Data Science is the science (and art) of extracting knowledge or insight from raw data, whether that is applied to research or to industry. Extracting meaning from and interpreting data requires tools and methods of multiple disciplines such as mathematics, statistics, physics and computer science.
Concise and informative visualisation is the key for comprehension and communication of data and analysis results. Especially these days where the amount of data is often huge and traditional visualisation forms may not be suitable. Treatments like data reduction or aggregation are then needed to visualise the data efficiently.
In compute intensive applications, maximizing the performance of limited hardware resources will always be a fundamental challenge. Be it numerical solution of partial differential equations or Monte Carlo integration to compute the multidimensional definite integral of some interesting function.
In today's world of everything distributed, making a system do the right thing is not always an easy task. In addition, requirements constantly evolve and business owners often like to use the newest and shiniest advancements. Developing software systems in a maintainable manner with focus on correctness becomes then a challenge. And we are up for that challenge - our software engineers usually consider the case more interesting, the more complex it gets.