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My 2018 reading list

Yet another year of books! Ok, in comparison with the previous list, this one is much shorter. 2018 was a great year, full of challenges, work activities and fun, so I didn’t commit too much time to reading.

Without further ado, the books I’ve read were:

Spreading the word: My talk on data science with R

Last week I had a great time at the FLISoL 2018 Tucumán. First organized in 2005, the Festival Latinoamericano de Instalación de Software Libre (Latin American Free Software Install Fest) is the biggest event for spreading free software in Latin America and Spain.

Books I've read on 2017

For 2017, as part of my yearly planning, I tried to make more time to read several books that I had selected from my list (tsundoku, anyone?). It felt like a good challenge, both in planning and execution, and now I can say it was a great initiative to move forward in both my personal and professional life.

From january to december, I had read the following books:

Ethics in data science

Working with data is not only about algorithms, feature selection, business domain and all of the other technical topics usually brought up in discussions about the field. The current availability of data and processing power enables us to invent new ways to approach problems in society. Now more than ever, we as tech workers need to think how to act ethically and not just by the law. Fortunately in the past years we saw new initiatives like Fairness, Accountability, and Transparency in Machine Learning, books like Weapons of Math Destruction and courses like Data Science Ethics.

AI safety

Safety in Machine Learning and Artificial Intelligence is a very active research area. For a quick introduction to the subject in bite-sized chunks I strongly recommend to follow the work of Robert Miles. After gaining popularity in Computerphile videos he has now his own YouTube channel. Computerphile compiled a playlist about AI and Rob is producing a very interesting video series about a paper on concrete problems in AI safety here.

My learning path in data science

For more than two years now, in parallel to my daily data-related work, I’ve enrolled in several interesting courses. In this post I’d like to describe these as part of my learning path, even when some of them are no longer active you can still grab the resources.