I am a fresh graduate and actively applying for data science positions. Since I have no professional experience yet, I would like to know if there is any public repo, blog or book where I can find guidance on how to prepare for take home data challenges. Apart from building predictive models (common challenges), I am interested in knowing:
- What are other types of common challenges?
- What are the main key points to include in a report. For instance, should I include all steps followed in data cleaning, all tried and failed prediction methods, …?
- What are the hints to prepare for these kind of challenges
I will also love to hear from anybody who has gone through this