Data ScienceTech Institute - MSc Data Scientist Designer

How to become a Data Scientist?

Data Science and Big Data are not just buzzwords. Decades of software engineering and research lie behind the recent media coverage.

What media do not describe so well is that if Data Science and Big Data are indeed naturally connected, the two are not exchangeable.

Data Science encompasses models, methods, tools and techniques for dealing with data, no matter the size of the dataset, requiring highly qualified Data Science Engineers to build them.
Big Data is an observable phenomenon of exponential data growth, which requires highly qualified Data Science Analysts to extract meaningful and actionable knowledge for their employers.

As a consequence, Data Scientists come in three flavours:

  • the specialists in Information Systems and IT for Big Data who usually come from a computer science background,
  • the applied mathematics lovers who focus on algorithmics for artificial intelligence (optimisation, statistics, machine learning, deep learning)
  • the Data Engineers, i.e those who can do both!

At Data ScienceTech Institute, we are very conscious the job markets expectations and have created the most intensive programmes for covering all needs: the MSc in Applied Data Science and Big Data & the Advanced MSc in Information Systems & Artificial Intelligence for Big Data Engineering, all accessible from either our Nice Sophia-Antipolis or Paris campuses as well as online.

What is Data Science?

Data Science is the study of the computational principles, methods, and systems for extracting knowledge from data. Large data sets are now generated by almost every activity in science, society, and commerce — ranging from molecular biology to social media, from sustainable energy to health care.

Data Science asks: How can we efficiently find patterns in these vast streams of data? Many research areas have tackled parts of this problem: machine learning focuses on finding patterns and making predictions from data; databases are needed for efficiently accessing data and ensuring its quality; ideas from algorithms are required to build systems that scale to big data streams; and natural language processing, computer vision, and speech processing are each needed for analysis of different types of unstructured data. Recently, these distinct disciplines have begun to converge into a single field called Data Science.

What is Big Data?

Big Data is an all-encompassing term for any collection of data sets so large and complex that it becomes difficult to process them using traditional data processing applications.

The challenges include analysis, capture, curation, search, sharing, storage, transfer, visualization, and privacy violations. The trend to larger data sets is due to the additional information derivable from analysis of a single large set of related data, as compared to separate smaller sets with the same total amount of data, allowing correlations to be found to “spot business trends, prevent diseases, combat crime and so on.”

How do we get you there?

Data Science and Big Data are transverse to organisations. Looking at self-declared Data Scientists on Linkedin, shows backgrounds as diverse as mathematics, physics, biology, geography, electronics, telecoms or, of course, computer science graduates.
For at least the last ten years, it is also rather noticeable that all science-based higher education has been including various courses of computer science and IT.

Throughout this kaleidoscope of skills, a common denominator strongly appear: science and most particularly the “scientific method”, most notably thanks to mathematical education. This way of reasoning via systematic observation, measurement, experiment, and the formulation, testing, and modification of hypotheses is common to most of higher education graduates.

As such, and in order to graceful integrate students from all educational and professional backgrounds, Data ScienceTech Institute organises its programmes so that students will start with the most “science-oriented” courses, where tutorials will bring everyone together to computer science and IT applications.
Thanks our “project-based” approach of education, the “Information Systems” core courses will allow your lecturers to entrust projects leaderships to your fellow students with more CS/IT experience, as they will create the groups dynamic.