Eligibility Requirements for Dublin Business School MSc in Data Analytics

A minimum Level 8 Honours Degree with 55% or higher in a cognate discipline from a recognised third level institution or equivalent.

55/100

Bachelor's GPA Requirement

3 years degree

UG Requirement

Dublin Business School MSc in Data Analytics Class Profile

2.2

Avg. Bachelors GPA / Percentage

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 visualisation 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 visualisation that form the modern AI ecosystem).

The programme 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, graph technology considered to be effective means to empower the development of sophisticated AI applications. Additionally, the programme learning outcomes focus on the learner’s ability to meet requirements and deliver against business intelligence goals of organisations they will work in and allow for all of these outcomes to be demonstrated from an academic perspective but also to have a portfolio piece of work that can be shared with current or prospective employers.

Either full time or part time, the programme is designed to facilitate learners with computer science/data science/ technology/general science/ mathematics/statistics or related background who wish to upskill in this new and emerging area of Data Analytics. It will also be of interest to learners who have completed their undergraduate degree and wish to specialise in this area.

Dublin Business School MSc in Data Analytics Program Curriculum

1.Big Data Analytics: This course would cover the analysis of large data sets, including data warehousing, data mining, and data visualization.

2.Machine Learning: This course would cover the fundamentals of machine learning, including supervised and unsupervised learning, decision trees, neural networks, and deep learning.

3.Data Visualization: This course would cover the principles of data visualization, including the design and implementation of effective visualizations to communicate complex data sets.

4.Business Intelligence: This course would cover the use of data analytics to support business decision-making, including data-driven decision-making, data warehousing, and business analytics.

5.Data Mining: This course would cover the extraction of useful information from large data sets, including association rule mining, classification, and clustering.

Electives

1.Big Data Analytics: This course would cover the analysis of large data sets, including data warehousing, data mining, and data visualization.

2.Machine Learning: This course would cover the fundamentals of machine learning, including supervised and unsupervised learning, decision trees, neural networks, and deep learning.

3.Data Visualization: This course would cover the principles of data visualization, including the design and implementation of effective visualizations to communicate complex data sets.

4.Business Intelligence: This course would cover the use of data analytics to support business decision-making, including data-driven decision-making, data warehousing, and business analytics.

5.Data Mining: This course would cover the extraction of useful information from large data sets, including association rule mining, classification, and clustering.

1.Data Analytics Fundamentals: This course would cover the fundamentals of data analytics, including data collection, data cleansing, data transformation, and data analysis.

2.Data Warehousing and Business Intelligence: This course would cover the concepts and technologies used in data warehousing, including data modeling, ETL processes, and OLAP.

3.Statistics for Data Analytics: This course would cover the fundamentals of statistical analysis, including descriptive statistics, probability distributions, hypothesis testing, and regression analysis.

4.Programming for Data Analytics: This course would cover the fundamentals of programming for data analytics, including scripting languages, SQL, and data manipulation with programming languages like Python, R, and Java.

5.Machine Learning: This course would cover the principles and applications of machine learning, including supervised and unsupervised learning, decision trees, neural networks, and deep learning.

1.Business Analytics: This specialization would focus on the use of data analytics to support business decision-making, including data-driven decision-making, data warehousing, and business analytics.

2.Big Data Analytics: This specialization would focus on the analysis of large data sets, including data warehousing, data mining, and data visualization, with an emphasis on big data technologies.

3.Machine Learning and Artificial Intelligence: This specialization would focus on the application of machine learning and artificial intelligence techniques to data analytics problems, including supervised and unsupervised learning, decision trees, neural networks, and deep learning.

4.Data Visualization and Communication: This specialization would focus on the principles and techniques of data visualization, including the design and implementation of effective visualizations to communicate complex data sets.

5.Predictive Analytics: This specialization would focus on the use of statistical models and machine learning techniques to predict future outcomes based on historical data.

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Dublin Business School MSc in Data Analytics Employment

Companies Recruiting Dublin Business School MSc in Data Analytics Graduates

Amazon
Microsoft
Google
IBM
Accenture
Deloitte
KPMG
PwC
EY
McKinsey & Company

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