“Data Science Is Trial And Error, It’s Research And Recursive, It’s Practical And Theoretical, It Requires Domain Knowledge, It Boosts Your Strategic Skills, You Learn About Statistics And Master Programming Skills. But Most Importantly, It Teaches You To Remain Patient As You Are Always Close To Finding A Better Answer.” Farhad Malik ( December 28, 2018 )

“The value of log data for business is unimpeachable.” Kiyoto Tamura ( April 21, 2015 )

“Ethics are essential to establishing a guiding basis for how powerful new technologies are developed and used.” Andrew Maynard ( 15.05.2019 )

“It is with data affected by numerous causes that Statistics is mainly concerned. Experiment seeks to disentangle a complex of causes by removing all but one of them, or rather by concentrating on the study of one and reducing the others, as far as circumstances permit, to comparatively small residium. Statistics, denied this resource, must accept for analysis data subject to the influence of a host of causes, and must try to discover from the data themselves which causes are the important ones and how much of the observed effect is due to the operation of each.” G. U. Yule and M. G. Kendall ( 1950 )

“The point is to embrace Big Data Management for analytics as a unique practice that doesn’t follow all the strict rules we’re taught for reporting and data warehousing.” Philip Russom ( 2013 )

“… that the more data-driven a firm is, the more productive it is (statistically) …” Foster Provost & Tom Fawcett ( 2014 )

“Having the right data is usually better than having more data; little data can be just as valuable as big data. In many cases, there are no data – because relevant data don’t exist, cannot be found, or are not available.” Christine L. Borgman

“These days, if you build a community around yourself, the news and people start to find you.” Andre Karpistsenko

“The “Iceberg Secret” speaks to the apparent gap between technical and non-technical stakeholders when it comes to evaluating the quality and progress of building an AI-based solution. Often the solution is judged on the visualization of the data or the scalar output of the predictions (the 10%), and little regard is given to the bulk of the work that is spent on the data preparation (the 90%). For the Data or ML Engineer, they understand the exponential value in carefully focussing on understanding the data, preparing the data and how important it is to ensure that a mature data pipeline is in place before any time is spent on the top 10%.” Isak Bosman ( 14.03.2019 )

“Big data isn’t merely an extension of BI and it isn’t necessarily about sifting through huge volumes of data. What sets organizations that are extracting meaningful insights from their big data apart from others is their ability to tap the right data in the right situation.” IBM ( 2015 )