Data Science

What is Data Science?

Data science, due to its interdisciplinary nature, requires an intersection of abilities: hacking skills, math and statistics knowledge, and substantive expertise in a field of science.


  • Hacking skills are necessary for working with massive amounts of electronic data that must be acquired, cleaned and manipulated.
  • Math and statistics knowledge allows a data scientist to choose approriate methods and tools in order to extract insight from data.
  • Substantive expertise in a scientific field is crucial for generating motivating questions and hypotheses and interpreting results.


  • Traditional research lies at the intersection of knowledge of math and statistics with substantive expertise in a scientific field.
  • Machine learning stems from combining hacking skills with math and statistics knowledge, but does not require scientific motivation.
  • Danger zone! Hacking skills combined with substantive scientific expertise without rigorous methods can beget incorrect analyses.

Unleash the power of your data

Never did we generate and store so much data with an unseen variety of form and structure. Data science continues to expand and adapt at an astronomical rate on an ever-growing pile of data. Using data science and advanced analytics techniques, you can and will leverage the power of your data. Develop new products, operationalize them and extend your current analytical capabilities in your journey to becoming a data-driven company.

What can Data Science do for you?

Our Data Science team explores the possibilities of your data and builds models that will provide insights and knowledge to support meaningful decision-making. We continuously gain experience in a wide range of machine learning and visualization techniques, including classification, regression, segmentation, time series, text mining and geospatial statistics. For instance, we can help you predict churn rates, burn-outs, credit risks, machine failures, sport injuries and patient re-admission. Our team uses a variety of data science tools, including R, Python, SQL, Spark, Hadoop and Oracle Advanced Analytics, while always on the lookout for new, emerging tools.