Metadata
- Author: Tessa Xie
- Full Title:: Analytics Frameworks Every Data Scientist Should Know
- Category:: 🗞️Articles
- Document Tags:: important,
- URL:: https://www.divingintodata.com/p/analytics-frameworks-every-data-scientist?utm_source=multiple-personal-recommendations-email&utm_medium=email&token=eyJ1c2VyX2lkIjoxMTk1OTgwOSwicG9zdF9pZCI6MTQ4MjIxMTExLCJpYXQiOjE3MzYyNjQ3MjQsImV4cCI6MTczODg1NjcyNCwiaXNzIjoicHViLTI2NzQzMjEiLCJzdWIiOiJwb3N0LXJlYWN0aW9uIn0.Kd2jvttsrLhY5mfzj3DoXx_6zGBhd54Bnr4O56fczEk&triedRedirect=true
- Read date:: 2025-01-11
Highlights
I realized what junior data scientists struggle with the most is usually not the technical/execution part of the job — that’s the easy to teach/easy to learn part. It’s usually the more abstract/soft-skill-related part of the job that most people don’t know how to navigate — things like how to break down an abstract business problem into smaller, clearly defined analyses that can eventually lead to concrete business impact. (View Highlight)
analytics work itself requires you to be deep in the weeds and it’s hard to then to zoom out when communicating your findings. (View Highlight)
A typical DS interview question is “XX metric is down, how would you go about investigating what’s causing it?” A lot of candidates again start grabbing hypothesis out of thin air. What interviewers (or your stakeholders and managers when it comes to your day-to-day work) want to see is that you can generate and test hypotheses in a structured way. (View Highlight)