Study Track: Data Science (DS)

General description

This study track educates specialists in extracting knowledge from data. The track offers a solid and broad program ranging from theoretical foundations to practical aspects of large-scale (“big”) data analysis. It provides a wide range of courses covering various aspects of contemporary data analysis, including machine learning, information retrieval, parallel processing, and visualization. Graduates of the data science track will come out with a solid background and hands-on experience in this fascinating and rapidly growing area of computer science. They will be well prepared for solving challenging data analysis tasks and pursuing a career in science or industry.

Special requirements

Basic knowledge of and experience in programming is required. Knowledge of linear algebra corresponding to an introductory undergraduate course on the topic is expected (in particular: vector spaces; matrix inversion; eigenvalue decomposition; linear projections). This knowledge can be acquired/refreshed using any introductory book on linear algebra.

Knowledge of basic calculus at an advanced high-school level is also expected (in particular: rules of differentiation; simple integration). This knowledge can be acquired/refreshed using any introductory book on calculus.

Knowledge of basic statistics and probability theory is a plus (in particular: discrete and continuous random variables; independence of random variables and conditional distributions; expectation and variance of random variables; central limit theorem and the law of large numbers).  Weaknesses in one or more of the above areas should not stop you from following this study track, however, be prepared to spend some extra self-study time.

Career opportunities

At the moment, demand is far outstripping the supply of highly skilled data analysts who can take the deluge of raw, unstructured data and then aggregate, clean, transform and analyse it, extract key information and, crucially, infer knowledge from their statistical analyses, and communicate and explain the results.

They should be able to look back in time and understand the data, and also look into the future and predict outcomes. They must be curious, creative and competent. For they will be the unbiased, independent advisors that grant decision-makers their mandate.

The Data Science track at the Department of Computer Science at Copenhagen University has been specifically tailored to gather all the key components that will provide its students with exactly the skill-sets needed to face the challenges that the “Age of Data” will impose. Potential employers of the graduates are Danish and international industrial companies as well as the public sector. As the study track addresses Data Science with scientific depth, graduate students will be prepared to pursue a Ph.D. in Data Science and related topics.

Basic courses of this recommended study track

The recommended courses in the Data Science study track are shown below. Keep in mind that, like for all the study tracks, none of these are actually mandatory, and you may replace them with relevant courses from other tracks as you see fit. 

Block 1 Block 2 Block 3 Block 4
Year 1 Advanced Programming Advanced Computer Systems

Web Science
or
Numerical Optimization
or
Signal and Image Processing (SIP)

Large-Scale Data Analysis
or
Information Retrieval
Advanced Algorithms and Data Structures Machine Learning Randomized Algorithms
Year 2

Programming Massively Parallel Hardware
or
Advanced Topics in Machine Learning
or
Natural Language Processing

MSc Data Science Project
or
Advanced Topics in Image Analysis (Requires SIP)
or
Visualization
MSc Thesis

     Compulsory course
     Restricted elective course
     Elective course