If you've ever tried to prepare for a tech role, you know the actual problem isn't a lack of resources. It's the opposite. There are thousands of courses, YouTube channels, GitHub repos, and roadmaps floating around, and figuring out which ones are actually worth your time eats up more energy than the preparation itself.
I run Let's Code, a free placement prep platform for engineering students, mostly from tier-2 and tier-3 colleges in India who don't always have access to paid bootcamps, mentors, or curated guidance. Over the last few months, I sat down and built out a set of Excel based resource kits, one for each major tech career path, so people stop wasting time hunting for links and start actually studying.
Each kit is a Google Sheet. No signup, no paywall, no "drop your email to unlock." Just open it and use it.
Below is the full list, what's inside each one, and who it's actually for.
DSA Complete Resource Kit
This is the one most people ask me about first, so it's also the most complete. It's not just a link dump, it's organized so you can actually build a study plan out of it: topic-wise problem sets, pattern-based practice, company-wise previously asked questions, and curated YouTube playlists for the topics people usually get stuck on (DP, graphs, trees).
https://docs.google.com/spreadsheets/d/1DiJ8PL-FoGD9JXs8CpJ2z4l8GcEfflVogjAqfJNH00Y/edit
If you already have a preferred creator, the kit also links out to individual sheets for Striver, Love Babbar, and Apna College, plus a separate DSA Pattern Sheet for anyone who prefers to learn pattern by pattern (sliding window, two pointers, backtracking, etc.) instead of topic by topic. This pattern-first approach tends to work better for people who are short on time before interviews, since it maps directly to how interview questions actually get asked.
Who it's for: anyone prepping for SDE interviews, from first-year students starting DSA to final years cramming before placement season.
System Design Resource Kit
System design prep usually falls apart because people jump straight into "design Twitter" style mock interviews without understanding the underlying building blocks first. This kit fixes the ordering problem: fundamentals first (load balancing, caching, database sharding, CAP theorem), then HLD and LLD case studies, then the actual mock question banks.
https://docs.google.com/spreadsheets/d/1NZwVMIcHsPWlDIgpoXDC3Nzj7cUC5WjZ2fRnJD1PGaY/edit
Who it's for: anyone with 1+ years of experience prepping for SDE-2 or above roles, or final year students targeting product-based companies where SD rounds are increasingly common even at entry level.
Java Full Stack Complete Resource Kit
Covers core Java, Spring Boot, and the frontend layer you need to actually call yourself full stack. Includes REST API design resources, database integration, and deployment basics so you're not stuck only knowing the backend.
https://docs.google.com/spreadsheets/d/1cjqosy6HXWWgPpYbpKVW6VRMGr17039kBzpOkufiQCA/edit
Who it's for: students and early career devs targeting service-based companies that heavily hire Java Spring Boot developers (which is most of them in India).
Python Full Stack Complete Resource Kit
Same structure as the Java kit, but for the Python ecosystem: Django, Flask, FastAPI, plus the frontend and deployment resources to round it out.
https://docs.google.com/spreadsheets/d/1im7voLl4DpEUxepSYswMQItNJQz2KhsCstVfE6szVV4/edit
Who it's for: anyone who wants to build and ship projects fast, since Python's ecosystem tends to get people to a working product quicker than most other stacks.
Complete Full Stack Resource Kit
For people who don't want to commit to a specific backend language yet, or just want the general MERN/MEAN style path. Covers HTML/CSS/JS fundamentals, React, Node, databases, and deployment, without tying you to Java or Python specifically.
https://docs.google.com/spreadsheets/d/1WPgTZqIsG6kysQElB1tkOxu2rWw8C5CmDcEno7_nRBQ/edit
Who it's for: beginners who are still figuring out which stack to specialize in.
Data Engineer Complete Resource Kit 2026
Data engineering is one of those fields where the college curriculum teaches you almost nothing relevant. This kit covers SQL at depth, ETL pipeline design, Spark, Airflow, and data warehousing concepts, the actual day to day tooling used in the field.
https://docs.google.com/spreadsheets/d/1LIdZHoUPediHJepSAm-nEprFGocn3fQKG2_jeTo98Hg/edit
Who it's for: anyone pivoting from a general SDE background into data engineering, or students who want to specialize early instead of doing generic full stack like everyone else.
