“Keeping your eyes on the final deliverable is essential to solving the right problems.” Jonathan Lenaghan

“A Data Scientist is someone with deliberate dual personality who can first build a curious business case defined with a telescopic vision and can then dive deep with microscopic lens to sift through DATA to reach the goal while defining and executing all the intermittent tasks.” n.n.

“The simplest hypothesis that fits the data is also the most plausible.” James Faghmous ( Nov 7, 2013 )

“Artificial Intelligence (AI) technologies could be broadly categorised into Analytics and Autonomy. Analytics focuses on algorithms offering perception, comprehension, and projection of knowledge gleaned from sensorial data. Autonomy revolves around decision making, and influencing and shaping the environment through action production.” Guoyun Tu, Yanwei Fu, Boyang Li, Jiarui Gao, Yu-Gang Jiang, Xiangyang Xue ( 21.12.2018 )

“The stunning achievements and advancements made on behalf of humanity within mathematics are paramount. After all, it is thanks to engineers, mathematicians and statisticians that we ever put a man on the moon, that we’ve ever mapped the floor of the ocean, that human eyes have ever been able to see out-of-this-world phenomena the likes of colliding galaxies or the rings of Jupiter. In fact, it’s arguable that mathematics is the very foundation of our physical world. The Fibonacci sequence, while prone to fodder for conspiracy theorists, can be found in every corner of the universe, from spiral galaxies, to sea shells, to the ratios of your facial features.” Tracey Wallace ( September 8, 2014 )

“A data scientist must possess the knack of being able to ‘identify business value from mathematical models.’ But that vital business value can only materialize if the data scientist also networks with other departments, understands their objectives, is familiar with their data and processes – and can spot the analysis options they provide.” Andreas Schmitz, Alexander Linden ( March 11, 2015 )

“The best technology tools will be those that empower subject matter experts to quickly apply intuitive reasoning:
• Data visualization and advanced analytics that do not require programming or advanced technical skills
• Data integration and workflow tools to rapidly infuse existing data sets with new, untapped data sources that enable a more complete analytic picture – again with no programming required
• Agile application development tools that shield the user from coding, data connectivity and other programming challenges
• Tools for predictive analytics that support intuitive reasoning as to what data attributes and other conditions will impact future performance.” Mike Urbonas ( April 11, 2015 )

“(Graph Databases:) No longer need enterprises make tradeoffs between rich and reach; they can submit highly complex queries against enormous, highly varied data sets without concern that new data with varying structure will break their queries. These innovations are also bringing the capability for asking questions that, in the SQL environment, would otherwise require tens or hundreds of table joins.” Tony Baer ( 2014 )

“Raw numbers are easy to report and analyze, but without the proper context, they can be misleading. Is the effect you’re seeing real, or a simple result of the underlying, obvious distribution? Too many analyses and news stories end up reporting things we already know.” Robert Kosara ( 23.07.2014 )

“It is important to remember that Data Science techniques are tools that we can use to help make better decisions, with an organization and are not an end in themselves. It is paramount that, when tasked with creating a predictive model, we fully understand the business problem that this model is being constructed to address and ensure that it does address it.” Damian Mingle ( September 15, 2015 )