Study Guide for Data Scientist (NLP)

Date: Feb. 2025 - Current

Description

A complete study guide for data scientist/AI research engieer positions (with NLP focus). In this repository, I organize all the notes of what I studied and learned during interview preparation. Still in progress.

---

Structure (In-progress)

1. Leetcode (Easy & Medium) - Python
   • Grind 75 & Neetcode
2. ML Coding - https://www.deep-ml.com/problems
3. System Design
4. Code for Learning
   • (Advanced) Python
   • PyTorch
   • Scikit-learn
   • Numpy
   • Pandas
   • statsmodels
   • SQL
   • Spark (pyspark)
   • Transformers (Huggingface) -> finetuning, evaluation
   • Docker
   • AWS,
   • NLP libraries (Langchain, Elasticsearch, VectorDB)
   • and more
5. DS/AI Concepts (study material)
   • Prob & Stats
   • Supervised
   • Unsupervised
   • Time-series
   • Recommendation
   • Deep Learning
   • RL
   • NLP
   • Bayesian Optimization
6. Interview
   • Resume questions
   • Behavioral
   • Interview Log