The tech giant plans to speed up ML proficiency by publicizing its long-internal material.
This sounds like the kind of gift you can't refuse to take advantage of - therefore my opinion is to go there and take a look - at the least you will get an insight into how they do things, at the most, you can get some free training.
Data science MOOCs are superficial. At least, a lot of them are. What are your options when looking for something more substantive?
This points you at four Data Science courses that are available via MOOCs that look substantial and well worth doing as well as you can in order to gain good skills that will be useful to you going forward. As with all MOOCs, there are free paths as well as paid ones that provide you with a certificate and a capstone. I would recommend doing the paid-for option if you can as it will be cheaper than taking a proper course at a University.
Getting better as an engineer is as much about reading code as it is about writing it.
Simon is completely right - I have to admit to you that I read code a lot better than I can write it - but that is ok as it is by reading code that I can improve how I write code. It also means that I can read code in some languages that I know I could not write in - but as long as I can understand it and even communicate to those who actually can (and probably actually did write it) how to improve it by doing something different with the data, that I can contribute to the understanding of and manipulation of the data to provide insights and information.
Business analytics and data science are often used interchangeably but are different at many levels. While both are used in solving business problems and decision-making processes in organisations, both have different job descriptions and skills requirements.
Interesting career advice that could prove invaluable and save making a mistake on the way you choose between these two subjects.
This article demonstrates the approach of how to use Spark on Kubernetes. It also includes a brief comparison between various cluster managers available for Spark.
I thought this was a really good article with a great level of detail. If you are interested in doing this in real life I recommend you read this first as there are code snippets and it will get you ahead of the curve.
Learn new techniques and optimize your existing code.
There are lots of useful Python posts on Medium by Jonathan so I suggest you read a few of them as it might improve your code a great deal.
Why it’s so hard to find roles in cloud technology, while jobs go unfilled.
I found this really interesting and could be insightful for anyone who is struggling to get a job even though they did certifications in the attempt to be more qualified. I have to agree, relevant experience is key.
