Tag: data science

5 interesting things (20/06/2021)

9 Steps to Software Project Handovers – handover is always a challenge and especially when a person leaves the organization and is no longer available for questions or able to access the resources. Similar issues can also arise when you leave the project for a while and then come back – you don’t alway remember all the tricks you used to run the code or the tiny bits of each function. This post suggests practical steps and behaviours that many of them can be TL;DRed with the Zen of Python – “Explicit is better than implicit.”.
https://betterprogramming.pub/9-steps-to-software-project-handovers-9325fbb72cfc

How to make an awesome Python package in 2021 – this post is a walk through building a python package. My main issue with it is the dependency management. I would put it in a different file, separate the development and usage dependency and lock the version – or shortly would rather use pipenv or poetry instead of pip but this could also be achieved with pip.

https://antonz.org/python-packaging/

A Simple Framework for Software Engineering Management – the suggested framework is indeed easy – 3 types of responsibilities (people management, delivery leadership and technical system ownership) vs 3 ranks of priorities (issues, things that are ok, ideas and aspirations). This framework is a good starting point for engineering leaders but also for engineers that can switch the people management with colleagues relations or similar or for personal growth. 

https://medium.com/swlh/a-simple-framework-for-software-engineering-management-f70b216540f2

Full Cycle Data Science (FCDS) – this is a heavy read but worth it both for data practitioners  and managers. Data science projects fail often. Sometimes it is because the problem is not defined well, other times because there is not enough data, data is not relevant, etc. FCDS tries to cast light and solve some of those problems – “In a nutshell, FCDS is a way of life that enables a single data practitioner to close the full product lifecycle and independently deliver end-to-end products, focusing only on where they bring added value”.
https://towardsdatascience.com/fcds-b2d2e6b08d34

Endorse People Publicly, and Other Actions for Allies –  Better Allies is an approach of making everyday actions to create inclusive and engaging workplaces. This post is a weekly newsletter where Karen Catlin (Advocate for Inclusive Workplaces) shares five simple actions to create a more inclusive workplace and be a better ally. This is a weekly reminder to be aware of biases and gaps and ideas to small and consistent changes that can make us and the people around us more comfortable and help everyone be the best version of themselves.
https://code.likeagirl.io/endorse-people-publicly-and-other-actions-for-allies-9352915c0956

5 ways to follow publications in your field

This post was published on Medium

An important part of the data scientists and researchers’ life is to keep track of publications in their field. Depends on your field and needs publications range from papers in academic conferences and proceedings (some of them you can find as youtube videos), new technology and code packages, blog posts, etc. This post focus on who to keep track of academic research and innovation.

  1. Follow the relevant conferences, journals — make sure you are familiar with the main conferences in your field (ee.g.List of Machine Learning and Deep Learning conferences in 2019 / 2020) and follow their publications. You can usually read the accepted papers in the conference website when the paper acceptance is published. Talk slides and videos are usually accessible a short while after the conference. Identifying the relevant conferences may require some initial effort but once you identified it, it is easy to get it going.
  2. Google scholar e-mail alerts — track authors and \ or keywords you find relevant for you. E.g if you are interested in causal inference you would probably want to follow Judea Pearl. You can track new articles, citations and new articles related by author or keywords. I prefer to track only new articles because I found the benefit from citations and related articles low. You can also get email alerts by more complex queries. Set your alerts here.
  3. arXiv E-Mail Alerting Service — arXiv provides a daily digest of new submissions by subject, it is less granular and less focused than google scholar but can give you access to the newest, hottest submissions. Subscribe to arXiv E-Mail Alerting Service here).
  4. Follow blogs and publications of companies and research institutes which interest you — those are usually softer publications that give you a taste of the company’s recent advances and research. If this lights up your imagination, move on to reading the full paper. Examples of such blogs — facebook research blog, OpenAI blog, Google AI blog.
  5. Social media — follow researchers which are relevant to your field in twitter, see the papers they publish and recommend, read the discussions they are involved in. Join facebook groups that discuss the topics you are interested in.

Now, you can lean back and enjoy the new ideas coming to you. The next challenge is to wisely invest your time and to pick the papers which will be most beneficial for you.