## Metadata - Author: [[tessa-xie|Tessa Xie]] - Full Title:: Analytics Frameworks Every Data Scientist Should Know - Category:: #🗞️Articles - Document Tags:: [[important|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](https://read.readwise.io/read/01jh96nrhvemat4hy6kgqq8ka3)) > 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](https://read.readwise.io/read/01jh96stqz3jj8v4pm65zhg5yg)) > 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](https://read.readwise.io/read/01jh976a832vwjqpj646sbvz7b))