Master of Science (MS) in Data Science

• Completed application form and personal statements (approximately 500 words), which may be submitted online.
• Two recommendation letters (at least one from an academic source and one from an employer).
• Official college/university transcript of undergraduate degree from an accredited institution.
• Official college/university transcript of graduate degree from an accredited institution (if applicable).
• Evidence of proficiency in English: Michigan State University Certificate of English Language Proficiency (MSU – CELP), Michigan Proficiency Certificate, Cambridge Proficiency Certificate, Cambridge Advanced English (CAE) with Grade A only, TOEFL (score 87 or higher on internet-based test), IELTS (score 6.5 or higher), GCE, International Baccalaureate, Graduation from an accredited English language college/institution.
If none of the above is available, the candidate must take the English Placement Test administered by the College. Based on the results of the test, students are placed into the appropriate English for Academic Purposes Program or Writing Program.
• A certified copy of an identification card for Greek citizens or of a valid passport for international students.
• CV (English or Greek).
• Interview with the respective Graduate Program Coordinator.

The MS in Data Science is a cutting-edge program that provides advanced knowledge and practical skills in the science of Big Data. You will learn to turn real-world data into insights, solutions, and tools that drive decision-making in organizations, and have the opportunity to pursue a Professional Certificate in Business Intelligence and Data Mining with SAS.

The MS in Data Science is an advanced program of study that prepares students for a professional career in Information Technology, particularly in handling big volumes of data, finding patterns in data, making predictions, and effectively visualising and communicating data, with the intent to facilitate organizational decision making. Students develop expertise in dealing with various types of data including business transaction data, text data, and images.

The program equips participants with a well-calibrated, theoretical, and practical synthesis of applied mathematics, computer science, statistics, and business information skills. The program allows students to pursue the professional pathway, suitable for immediate professional application, or the research pathway, suitable for advanced graduate study and research. Incorporates within the professional pathway is a Joint Professional Certificate in Business Intelligence and Data Mining offered in collaboration with SAS, a leading company in the field of analytics.

Student & Graduate Profile
The program is ideal for recent university or college graduates of information technology, engineering, economics, and science, or working professionals in IT who wish to advance their career in data science or big data. Students coming from different backgrounds, such as business or social sciences, are also encouraged to apply. The qualifications and experience provided by the program provide local and international employment prospects at a time when there is an increasing demand for Data Scientists worldwide. Graduates of the program can pursue careers in the field of Data Science such as: Business Data Analysist, Data Engineer, Data Scientist, and Big Data Engineer. Furthermore, they may be employed in companies specializing in information technologies, finance and insurance, retail trading, manufacturing, start-ups and more.

Program Structure
Students must successfully complete 12 graduate-level courses, representing a total of 36 US Credits. Students choose one of two pathways: the professional pathway that leads to a professional certificate by SAS, and project work with an organization; or the research pathway that is ideal for pursuing advanced graduate studies in universities or employment in research departments of large organizations.

Curriculum
Required Courses
Introduction to Big Data
Exploring and Analyzing Data
Applied Machine Learning
Data Visualization & Communication
Semantic Web
Big Data in Business
Storing and Retrieving Data
Search Engines and Web Mining
Machine Vision in Data Science
Natural Language Processing
Option 1: Professional Pathway

SAS Platform for Business Analytics
Capstone Project
Option 2: Research Pathway

Advanced Machine Learning
Thesis
Program Level Competencies

Upon successful completion of this program, students should be able to:

• Critically evaluate the techniques for storing and processing big volumes of data, including transaction business data, text data, and images and to apply the relevant tools.
• Analyze the basic machine learning techniques and apply the relevant tools.
• Articulate business problems using data science techniques.
• Design a comprehensive data science solution and assess it both from a technical and a business perspective. 5. Successfully complete a research project in big data or in data science.
• Formulate ideas and arguments and communicate them effectively both in writing and orally in an academic or business context.
• Undertake programming at an advanced level: use advanced algorithms, practice distributed computing, use of no-SQL databases.