Published Content | 15-21 Sept 2024
Self-created coding tutorials and popular science content from this week.
Apnea Coding:
Fibonacci numbers calculation in Erlang
This is a very simple code snippet demonstrating the basic syntax of Erlang, which is considered as one of the 'pure' functional languages with a rich set of idioms.
Visit the Youtube channel central for the full video:
Enable captions for more details and walk-through. Source code available at the Github repository (see channel info).
Data Science book writing
About two years ago an idea was hovering through my mind, a question actually: Why is that “traditional” project managers fail so gloriously in managing teams, especially in the Research and Development (R&D) field of work? I let it mature for a while, thinking that someone will eventually write a book or something about it. Two years have passed and no one did it, so I figured, well, I guess I will have to do that myself. After sending the proposal to only few top publishers, I was privileged enough to land a book contract with Elsevier, which was indeed my first choice for this endeavor.
The subject of the book will be simple: Managing and coordinating teams in the R&D environment, specifically in the domains of Data Analytics, Machine Learning and Artificial Intelligence in general, where I have been working for more than two and a half decades as a professional. Specifically, it will focus on every aspect of teamwork from the first to the last day of a R&D project, including recruitment, roles, technical problems and solutions, managing risk, conflict resolution, iterations and feedback, expectation management, deadlines, priorities, planning for the future.
I’ve had the privilege to work with exceptional scientists, colleagues and friends and throughout these years I have identified key characteristics of efficient teamwork in challenging situations. Efficient project management is far from enough for building successful teams for R&D. There is no magic recipe for success, but there are definitely some common lessons to be learned.
Starting this July (2024), the full writing of the first draft is active. It took six weeks of preparation, material organization and time planning, and now the main work begins. This subject is a unique case of content and it needs special attention to detail and discipline. Instead of thinking what else can be included, it is more like deciding what I must leave out due to excessive length. The book is going to be in the order or 100k words or 220+ pages in the classic academic / textbook format and it will be published some time around next summer (2025). I have planned the writing of the first draft itself for a span of 22 weeks, i.e., it should be finished by the end of October if everything goes well.
Week 12: Dealing with real-world data
Anyone who has worked in serious R&D projects in Data Science knows it too well: In the real world, there are no well-defined, high-quality datasets to start with. There are always degradations, missing ranges or "gaps" in the domain space, things that make the ML models struggle in training. The goal here is to prepare the DS team for it, especially the younger researchers, with guidance and support from the expert members.
For more details, check out:
https://apneacoding.blogspot.com/2024/07/announcement-writing-new-book-in-data.html
https://youtube.com/shorts/M1RMmK3I7iA