Omri Allouche "The top mistakes you're making in your Data Science interview"
Mr Allouche also did had an unorthodox track to DS. He used many metaphors from DS which was refreshing in this talk sequence as was his use of compelling visuals.
Ask what will you be doing:
- Writing code that goes to production
- Develop new algorithms
- Be in charge of collect data
- Work alone / lead others
Don't run away from your super powers.
Don't be the only/first data scientist
Running away from data in data science
- Don't skip - Exploratory analysis
- Unsupervised is cool... don't rush to do supervised models
- Learning to do proper error analysis - when is the model wrong...
Running away from science in data science
Use your intuition but learn to say - "I don't know but I would try ... " (2 different solutions)
Mr Allouche - talked about the community and that we could have a conversation about brain storming strange new ideas.
define
- Data set
- Input
- Output
- Your own the loss function yourself
Overconfidence is problem - it says that this person is not going to learn too much.
But my five cents on this interesting lecture is that it does not seem to be grounded in having done lots of interviews or sat in these. Some of his comments were contrarian and his pointers on CVs may be counterproductive.
Aharon Frazer "The Skills That Make a Great Data Scientist"
Aharon was the the only American Rabbi 🐰DS speaker.
He did his studies in US than was a PHP coder.
He suggests asking about jobs not being offered - in smaller companies.
Did BI at "Seeking Alpha" when he was looking for work as a web developer.
Went to Joy tunes and after 4 months was head hunted by FaceBook
He talled about
data engineering -
- Data acessibility
- Data quality
- Logging
- ETL Pipelines
- Dashboards
- Alerts
data science - are analysts
- Indentifing opportunities
- Product visions Foreccasting
- Goal setting & tracking
- Product updates
Before and after analysis -
Reality is messy
Experiments @ Facebook
Exposures \
======> Stats Engine ======> Metrics Change
Metrics /
Some interviewing notions:
I interviewed many more times than I got jobs
look at problems as 3-d
people look as if it is a text book problem but he is more interested in someone who imagines the problem is really happening.
Show you have the template of the problem in your head.
Model - Looking at errors
- Framing the problem first. - "Here is a metric of success."
Self awareness.
Come to agreement with the interviewers.
Some questions have no great answer but cover a fundamental issue.
Pros and cons of real world situations.
One note: Mr Frazer had a slide-deck disaster but it only slowed him down a bit and he could talk well without his slides - kudos on that. If you are going to give a talk practice giving it without your slides.
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