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Davenport University College of Arts and Sciences Master in Data Analytics
Davenport University College of Arts and Sciences

Master in Data Analytics

Grand Rapids, USA

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English

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Sep 2024

On-Campus

Key Summary

    About: The Master in Data Analytics equips you with essential analytical skills, combining theoretical knowledge with practical application. You'll explore data mining, statistical analysis, and machine learning techniques to extract meaningful insights from complex datasets. The program emphasizes real-world projects, enhancing your problem-solving abilities with industry-relevant tools and methodologies.
    Career Outcomes: Graduates can pursue diverse careers, including data scientist, data analyst, business intelligence analyst, and quantitative analyst. Opportunities exist in various sectors such as finance, healthcare, technology, and marketing, where data-driven decisions are increasingly essential.

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Introduction

Data analytics is used to analyze vast databases that must be examined using complex algorithms and artificial intelligence to identify previously unidentified useful sets of relationships and trends. All aspects of the business and medical communities, as well as government agencies and non-profit organizations, rely on data analytics, yet are hampered by a growing shortage of data analysts. Davenport’s 30 credit hour Master of Science in Data Analytics responds to this need. The degree is delivered jointly by the College of Arts and Sciences in partnership with the Colleges of Technology,

Business and Health Professions. The program is online and prepares individuals to conduct sophisticated analysis of existing data and create new data systems and methodologies. It is also designed to enable these individuals to make recommendations that increase the effective use of data to help organizations meet specific goals and respond to new opportunities. The program uses industry-standard software in practical applications directly related to current trends and issues that impact organizations across a broad spectrum. Course progression and content are carefully formulated to build competency in data analysis for students from a broad range of disciplines and experiences, including those who are new to the field.

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