Data Analyst Resource Kit
Excel, SQL, Power BI, and Tableau, plus analyst-specific interview prep (case studies, business problem framing, not just tool tutorials).
https://docs.google.com/spreadsheets/d/1XUUDIUH-QnxXHWXsjb5yBcxb2X3rPXG2P2dgxjbhWQw/edit
Who it's for: non-CS background students and career switchers, since data analyst roles are one of the more accessible entry points into tech from a non-traditional background.
AI Engineer Complete Resource Kit 2026
This is the biggest kit in the whole series, 400+ resources. It covers ML fundamentals, LLM architecture, RAG pipelines, fine-tuning, prompt engineering, and agent frameworks, basically the entire stack of what "AI engineer" means as a job title right now, since the role itself is still being defined across the industry.
https://docs.google.com/spreadsheets/d/193ziCsPxUO8QLTxJVC2blB_t3-YiWWTYX8xxTSIcf1g/edit
Who it's for: developers coming from a regular SDE background who want to move into AI/ML without starting from a pure research or academic path.
Cloud & DevOps Resource Kit 2026
AWS fundamentals, Docker, Kubernetes, CI/CD pipelines, and Infrastructure as Code with Terraform. Organized so you can go from "I've never touched a cloud console" to "I can deploy and manage a production pipeline" without jumping around ten different sources.
https://docs.google.com/spreadsheets/d/1cPqI9vKo47EUxoZXLP0411IzvRWRLbu5HQnzyn8HpxI/edit
Who it's for: SDEs who want to add cloud/DevOps skills on top of their existing stack, since almost every full stack role now expects at least basic deployment and CI/CD knowledge.
Android Developer Complete Resource Kit
Kotlin fundamentals, Jetpack Compose for modern UI, and the full path from your first app to something you'd actually put on the Play Store.
https://docs.google.com/spreadsheets/d/1cbsyL6F8bkOFmskz3DVm46JYleMlhokyDWiGOQT3Yio/edit
Who it's for: anyone who wants to specialize in mobile instead of going the generic web dev route.
C# Complete Resource Kit 2026
C# fundamentals and the .NET ecosystem, for anyone targeting companies on the Microsoft stack, which is a bigger chunk of the enterprise hiring market than most students realize.
https://docs.google.com/spreadsheets/d/1KLYjjLtS5zUSd35Ru_SQo3mudUAl6cwDvyJz_iROj98/edit
Who it's for: students targeting enterprise and product companies that specifically hire for .NET roles.
Cybersecurity Complete Resource 2026
Network security fundamentals, ethical hacking, the major certifications worth pursuing (and which ones are actually worth the money), and career roadmaps into security-specific roles.
https://docs.google.com/spreadsheets/d/1ZwDT1uUOgKjNwQMH4jVHMrXqOGYMT0oQ/edit
Who it's for: students who want a security-focused career path from the start, rather than pivoting in later from general SDE work.
Open Source Toolkit for Building AI Agents 2026
Different from the AI Engineer kit above, this one is purely for people who want to build. It's frameworks, open source repos, and tools for actually shipping agents, not tutorials about the theory behind them.
https://docs.google.com/spreadsheets/d/1cGylccKIuHDxnPpg68tPZxp8_XZP8VoPqkM7HgofPdk/edit
Who it's for: developers who already understand the basics and want to start building agent projects for their portfolio right now.
How to actually use these
A few things I'd suggest based on watching people go through these kits in our community:
- Don't try to go through an entire kit top to bottom in one sitting. Pick the section relevant to where you are right now (fundamentals vs interview prep vs project building) and start there.
- Use the company-wise and pattern-wise sections in the DSA kit if your interview timeline is tight. Topic-wise is better if you have 2+ months to prepare.
- If you're switching career tracks (say, SDE to Data Engineering, or SDE to AI Engineering), start with the fundamentals section of that kit before jumping to the advanced resources. Skipping fundamentals is the most common mistake I see.
- Bookmark the sheet, don't just star this post. Google Sheets tabs are easy to lose in browser history.
Why this exists
Let's Code started as a free platform because a lot of good preparation material is either paywalled or scattered across places that are hard to discover if you're not already plugged into the right Twitter/X circles or Discord communities. These kits are an extension of that same goal: put the good stuff in one place, make it free, and make it easy to actually use.
If these are useful to you, share this post with a junior, a classmate, or anyone else prepping right now. That's genuinely the most useful thing you can do with it.
If you want more resources like this, or want to be part of a community actively prepping for placements together, check out Let's Code:
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