This course provides a full-length data science mock interview focusing on developing a model to identify bots on a social media platform. The learning outcomes include understanding feature vectorization, one-hot encodings, dataset building, and more. The course teaches skills such as investigating features related to bot detection, implementing classification models using feature vectors, and collecting data for training models. The teaching method involves a walkthrough of the interview process, including introductory behavioral questions, technical implementation details, and post-interview analysis. This course is intended for individuals interested in practicing data science interview scenarios and enhancing their data science skills.
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
