“You don’t have to know everything, but you should have a general idea.” John Foreman

“As I’ve worked in the data science arena long before that name was even used, and having gone through an aborted graduate program in computer science and mathematical statistics some years ago, I think I have a unique perspective for how Data Science manifests itself today. I see it as a confluence of disciplines: computer science, mathematical statistics, probability theory, machine learning, data analysis, and visualization. Being a theorist specializing in applications of machine learning, I can safely say that I’d be hard pressed to minimize the importance of any of these disciplines, especially statistics, in its overall impact to the to the field.” Daniel Gutierrez ( 2013 )

“Learning agents that are not only capable of taking tests but also innovating is becoming the next hot topic in AI.” Yuhang Song, Jianyi Wang, Thomas Lukasiewicz, Zhenghua Xu, Mai Xu, Zihan Ding, Lianlong Wu ( 17 May 2019 )

“As data becomes cheaper and cheaper there are more people that want to be able to analyze and interpret that data. I see more and more that people are creating tools to accommodate folks who aren’t trained but who still want to look at data right now. While I admire the principle of this approach – we need to democratize access to data – I think it is the most dangerous way to solve the problem. … The danger with using point and click tools is that it is very hard to automate the identification of warning signs that seasoned analysts get when they have their hands in the data. These may be spurious correlation like the plot above or issues with data quality, or missing confounders, or implausible results. These things are much easier to spot when analysis is being done interactively. Point and click software is also getting better about reproducibility, but it still a major problem for many interfaces.” Jeff Leek ( October 9, 2014 )

“The traditional software engineering approach is to run tests every time there is a change in the codebase. But applying machine learning to DevOps, you need to remember that the quality of machine learning can change with no changes in the code.” Boris Tvaroska ( 2017 )

“To get meaningful results from big data, companies need to slice and dice their data in many different ways to work out which parts are worth using. Considering the enormity of the task, for many, this requires– above all – speed.” Simon Garland ( September 16, 2014 )

“In the near future we might see BI take another direction: Rather than companies merely purchasing dashboard reporting software for the purposes of internal usage, we’ll be seeing a surge in companies looking to integrate advanced analytics and reporting into their own products. Welcome to the world of embedded analytics.” Eran Levy ( December 17, 2014 )

“Feeding the algorithm raw data rarely ever works, so feature extraction is a critical part of the traditional machine learning workflow.” Nikhil Buduma ( 29 December 2014 )

“Information is the oil of the 21st century, and analytics is the combustion engine.” Peter Sondergaard

“In today’s competitive environment, enterprises require fast, effective and agile business solutions. In other words, not traditional business intelligence.” Simon Moss ( 21.12.2015 )