Data Science

Overview

For almost half a century now, the world has been producing and collecting data in digital form. However, the past decade hardware and network advances have allowed for very large, cost-effective storage and powerful processing as well as fast transfer speeds. At the same time, scientists have been developing software tools for the analysis of big and complex data, to extract valuable knowledge from them.

At the same time, scientists have been developing software tools for the analysis of big and complex data, to extract valuable knowledge from them. Data Science nowadays is an “umbrella term” that encompasses a variety of scientific fields. Essentially, it is an interdisciplinary field that combines a multitude of different disciplines, some of the most important being the following:

• Artificial Intelligence & Machine Learning

• Statistics & Mathematics

• Database and Big Data technology

• Software Development & Algorithmics

However, the above list is not exhaustive, as a data scientist often needs to employ other skills, such as hacking, coding, critical thinking, problem understanding etc. All those make the job of the data scientist to be a mashup of different skills that are rarely found together.

Students who apply for the Data Science programme of the International Hellenic University, are mainly graduates with a STEM (Science, Technology, Engineering and Mathematics) or an Economics degree, who have a background in statistics and a good knowledge of fundamental concepts of databases and programming.

Career Paths

According to the European Commission’s European Data Market study the number of “data companies” as well as the need for “data workers” are already high and it is expected to grow even more in the near future. Depending on the focus of your study and skills, there are several career paths you can follow; the list below, although it is non-exhaustive, it covers the spectrum of roles you can play in an organisation:

• Data Management Professional: Focuses on managing the infrastructure and storage of (usually big) data.

• Data Engineer/Data Architect: Focuses on the design and implementation of (usually big) data infrastructure, choosing the right database and cloud technologies and deploying them to serve the analytics needs of the organization.

• Business Analyst: Focuses on the analytics part, trying to process data to build models to form useful and actionable insights. It includes anything related to Business Intelligence, such as creating reports, dashboards etc.

• Data Analyst/Data Scientist: Focuses on developing and applying machine learning and statistical models on the data at hand. They need to have coding skills, with Python and R being the most popular options right now as well as knowledge of algorithmics, statistics and databases.

• Machine Learning Researcher: Focuses on developing and testing predictive and descriptive models from data. They need to have a deep understanding of machine learning and statistics to run experiments and evaluate the results. Machine learning research positions are available not only in universities (e.g. PhD candidateships, PostDoc Associates etc.) but also in industry as big companies are being staffed with analytics researchers who try to create custom models for their needs instead of using off-the-shelf products and applying sub-optimal, generic solutions.