Experience a comprehensive mock data science interview in this 1 hour 28 minute video featuring Kylie Ying. Dive into a real-world task of developing a model to identify bots on a social media platform. Learn about feature vectorization, one-hot encodings, and dataset building. Follow along as the interview covers behavioral questions, task overview, feature investigation, classification model implementation, dataset creation, technical details, and a post-interview analysis. Gain valuable insights into the data science interview process and enhance your skills in tackling complex machine learning problems.
Overview
Syllabus
- Video overview & format
- Introductory Behavioral questions | Data science interview
- Social media platform bot issue task overview | Data science interview
- What are some features we should investigate regarding the bot issue? | Data science interview
- Classification model implementation details using feature vectors | Data science interview
- What would a dataset to train models to detect bots look like? How would you approach collecting this data? | Data science interview
- Technical implementation details python libraries, cloud services, etc | Data science interview
- Any questions for me? | Data science interview
- Post-interview breakdown & analysis
Taught by
Keith Galli
