Program Overview
Data Analytics in Accounting and Finance
Program Summary
- Languages: English
- Duration: 18 months
- Mode of Study: On Campus
- ECTS: 90
Program Description
The MSc Data Analytics in Accounting and Finance at EUC aims to train students in key concepts and theories in Accounting, Finance, and Data Analytics. The program equips students with advanced skills, scientific methodologies, and analytical tools to meet the challenges of a fast-changing environment where vast amounts of data are available.
Career Opportunities
Our graduates work in:
- Corporate Finance
- Business Consultancy
- Banking
- Accountancy
- Management
Admission Process
Our dedicated Admissions Team will guide you every step of the way by answering all of your questions, helping you choose a program of study, and starting your EUC experience with a virtual or actual campus tour.
Scholarships and Financial Aid
Our Scholarship and Financial Aid program aims to provide prospective and enrolled students with information and resources to financially support your educational goals. This takes the form of academic scholarships, athletics scholarships, and family
eed-based financial aid.
Student Housing
Our student accommodation units are located only minutes from campus and are designed to offer students high-quality living spaces in elegant studios and one-bedroom flats supported by great social spaces, in-house gym, laundry rooms, and 24–hour security service.
Exemptions from ACCA
The degree also earns you up to 7 exemptions for ACCA professional accounting qualification, which is highly valued for a career in financial institutions and accountancy firms.
Program Requirements
- Degree Requirements: ECTS
- Compulsory Courses: 60
- Elective Courses: 30
- Total Requirements: 90
Compulsory Courses
- AEM610: Fundamentals of Finance (10 ECTS)
- AEM630: Advanced International Financial Reporting (10 ECTS)
- AEM640: Quantitative Methods in Accounting and Finance (10 ECTS)
- AEM650: Analytics in Accounting and Finance (10 ECTS)
- AEM660: Computational Finance (10 ECTS)
- AEM670: Applied Financial Analysis and Valuation (10 ECTS)
Elective Courses
- AEM600: Financial and Managerial Accounting (10 ECTS)
- AEM620: Principles of Programming for Python (10 ECTS)
- AEM625: Principles of Programming for R (10 ECTS)
- AEM675: Principles of Machine Learning (10 ECTS)
- AEM680: Principles of Artificial Intelligence (10 ECTS)
- MBC630: Ethics and Corporate Social Responsibility (10 ECTS)
- MBC650: Investment and Risk Management (10 ECTS)
- N/A: One Elective course from another relevant Master Program of the University. Only one (1) course of this provision may be selected by the student (of at least same number of ECTS) throughout the completion of the program requirements, with the program coordinator’s approval.
Teaching Personnel
Click here to view the teaching personnel.
Accreditation
European University Cyprus has been ranked with an overall assessment of 5-Stars by QS TOP UNIVERSITIES (QS Stars). The QS Stars University Rating evaluates institutions globally on 50 different indicators and awards universities between one and five stars in a range of fields. EUC has received five stars for performance in the areas of teaching, employability, internationalization, online learning, inclusiveness, and the MD Medicine for the competitiveness and strength of the program. Special Admissions Criteria apply.
Program Outline
Degree Overview:
Overview:
The MSc Data Analytics in Accounting and Finance program at European University Cyprus (EUC) equips students with advanced skills, scientific methodologies, and analytical tools to navigate the challenges of an ever-changing business environment characterized by vast amounts of data. As digital technologies gain widespread adoption in the accounting and finance industry, it becomes imperative to equip students with the technological proficiency and knowledge necessary to harness big data for decision-making in contemporary and future business landscapes.
Objectives:
- Provide a comprehensive understanding of key concepts and theories in accounting, finance, and data analytics.
- Develop proficiency in advanced data analytics techniques and their application in accounting and finance.
- Foster critical thinking, problem-solving, and analytical skills to enable students to make informed decisions based on data-driven insights.
- Prepare students for leadership roles in the accounting and finance industry, where data analytics plays a pivotal role.
Program Description:
The MSc Data Analytics in Accounting and Finance curriculum combines core courses in accounting, finance, and data analytics with elective courses that allow students to specialize in specific areas of interest. The program's curriculum is designed to provide students with a solid foundation in the theoretical and practical aspects of data analytics in accounting and finance, enabling them to excel in complex analytical roles and pursue further academic qualifications.
Outline:
Program Content:
The program covers a wide range of topics, including:
- Business and Technology
- Management Accounting
- Financial Accounting
- Corporate and Business Law
- Financial Reporting and Performance Management
- Quantitative Methods in Accounting and Finance
- Analytics in Accounting and Finance
- Computational Finance
- Applied Financial Analysis and Valuation
- Financial and Managerial Accounting
- Principles of Programming for Python
- Principles of Programming for R
- Principles of Machine Learning
- Principles of Artificial Intelligence
- Ethics and Corporate Social Responsibility
- Investment and Risk Management
Structure:
The program consists of 90 ECTS credits, which are typically completed over 18 months of full-time study. The program is divided into two semesters, with each semester comprising 15 weeks of instruction.
