Notes on Data Science Resources

Good websites/blogs/resources

ClaoudML

SuperDataScience

A Cloud Guru

Interview Qns

interviewing.io

Pramp

Bogs on Machine Learning System Design

CHEATSHEET: LEETCODE COMMON TEMPLATES & COMMON CODE PROBLEMS

Towards DS: Developing Machine Learning Pipelines

Job boards

Dice (tech roles)

Communities

Quora: What are some active Data Science Slack channels?

15 Data Science Slack Communities to Join

Soft Skills

WSJ: How to Change Anyone’s Mind

Standing Out in a Sea of Data Scientists

5 cognitive biases in data science — and how to avoid them

Datasets

Kaggle datasets

UCI dataset repo

Google dataset search

NLP dataset@Github

NLP dataset@ML Mastery

Career Advices

Book: Build a Career in Data Science (Emily Robinson and Jacqueline Nolis)

CMU Career Tips and Advice for Data Science/Data Analytics

Medium: 12 things I wish I’d known before starting as a Data Scientist

Medium: The academic trap and data science

Medium: Hiring data scientists

Forbes: Radical Change Is Coming To Data Science Jobs

Main point: Many of the data science tasks today will become automated and part of the anaytics platform. The five typical career path for future data scientists are: 1. Generalist; 2. Industry specialists; 3. Deep specialists; 4. Analytics developers; 5. Data engineers

CrowdFLower: Data Science Report 2017

Main point: most of the time is spent with cleaning and organizing the data

Towards DS: Voicing for Data Engineering, the unsung hero

Behavioral Interview Questions

Q: What Are Your Salary Expectations?

Link 1 (examples)