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Back to News 14/03/2018

What is meant by data science?

Data is big, really big. By 2020, it is expected that there will be 1.7 MB of data, created every second for every person on the planet. And data is powerful. It helps us to create insight, build models, make predictions, and develop knowledge. Out of this new age of data comes a discipline to truly understand and utilize these data - ‘data science’. But what is data science?

A short history of data science

We talk a lot about the creation and application of data in modern business, but data has always been an intrinsic part of what science is about; after all, you need data from experiments to test a hypothesis. It is natural, then, to take digital data, generated from our digital lives, and use it to model events and build knowledge.

The beginning of data science is most often attributed to John Tukey, a statistician, who in 1962 remarked on how the world of statistics was being disrupted by data generated by computing.

Fast forward to 1989 and the first conference on ‘Knowledge Discovery and Data Mining’ was held. Since then, changes in computing, driven by the ubiquitous nature of the Internet has created more and more data. By 2008, Hammerbacher and Patil (formerly of Facebook and LinkedIn) had coined the term ‘data scientist’.

Between 2011 and 2013, 90% of the data ever seen in the world had been generated. And now, with the advent of the Internet of Things and smart cities, we can expect that figure to be blown away. The discipline of data science is truly here to stay and will become even more intrinsically linked to business marketing and operations.

 

The Venn diagram of data science

Data science gives us the tools to take these data and put them to work. To be effective, data scientists have to call upon multiple skills. To succeed in the application of data, there are specific core competencies that a data scientist needs to master. One of the best-known descriptions of this skillset is in the form of the Drew Conway Data Science Venn Diagram. Drew Conway is a leading light in applying computing and data to solve behavioral problems.

He created his Data Science Venn Diagram to describe the three core skills that make up a data scientist’s toolset. The Data Science Venn Diagram focuses on these main areas of knowledge needed to be successful in the field:

  • Hacking skills: This is a skill needed to extract the data. This may sound like you need to be a computer programmer to be a data scientist, but it is more about being able to manipulate and distill data using computing tools.
  • Math and statistics: Once you have the data, you then need to know what to do with it. Having at least a reasonable comfort level in using maths and statistics is important.
  • Substantive expertise: Having knowledge about the area where you wish to apply your data and results, allows you to create “substantive hypotheses” that is you must know how to ask the right questions. emlyon business school has uniquely built their MSc in Digital Marketing & Data Science program around the convergence of data science and digital marketing. The program gives you the ‘substantive expertise’ needed to become a successful practitioner in digital marketing through the application of data.

Marketing is an area that is embracing the explosion in data, using it to develop better campaigns based on data-driven customer insights. At emlyon business school, you have an opportunity to combine data science and marketing to give you the tools to take on modern marketing and succeed.

“MSc in Digital Marketing & Data Science is designed to grow a new generation of leading marketing specialists – digital savvy professionals that can benefit from an explosive growth of online technologies to develop business.”  - emlyon business school. The program is headed by Margherita Pagani, Ph.D. Professor of Digital Marketing and Clement Levallois, Associate Professor in Data Science for Management.

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