Day: July 14, 2018

#ICYDK: NI announced two series of new mmWave radio heads for the mmWave Transceiver System. The new radio heads, which cover spectrum from 24.5 to 33.4 and 37 to 43.5 GHz, are targeted at wireless researchers prototyping 5G New Radio (NR) systems. https://goo.gl/qnFu5w #5G #IoT #5GWorld

Critically reading scientific papers is critical for Data Scientists working some areas – especially those working in health. With that in mind, here are some key considerations in reading scientific (peer-review, grey literature) papers:

Theory: Is the theory sound? Are there theoretical issues in the design that cause problems? Implementation: Are there concerns about the implementation that cause you to question the conclusions?

Methodology best practices: Consider the best practices for doing that particular study type. Did the authors follow these practices? Did they perform the quality checks that have been discussed? How did the study perform on those quality checks?

Threats to validity: What are the “threats to validity?” How have the authors addressed these concerns or are some of these concerns not issues in this study?

Data: Are there concerns about the data acquisition, data quality, etc.? Does the raw data seem compelling or does it have any fundamental flaws? 

Analysis: Are the models logically sound? Are the models strong representations of the data? Are correlations/collinearities impacting the conclusions? Models with low predictive power are a concern. 

Author bias: Are the authors selectively focusing their results or their discussion and ignoring key elements that are not supporting their thesis? If there are reasons to question the author’s bias, that doesn’t mean we reject the study but it does raise concerns.

Further research/ research limitations: How could the study have been improved? What changes would you have made to the data acquisition, study design, analysis, variable inclusion, etc. in order to draw conclusions? Are there reasons that the authors didn’t do the steps you are considering?

Conclusions: After thinking through all of the steps above, do you believe the author’s conclusion? Why or why not? https://goo.gl/7EQg4A #DataScience #Cloud

In 2018, Fast Company declared ‘Data Scientist’ as the best job in America for the third year in a row! 

How many of you have noticed people suddenly calling themselves data scientists?

So many people out there are suddenly calling themselves ‘data scientists’ because it’s been called things such as the sexiest job of the 21st century. That’s just the problem – you have far too many people who are falling into data science without having enough background to really answer the questions that no one else can answer, and are becoming disillusioned with the field.

Matt Tucker, recently posted on Data Science Central, that he believes data science is in its golden years and is dying.

Matt goes on to state:

With the increasing power of user-friendly tools and GUIs, and a data science course seemingly available on every website, being able to perform data science will eventually be like being competent in Excel. Just knowing the ins and outs of data science as a skill will not be enough. The tools will be powerful enough to handle the data “sciencey” aspects, and the fundamental concepts will be taught throughout school, evolving data science into a skill integral to every job role, not a title. There will be no more data scientist roles, just roles that use data science.

While I agree there will be many roles that use data science through the use of intuitive applications and tools which are derived from data science components and machine learning – it will not cause the death of data science.

I go into what I believe is a data scientist here, but if you want to skip that, my TL;DR is that data science is essentially a blend of having business domain knowledge, coupled with math (statistics and probability), computer science (data analysis, programming), and the ability the communicate all of this through data visualization (dashboards, quantitative/qualitative reporting) and storytelling.

As the data science profession settles within the scope of businesses better, there will be plenty of opportunities available.

I believe that true data science isn’t just a science, but also an art. The art of data science is how we apply our domain knowledge and strategic thinking in answering questions and solving of problems. As I see it, the role of the data scientist is to really understand what the problem is that you are trying to solve, and then figure out a way to solve it. 

Data scientists without domain knowledge are how we add risks to the data science profession by producing suboptimal results due to their Rumsfeldian “unknown unknowns”.

This post is also available at the original source ziyadnazem.com https://goo.gl/h7cuxJ #DataScience #Cloud