In
today’s world, data science has immensely grown across a multitude of
industries including finance, energy, travel, and government, but even more
importantly, universities have begun to recognize the importance of offering
courses and programs in this field.
Data
Science and Analytics will continue to be one of the cornerstones for
innovation as the businesses explore its revolutionary potential to transform
business processes, generate new business models, boost operations’ efficiency
and catalyze innovation.
Data Science is a multidisciplinary field that involves processes and
systems to extract knowledge, focused on the future by performing exploratory
analysis to provide recommendations based on
models identified by past and present data, representing high value for the business.
While data
science asks:
What will
happen next? And What should be done to prevent...?
The data analysis asks: what happened? And Why did it happen?
The following table explains the differences with respect to processes, tools, techniques, skills and outputs:
Analisis
de Datos
|
Ciencia
de Datos
|
|
Perspectiva
|
Looking backward.
|
Looking forward.
|
Naturaleza
del Trabajo
|
Report and optimize.
|
Explore, discover, investigate and
visualize.
|
Resultados
|
Reports and Dashboards.
|
Data Product.
|
Herramientas
usadas
|
Hive, Impala, Spark SQl and HBase.
|
MLib and Mahout.
|
Técnicas
usadas
|
ETL and exploratory
analytics.
|
Predictive analytics and
sentiment analytics.
|
Habilidades
Necesarias
|
Data engineering, SQL and programming.
|
Statistics, Machine Learning and
programming.
|
