downloading folders from google drive.

I wanted to download some course material on RL shared by the author via Google drive using the command line.  I got a bunch of stuff using wget a folder in google drive was a challenge. I looked it up in SO which gave me a hint but no solution. I installed gdown using pip and then used: gdown --folder --continue if there are more than 50 files you need to use --remaining-ok and only get the first 50. In such a case its best to download using the folder using the UI and decompress locally. Decompressing from the command line created errors related to unicode but using the mac UI I decompressed without a glitch.

Big Data Analytics Israel - New Year, New Data Scientist Job: 5 Things To Think About

Data science interviews can be over whelming 

New Year, New Data Scientist Job: 5 Things To Think About

My notes:

The first talk was by:

Raya Belinsky - "New job - yes or no?"

The talk about finding your next job or reinventing your current jobs. Miss Belinsky's humour and background as an executive life-coach made this talk both pleasant and worth-while.

She covered her operational definition of job burnout
Linkin profile - complete the profile (it tells you what to do)
The CV - ask 2 people to prepare it
The Interview - e.g. prepare 3 questions

Each had at least a couple of points worth taking care of in your next round of job search. Check out the talk and slides when they go online.

Second talks by:

Nathaniel Shimoni - "Life story"

Mr Shimoni is an experienced story and had a compelling story to tell and his own twiting path to  becoming  a data scientist. 

Some Highlights:
  • Listened 1 hour to lectures during commute.
  • Later he decided to dedicate 1 hour a day to 
  • Participated in many Kaggle competition 
  • Liked: Data Hack
  • Read papers - keep up to date
  • Recommends to take risks you can afford

Third talk:

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.

  • 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.

Fourth talk: 

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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