Data Science

This course looks at the laws that regulate the use of data and the role of ethics in helping data scientist determine ethical pitfalls.

The first half of the course introduces the fundamental principles of moral philosophy and ethics and their applications to data science processes and outcomes. The second half of the course looks at the various data regulations at the local, regional, and international level, and how these laws and regulations impact data science projects.

This course teaches linear algebra and other mathematical foundations of data science. Linear algebra is the mathematical foundations of data science that deals with planes, vectors and vector spaces, matrices and linear transformation of vectors and matrices, which provides basic foundations for machine learning.

The course consists of lectures and hands-on training with open source libraries (such as numpy and sciPy) in Python. Finally, students will develop mathematical skills that are needed to understand the foundations of various ML algorithms.

This course teaches basic Python programming knowledge, as well as advanced topics and libraries for data science. Students will learn on how to use the Python programming language to work with numerical, string, and other data structure and sequences, to use expressions and control flows to perform data analysis. Students should also be able to import Numpy and Pandas to perform more complicated data storage and manipulation. Students should also be able to visualize results through using Matplotlib or Seaborn libraries.

The course will start with basic programming skills, and then focus on open-source technologies. It would consist of lectures and hands-on training with open-source libraries in Python for data preparation, manipulation and data visualizations. Finally, students will develop practical programming skills in problem solving by working on real-world datasets as part of their course project.

At completion of the course Databases students will be able to explain what a relational database is, what it can be used for and how it differs from other forms of persistent saving. Students will have learned how to model and structure data for a domain, as well as how to establish tables, enter various types of data, link tables and perform SQL queries. They will be able to explain and apply the principles of good design (normalisation and use of keys).

The goals of this course are two-folds: First it provides knowledge of key concepts, methods, techniques, and tools of big data. Second, it provides an overview of cloud computing, its enabling technologies, main building blocks and architectures and hands-on experience through projects utilizing public cloud infrastructures (eg Amazon Web Services (AWS), Microsoft Azure). As part of these goals, the course will also introduce and cover the topics of cloud infrastructures, virtualization, software defined networks and storage, cloud storage, and distributed programming models such as map-reduce, parallel programming models like Dryad, dockers and containers and so on.

The course will focus on open source technologies to the extent possible and consists of lectures and hands-on training with open source libraries and public cloud infrastructures such as AWS and Azure. Finally, students will develop practical programming skills in cloud storage systems and learn to develop different applications in several distributed programming paradigms.

This course introduces theoretical principles of probability and statistics with a focus on practical applications in data science. Topics include but are not limited to: permutations and combinations, frequentist vs. subjectivist probability, parametric vs. non-parametric statistics, probability distributions, Bayesian inference, null hypothesis significance testing, confidence intervals, effect sizes, point estimation, linear regression, multiple regression and logistic regression.

This course teaches principles and techniques for visual analytics of organizational datasets. The course will enable students to design and develop information dashboards for applications in various domains. The students will be able to reflect upon the different models, theories, and frameworks for visual analytics from a decision-making perspective. The students will also work in groups and learn to communicate effectively involving technical and non-technical people assisting in informed decision making.

Trusselbildet for en datamaskinbruker er i dag preget av angrep fra datakriminelle som er ute etter direkte økonomisk gevinst, eller å overta enkeltmaskiner for å benytte disse videre til kriminelll virksomhet. Bevissthet om de ulike trusslene som finnes i Internett er forutsetningen for å treffe riktige tiltak. Etter å ha fullført emnet skal en student være i stand til å analysere trusselbildet og foreta egnede sikringstiltak på egen maskin, i eget hjemmenettverk og gi begrunnede råd i forhold til oppsett og teknologivalg for websteder. Man skal også ha oversikt over hvilke lover og forskrifter som gjelder for bruk av datamaskiner til lagring, prosessering og formidling av data, her under personvern og opphavsrett.