Some ideas to improve your LinkedIn profile
Hey everyone,
We’re entering difficult economic times, so I thought I could share some of the tactics I’ve used to get more job opportunities my way by making my LinkedIn (LI) profile stand out.
I’m not an influencer on LI nor I have insider information about its talent search algorithm. This information comes from reading papers about LI’s search algorithms, researching LI Recruiter, and a lot trial and error experimenting with my own profile.
Let me begin by setting the stage.
To find candidates, recruiters use a tool called LI Recruiter. It allows them to enter relevant search terms such as “Data Scientist” and define filters such as “has worked at Google” to look for candidates.
After a query is defined, LI Recruiter uses a “talent search algorithm” that works in two stages:
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1. It searches the network and defines a set of a few thousand candidates who meet the recruiter’s search criteria.
2. Then the candidates are ranked based on how well they fit the search term and how l
I investigated the Underground Economy of Glassdoor Reviews
Part I
Online company reviews are high stakes.
Top reviews on sites like Glassdoor and Google can get thousands of impressions each month and are major drivers of brand perception.
Employers know this. And when I come across multiple 5 star reviews left with no cons, or a Pulitzer worthy essay from a former intern, I become suspicious.
These reviews start to resemble 30 under 30 lists: so artificially constructed that you begin to question their credibility in the first place.
The scrutiny around company reviews is well documented; some companies file lawsuits worth over a million dollars to reveal anonymous reviewers that complain about their jobs.
Whilst it’s the flashy lawsuits that make the headlines, there also exists an underground economy of company reviews operating quietly every single day.
In this underground economy, some companies pay over $150 to freelancers to try and get a negative review removed. If they want “better” results, they go to the plethora of Online R
PYTHON CHARTS: a new visualization website feaaturing matplotlib, seaborn and plotly
I’ve recently launched “PYTHON CHARTS”, a website that provides lots of matplotlib, seaborn and plotly easy-to-follow tutorials with reproducible code, both in English and Spanish.
Link: https://python-charts.com/ Link (spanish): https://python-charts.com/es/
The posts are filterable based on the chart type and library:
Each tutorial will guide the reader step by step from a basic to more styled chart:
The site also provides some color tools to copy matplotlib colors both in HEX or by its name. You can also convert HEX to RGB in the page:
https://preview.redd.it/hxhdctl2o0x91.png?width=890&format=png&aut
Here are the questions I was asked for my entry level DS job!
Hey everyone. I posted a thread a few days ago about being nervous about my first DS interview. The thread was taken down by mods due to it being more appropriate for the stickied thread. So I want to make this thread less about questions, but more of an informative post to show you some of the questions I was asked. Hopefully it’s helpful for newbies and veterans alike!
SQL:
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• What is a view?
• Is a table dynamic or static?
• Difference between a primary key and foreign key
• Inner Join vs. Left Join scenario (pretty sure it was from w3schools. ez pz)
• WHERE vs. HAVING
• When would you use a subquery? Provide an example
• How would you improve the performance of a slow query?
• EDIT: Some aggregation and GROUP by questions (MAX, AVG, COUNT, etc.) that I just remembered.
Python
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• Explanation of libraries I use (Pandas mainly)
• How would you get the maximum result from a list?
• Can you explain the concept of functions
• Difference between FOR and WHILE loops?
• Gi
Migrating r/DataScience to shitjustworks
I’ll be manually copy pasting (with references) some useful posts from the past year in r/DataScience to get the things rolling in this instance.