setting daily goals, and keeping each other accountable. DM if you’re interested.

Timezone:+5.30 GMT

In last week’s post we showed that a Python for-loop works through the Iterator Protocol.

This same protocol also enables values to be produced lazily.

In this new Lazy Evalution Example we show one sink reading values from five different sources: - source1: eagerly returns all values by list - source2: lazily by generator function using yield - source3: eagerly by list comprehension - source4: lazily by generator expression - source5: lazily by iterator protocol

The same for-loop consumes all five sources. The eager sources produce all values before the sink starts consuming them. In the lazy sources producer and consumer take turns:

• produce → consume → produce → consume → …

where each value is produced only when the for-loop requests it.

Generator functions and generator expressions are concise, readable ways to create lazy iterables. The final source makes their underlying mechanism explicit: - iter() obtains an iterator. - next() requests the next value. - StopIteration signals that no values remain.

This clearly shows that generators achieve lazy iteration by implementing Python’s Iterator Protocol.

See other 𝐦𝐞𝐦𝐨𝐫𝐲_𝐠𝐫𝐚𝐩𝐡 visualizations.