alexeygrigorev/data-science-interviews

Features

• Technical Interview Questions - Serves as a comprehensive curated collection of technical interview questions and verified answers.
• Technical Interview Preparation - Provides structured study materials, practice challenges, and technical questions for preparing for data science interviews.
• Machine Learning Concepts - Covers fundamental mathematical and structural principles of supervised and unsupervised learning and neural networks.
• Data Science and Analytics - Acts as a comprehensive interview preparation resource for data science and machine learning roles.
• Machine Learning Resources - Provides a reference for learning algorithms, neural network architectures, and model evaluation methods.
• Machine Learning Foundations - Contains curated questions and answers on supervised and unsupervised learning theory.
• Mathematics and Statistics - Provides mathematical and statistical foundations, including probability problems and time series forecasting.
• Query Exercises - Offers a collection of SQL query problems designed to simulate recruitment take-home assignments.
• Coding Challenges - Provides structured take-home assignments and recruitment challenges to vet technical candidate skills.
• Recruitment Simulation Challenges - Ships a bank of practical programming and SQL exercises designed to simulate recruitment take-home assignments.
• Probability and Statistics - Offers a curated set of foundational probability and statistics problems for technical interview preparation.
• Python Exercises - Includes a dedicated bank of Python programming exercises covering data cleaning and mathematical computations.
• Question and Answer Sets - Ships structured educational materials organized as discrete question-and-answer pairs for targeted technical interview study.
• Algorithmic Problem Solving - Implements classic computer science challenges focusing on algorithmic efficiency and time-space complexity.
• Technical Skill Exercises - Offers practical coding challenges in Python and SQL to improve technical proficiency for data-driven roles.
• Feature Engineering - Provides theoretical and practical guidance on feature engineering techniques like PCA and TF-IDF.
• Algorithm Comparison Guides - Offers frameworks for comparing algorithms to determine the optimal model for specific problem types.
• Neural Network Architectures - Analyzes neural network architectures including feed-forward and convolutional networks and their optimizers.
• Recommendation Systems - Covers the theoretical logic of collaborative and content-based filtering for recommender systems.
• Time Series Forecasting - Provides analysis and guides for time series forecasting using autoregressive and Holt-Winter's models.
• Compensation Negotiation - Includes specific frameworks and strategies for negotiating employment salary and benefits.
• Career Development Guides - Provides professional advice on navigating the job market, networking, and interview strategies.
• Model Evaluation Techniques - Explains model validation techniques including K-fold cross-validation and data splitting strategies.
• Career Development - Offers guidance on professional growth and navigating the hiring process for data science professionals.
• Career Guidance - Provides guides on technical screening, job search strategies, and professional networking for technical roles.
• Curated Knowledge Bases - Preparation materials for data science job interviews.
• Curated Resource Lists - Preparation materials for data science job interviews.
• Interview Preparation - Collection of data science interview questions and resources.

Star history