Credimus! Possumus! Vincimus!
🤍 ❤ 🤍
This is the time for me to start looking for a new position. The first thing to do when you start this process, is to update your CV and resume. Although a lot of people (including myself until recently) think that these are two identical documents, in reality they are not. Resume is a short (max 3 pages) concise summary of your experience and achievements that show how you fit the future position. HR people screen tons of documents everyday, and they want to know if a person fits the position from the first glance. In CV, you describe your experience in details, mentioning all the projects that you have participated in, your contributions, what technologies have been used, etc. Moreover, if you have an academic experience, you list there all your publications and academic achievements. As a result, your CV could be quite long especially if you have huge experience, a lot of publications or both. Thus, if you have been chosen the interviewers may understand your experience in details.
Still, both these documents may share the same sections like education and working experience. In order to follow the DRY (don’t repeat yourself) principle and unify the style of my CV and resume, this time I have made them using the same LaTeX template called moderncv. In this article, I explain how I maintain these two documents together and list the modifications that I have made.
Nowadays, it is a quite popular to store semi-structured information using JSON format. Indeed, JSON files have quite simple structure and can be easily read by human beings. JSON syntax allows one to represent complex dependencies in data and avoid data duplication. Moreover, all modern programming languages have libraries that facilitate JSON parsing and storing data into this format. Not surprisingly, JSON is extensively used to return data in Application Programming Interfaces (APIs) .
At the same time, data analysts prefer to deal with structured data represented in the form of series and dataframes. Unfortunately, transforming JSON data into structured format is not that straightforward. Previously, I preferred to develop code to parse manually complex JSON files and create a pandas dataframe from the parsed data. However, recently I have discovered a pandas function called
json_normalize that saved me some time in my projects. In this article, I explain how you can start using it in your projects.
Until recently, I used tmux occasionally, only if I had to run some experiments on a remote server and later see the results of the execution. Basically, I used it only as a mean to execute commands in the background. If I needed to run several commands on a remote server parallelly, I used to open several terminals, connect each of them to the remote host and then switch between them.
Recently, I started working with a remote server through
ssh more often and the routine, I used to, became very operation consuming. So, to improve my effectiveness, I spend several hours reading articles, watching videos and trainings how to use tmux. This article combines the knowledge I have acquired. It is also a crib for me if I forget something in the future.