Course Schedule:
The course schedule is designed to provide students with a balanced and comprehensive learning experience. Core courses are offered in the first semester, while elective courses are offered in the second semester. Students are required to complete all core courses and three elective courses to graduate.
Modules:
Core Courses:
- Fundamentals of Finance: Provides a comprehensive overview of the principles and practices of finance, including financial markets, investment analysis, and corporate finance.
- Advanced International Financial Reporting: Focuses on the international financial reporting standards (IFRS) and their application in the preparation of financial statements.
- Quantitative Methods in Accounting and Finance: Covers statistical and mathematical techniques used in accounting and finance, including regression analysis, time series analysis, and forecasting.
- Analytics in Accounting and Finance: Introduces students to the application of data analytics techniques in accounting and finance, including data mining, predictive modeling, and fraud detection.
- Computational Finance: Explores the use of computational methods in finance, including numerical methods, optimization techniques, and Monte Carlo simulation.
- Applied Financial Analysis and Valuation: Provides students with the skills and knowledge necessary to analyze and value financial assets, including stocks, bonds, and derivatives.
Elective Courses:
Students can choose three elective courses from the following list:
- Financial and Managerial Accounting: Focuses on the principles and practices of financial and managerial accounting, including budgeting, cost accounting, and performance measurement.
- Principles of Programming for Python: Introduces students to the Python programming language and its applications in data analytics, including data manipulation, visualization, and machine learning.
- Principles of Programming for R: Introduces students to the R programming language and its applications in data analytics, including data analysis, visualization, and statistical modeling.
- Principles of Machine Learning: Provides students with a foundational understanding of machine learning algorithms and their applications in data analytics, including supervised learning, unsupervised learning, and ensemble methods.
- Principles of Artificial Intelligence: Explores the fundamental concepts and techniques of artificial intelligence, including natural language processing, computer vision, and robotics.
- Ethics and Corporate Social Responsibility: Examines the ethical and social implications of data analytics in accounting and finance, including data privacy, data security, and algorithmic bias.
- Investment and Risk Management: Provides students with the knowledge and skills necessary to make informed investment decisions and manage financial risk, including portfolio management, risk assessment, and hedging strategies.
Assessment:
Assessment Methods:
- Exams: Written examinations are used to assess students' understanding of the theoretical concepts and principles covered in the courses.
- Assignments: Students are required to complete assignments that demonstrate their ability to apply the knowledge and skills acquired in the courses to practical situations.
- Projects: Students may be required to complete projects that involve the application of data analytics techniques to real-world problems.
- Presentations: Students may be required to give presentations on their research or project work.
Assessment Criteria:
- Knowledge and understanding: Students are assessed on their ability to demonstrate a comprehensive understanding of the course material.
- Analytical skills: Students are assessed on their ability to analyze data, identify patterns, and draw meaningful conclusions.
- Problem-solving skills: Students are assessed on their ability to solve problems using data analytics techniques.
- Communication skills: Students are assessed on their ability to communicate their findings effectively in written and oral form.
Teaching:
Teaching Methods:
- Lectures: Lectures are used to introduce new concepts and theories and provide students with a comprehensive overview of the course material.
- Tutorials: Tutorials provide students with an opportunity to discuss the course material in more depth and receive personalized feedback from instructors.
- Practical sessions: Practical sessions allow students to apply the knowledge and skills acquired in the lectures and tutorials to practical exercises and case studies.
- Guest lectures: Guest lectures from industry professionals provide students with insights into the practical applications of data analytics in accounting and finance.
Faculty:
The program is taught by a team of experienced and qualified faculty members with expertise in accounting, finance, and data analytics. The faculty is committed to providing students with a high-quality learning experience and supporting their academic and professional development.
Unique Approaches:
- The program combines theoretical knowledge with practical applications, ensuring that students are equipped with the skills and knowledge necessary to succeed in the data-driven business environment.
- The program provides students with access to state-of-the-art facilities, including a dedicated data analytics laboratory.
- The program offers a variety of opportunities for students to engage in research and professional development activities.
Careers:
Potential Career Paths:
Graduates of the MSc Data Analytics in Accounting and Finance program are well-positioned for a wide range of career paths in the accounting and finance industry, including:
- Data Analyst
- Financial Analyst
- Risk Analyst
- Auditor
- Management Consultant
- Corporate Finance Analyst
- Investment Analyst
- Data Scientist
Opportunities:
The program prepares students for leadership roles in the accounting and finance industry, where data analytics is becoming increasingly important. Graduates can expect to find employment opportunities in a variety of sectors, including banking, financial services, consulting, and technology.
Outcomes:
Graduates of the program have gone on to successful careers in the accounting and finance industry. Many graduates have obtained positions in leading companies such as Deloitte, PwC, KPMG, EY, and Citigroup.
