Hello there! 👋 Welcome to your ultimate resource for acing that Data Science interview! This repo has got you covered from business case scenarios to hands-on coding tasks. Whether you're a newbie or a seasoned data scientist, you'll find something useful here.
This is your step-by-step guide to solving business case scenarios as you'd encounter in a case interview. It's structured just like a typical case interview but with additional details on ML models. Handy for consulting interviews or any situation where understanding the business context is key. Parts of this sheet were inspired by Aaron Wang. Thanks for your helpful sheet!
Future Sections 🚀
- RNN
- CNN
Ever get stuck on syntax during a coding interview? This cheat sheet lays out the most important commands you'll need to analyze data with Python.
This sheet is perfect for brushing up on sorting, searching, and data structures like linked lists and trees. Whether you're facing a tough coding challenge or tackling graph-related problems, this sheet has got you covered.
- Sorting: Merge Sort, Quick Sort, Heap Sort, Tim Sort, Bucket Sort
- Searching: Linear Search, Binary Search
- Linked Lists: Singly and Doubly
- Trees: Binary Trees, Binary Search Trees
- Graphs: Graph as Adjacency List, Matrix BFS, Matrix DFS
- Common Problems: Detect cycles in a list, Knapsack Problem, and more.
I've crafted these cheat sheets using the LaTeX template from latex4ei-packages. Big shoutout to them for providing an awesome template!
Viewing: Click on title Editing: Fork to your account and connect with overleaf or download and upload ZIP to overleaf.
Feel free to fork this repo and submit PRs. Your contributions are more than welcome!
Best of luck with your interviews! 🍀
Happy coding! 💻🎉