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University of Westminster

London, England, United Kingdom
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Data Science and Analytics at University of Westminster at Cavendish

This course (previously Business Intelligence and Analytics MSc) addresses the need to propel information-gathering and data organisation, and exploit potential information and knowledge hidden in routinely collected data to improve decision-making. The course stretches the artificial intelligence (AI), machine learning (ML) and decision science themes to business intelligence, data science and business analytics.

Students will focus on developing solutions to real-world problems associated with the changing nature of IT infrastructure and increasing volumes of data, using applications and case studies, while gaining a deep appreciation of the underlying models and techniques. Students will also gain a greater understanding of the impact technological advances have on nature and practices adopted within data science, business intelligence and analytics, and how to adapt to these changes.

Embedded into the course are two key themes. The first will help students to develop their skills in the use and application of various technologies, architectures, techniques, tools, and methods for data science. These include data warehousing and mining, distributed data management, and the technologies, architectures, and appropriate AI and ML techniques. The second theme will enhance student's knowledge of algorithms and the quantitative techniques including AI, ML, and Operational Research (OR) suitable for analysing and mining data and developing decision models in a broad range of application areas. The project consolidates the taught subjects covered, while giving students the opportunity to pursue in-depth study in their chosen area.

Teaching approaches include lectures, tutorials, seminars, and practical sessions. Students will also learn through extensive coursework, class presentations, group research work, and the use of a range of industry-standard software such as R, Python, Simul8, Palisade Decision Tools, Tableau, and Oracle.

Modules are typically assessed through practical coursework, which may also include an in-class test.

Level Masters
Discipline Business & Management
Duration 12 months
Intakes Jan, Sep
Application Fees GBP 0
Tuition Fees GBP 17000
Campus Cavendish
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Admission Requirements
Language proficiency (minimum)
IELTS 6.5
TOEFL 88
PTE 59
Duolingo 125
Exam proficiency (minimum)
SAT Not Required / Waiver
ACT Not Required / Waiver
GRE Not Required / Waiver
GMAT Not Required / Waiver
Requirements

Minimum GPA - 60.0%

Rankings
QS Ranking --
Times Higher Education --
ARWU 1084
Webometrics --
QS Ranking

QS Quacquarelli Symonds is the world’s leading provider of services, analytics, and insight to the global higher education sector, whose mission is to enable motivated people anywhere in the world to fulfil their potential through educational achievement, international mobility, and career development.

THE Ranking

THE (Times Higher Education) has been providing trusted performance data on universities for students and their families, academics, university leaders, governments and industry, since 2004. We create university rankings to assess university performance on the global stage and to provide a resource for readers to understand the different missions and successes of higher education institutions.

Shanghai Ranking

The Academic Ranking of World Universities (ARWU) was first published in June 2003 by the Center for World-Class Universities (CWCU), Graduate School of Education (formerly the Institute of Higher Education) of Shanghai Jiao Tong University, China, and updated on an annual basis

Webometrics Ranking

The "Webometrics Ranking of World Universities" is an initiative of the Cybermetrics Lab, a research group belonging to the Consejo Superior de Investigaciones Científicas (CSIC), the largest public research body in Spain. CSIC is among the first basic research organizations in Europe. The CSIC consisted in 2006 of 126 centers and institutes distributed throughout Spain.

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