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Ireland
Dublin Business School

Dublin Business School

dublin (dublin), Ireland

MSc in Data Analytics

Free Application Assistance

Data Science & Big Data

Specialization

1 year (full time)

Duration

$29,060 overall

Tuition Fee

fall, spring, summer

Intake

Table of Contents

Eligibility Requirements Introduction Program Description Program Curriculum Employment

Eligibility Requirements for Dublin Business School MSc in Data Analytics

55/100

Bachelor's GPA Requirement

Relevant degree degree

UG Requirement

Maximum Backlogs Accepted

About Dublin Business School

Dublin Business School (DBS) is one of the largest independent colleges in Ireland, offering a range of undergraduate and postgraduate programs in business, law, arts, media, psychology, and IT. DBS has a diverse student body, with students from over 70 countries, and provides a student-centered learning environment with small class sizes and individual attention from experienced faculty members. The college has a strong focus on employability, with many programs offering work placements and internships, and a career-focused curriculum designed to prepare graduates for success in their chosen fields. DBS is located in the heart of Dublin, a vibrant and multicultural city with excellent career opportunities.

Dublin Business School MSc in Data Analytics Program Description

This Master of Science in Data Analytics has been designed to meet the growing need for graduates with data science skills in the light of increasing applications of new and existing technologies and techniques such as statistical analysis, machine learning, and data visualization across many industries throughout the global economy. Given the rapid growth in internet data usage, the shift to cloud computing, and the rate at which Irish businesses integrate data and analytics into their daily operations, Data Analytics is an identifiable discipline with a breadth and depth of content that encompasses many of the subfields (e.g., software development, machine learning, data visualization that form the modern AI ecosystem).

The program aims to respond to the ever-growing demand across industries for data analytics specialists involving skills in technology, programming for data analytics, data analysis with related context, and graph technology, considered effective means to empower the development of sophisticated AI applications. Additionally, the program learning outcomes focus on the learner’s ability to meet requirements and deliver against business intelligence goals of organizations they will work in and allow for all of these outcomes to be demonstrated from an academic perspective but also have a portfolio piece of work that can be shared with current or prospective employers.

Program URL:https://www.dbs.ie/course/postgraduate/master-of-science-(msc)-in-data-analytics

Dublin Business School MSc in Data Analytics Program Curriculum

1.Data Mining: This elective would cover the principles and techniques of data mining, including data preprocessing, classification, clustering, and association rule mining.

2.Big Data Analytics: This elective would cover the principles and techniques of analyzing large data sets, including data warehousing, data mining, and data visualization, with an emphasis on big data technologies.

3.Predictive Analytics: This elective would cover the principles and techniques of predictive analytics, including statistical models, machine learning algorithms, and data visualization.

4.Business Intelligence: This elective would cover the principles and techniques of business intelligence, including data warehousing, data mining, and data visualization, with an emphasis on decision support systems and business analytics.

5.Natural Language Processing: This elective would cover the principles and techniques of natural language processing, including sentiment analysis, text classification, and information retrieval.

Electives

1.Data Mining: This elective would cover the principles and techniques of data mining, including data preprocessing, classification, clustering, and association rule mining.

3.Predictive Analytics: This elective would cover the principles and techniques of predictive analytics, including statistical models, machine learning algorithms, and data visualization.

5.Natural Language Processing: This elective would cover the principles and techniques of natural language processing, including sentiment analysis, text classification, and information retrieval.

Core Courses

1.Data Science: This course provides an introduction to data science, including data acquisition, data preprocessing, exploratory data analysis, and data visualization.

2.Statistical Methods: This course covers statistical methods for data analysis, including probability theory, hypothesis testing, regression analysis, and ANOVA.

3.Data Management and Warehousing: This course covers the principles and techniques of data management, including data warehousing, data integration, and data governance.

4.Machine Learning: This course provides an introduction to machine learning, including supervised learning, unsupervised learning, and reinforcement learning.

5.Data Visualization: This course covers the principles and techniques of data visualization, including static and interactive visualizations, design principles, and best practices.

Specialization

1.Business Analytics: This specialization focuses on the application of data analytics techniques to solve business problems. Students will learn how to use data analytics to support decision-making, optimize business processes, and identify new business opportunities.

2.Health Analytics: This specialization focuses on the application of data analytics techniques to the healthcare industry. Students will learn how to use data analytics to improve patient outcomes, manage healthcare costs, and develop new medical treatments.

3.Marketing Analytics: This specialization focuses on the application of data analytics techniques to the marketing industry. Students will learn how to use data analytics to develop effective marketing strategies, optimize marketing campaigns, and measure marketing performance.

4.Financial Analytics: This specialization focuses on the application of data analytics techniques to the finance industry. Students will learn how to use data analytics to manage financial risk, identify investment opportunities, and improve financial performance.

5.Social Media Analytics: This specialization focuses on the application of data analytics techniques to social media data. Students will learn how to use data analytics to analyze social media data, identify trends and patterns, and develop social media strategies.