EDeeU Education


Data science is the study of data. Its aim is to understand how to read and extract information from both structured and unstructured data. It combines statistics, data analysis, and machine learning to capture and decipher real-world facts with the use of computers and software. As such, it involves a strong familiarity with mathematics, computer science, and information theory; and it applies these to better understand real-world statistics.

Course Details

Duration: Three to four years (full-time study); six to eight years (part-time study)

Difficulty: Advanced

Entry Requirements: Undergraduate degree or equivalent general education

Course Description

Core Curriculum

The core topics introduce the field of data science and give students the skills and understanding to choose and pursue their interests in the specialized topics. The Director of Studies will make adaptations based on student ability and schedule. On completing the core, students will be able to independently read the historical and academic literature. The core topics are:

  • Statistics and Probability Theory
  • Linear Algebra
  • Introductory Network Theory
  • Databases
  • Data Mining
  • Numerical and Scientific Programming
  • Data Visualization
  • Machine Learning
  • Scientific Writing

The student is expected to be a competent computer scientist, mathematician, physicist, or engineer with computer programming expertise before commencing their studies in data science.


Specializations should be discussed with the Director of Studies. On completing specializations, students will be able to independently read advanced literature and conduct a final project in that topic. Some suggested specializations are:

  • Statistical Learning
  • Statistics in Finance
  • Agents and Multi-Agent Systems
  • Nature-Inspired Learning Algorithms
  • Deep Learning
  • Big Data Technologies
  • Simulation
  • Computer Vision
  • Machine Learning
  • Optimization Methods
  • Artificial Intelligence

Final Project

The final project in data science consists in one or more papers written on an area of specialization. The paper should take the form of a dissertation elaborating an original argument, interpretation, or perspective. It should include original code, but exceptions can be discussed with the Director of Studies. All final projects should be discussed with the Director of Studies, who will assist in choosing an appropriate focus and method. Advanced students may be advised to submit their paper for publication in an appropriate academic journal.

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