Master in Business Analytics & Data Science

EU Business School Barcelona, Spain Ranked #42 in Europe

master-in-business-analytics-data-science

Next enrollment cycle

October 2021

See all cycles

Total Course Fee

USD 16,560

Course Accredited By

IACBE

  • 1 Years
  • On Campus
  • Postgraduate
  • Degree

Technology advances have dramatically changed the way businesses function, and expertise in business analytics and data science is essential to leverage technology for optimal results. Applying a hands-on approach, this program gives students a comprehensive foundation in data science, using industry-leading software, tools and applications.

The admission process at EU Business School is clear and straightforward. Applicants must fulfil specific academic and English language requirements to be eligible for admittance into their chosen program.

Prospective students are evaluated on the strength of their applications as a whole. Consideration is given to the student’s qualities and abilities: leadership potential, academic achievements, performance on standardized tests, extracurricular activities and personal experiences. EU's Admissions Services department will assist all applicants throughout the entire application process.

Enrollment Cycles

  • October 2021
  • January 2022
  • March 2022

Admission Requirements:*

  • 1 certified copy of bachelor's degree and transcripts
  • Proof of English level: TOEFL score 89 (internet-based), 233 (computer-based); IELTS 6.5; CAE C1 with a minimum score of 176; PTE score 59; English native or equivalent

Applicants must also meet one of the following:

  • A GPA of 3.0 on a 4.0 scale
  • A satisfactory score on the GMAT or GRE
  • An interview with the academic dean

* Students who do not meet the criteria will have an interview with the admission committee and will be considered on a merit basis. For more information, please contact the admissions department of your chosen campus.

All applicants should submit the following documents in order to complete the application process:

  • 1 completed application form (if the application was not filled in online)
  • 1 certified copy of bachelor's degree and transcripts*
  • Proof of English level: TOEFL score 89 (internet-based), 233 (computer-based); IELTS 6.5; CAE C1 with a minimum score of 176; PTE score 59; English native or equivalent
  • 1 copy of CV/résumé
  • 2 letters of recommendation**
  • 1 written or video essay***
  • An electronic passport photo or 3 printed passport-size photos
  • 1 copy of valid passport or ID card
  • 1 bank certificate or letter certifying the applicant's financial solvency
  • €/CHF 200 non-refundable application fee. Please attach a money order, check or receipt for a bank transfer payable to EU

YEAR 1

TERM 1

(13 CH | 18 ECTS)

Introduction to Big Data & Data Science

Data is truly everywhere. Digitization, computing and the Internet have revolutionized the accumulation, volume and use of data. Today we can collect, store and preserve more data than ever before. Working with these large amounts of dynamic and unstructured data requires a whole new set of skills and technologies. This course introduces students to the landscape of big data, data science, machine learning and statistics and how they can be used together to derive business value from data.

3 CH | 4 ECTS

The Data Science Toolkit

This hands-on course introduces students to the main tools used in data science, including Python and the Jupyter ecosystem. Students will understand how to create a virtual environment and install libraries for different data science projects and identify the main advantages of Python and R as tools for data science. They will also complete an end-to-end data analysis project using the Pandas library.

3 CH | 4 ECTS

The Big Data Toolkit

Students will learn about the tools available for dealing specifically with big data. Students will gain hands-on experience with the different models available for big data, including SQL databases, NoSQL databases, Hadoop ecosystem and Apache Spark. Emphasis will be placed on the different business problems that each model helps us solve, and their advantages and shortcomings will be assessed.

3 CH | 4 ECTS

Data Security & Privacy

In today’s regulatory landscape, data security and privacy are at the forefront of every enterprise. Students will understand the principle legal structures governing how organizations can collect, store and handle their data; understand the main threats in the cybersecurity landscape; and become equipped with the right set of tools and knowledge for handling them. Real-world examples illustrate the complex nature of ethical and social issues underlying the technology industry and students will understand the best strategies for success from the perspective of security, privacy and ethics.

3 CH | 4 ECTS

Masterclass I

This masterclass, the first of three held throughout the year, will be dedicated to the acquisition of specific practical skills relating to big data and data science.

1 CH | 2 ECTS

TERM 2

(13 CH | 18 ECTS)

Machine Learning

This course will introduce students to the fundamental concepts in machine learning starting from the very basics of a data model and what value can be derived from using machine learning algorithms. Students will learn about the two main types of classical machine learning algorithms: supervised and unsupervised and gain hands-on experience with using these models on data sets using the Python programming language and the corresponding libraries. Emphasis will be placed on choosing the correct machine learning algorithms for a given data set and application. Students will also learn the different metrics used to evaluate the performance of a machine learning algorithm.

3 CH | 4 ECTS

Deep Learning & AI

Deep learning is one of the most sought-after skill sets in the data world and it has made an extraordinary contribution across several industries over the last few years. This course aims to give an overview of the deep learning landscape and establish how deep learning differs from classical machine learning. Students will learn which problems are particularly suited to deep learning and gain hands-on experience with the deep learning toolset in Python.

3 CH | 4 ECTS

Data Visualization & Communication

This course provides learners with the essential knowledge and skills to understand the concept of information visualization with an emphasis on its importance in a business setting. The course will introduce an overview of how data can be encoded visually and how information can be communicated efficiently.

3 CH | 4 ECTS

Data Visualization Lab

This course provides learners with the practical skills to apply the concept of information visualization in a business setting. Students will gain hand-on experience with advanced web-based applications and understand their key role in the data visualization process. This course will be delivered in the format of a lab, in which students will work on creating interactive web-based visualizations.

3 CH | 4 ECTS

Masterclass II

This masterclass, the second of three held throughout the year, will be dedicated to the acquisition of specific practical skills relating to machine learning and AI.

1 CH | 2 ECTS

TERM 3

(13 CH | 18 ECTS)

Business Intelligence

This course introduces students to the main concepts of business intelligence and how they can support decision-making across a wide range of business sectors. Students will become familiar with the main business intelligence tools and applications including data management systems and data warehouses. Hands-on projects will teach students effective business reporting and how to create various visualizations and dashboards.

3 CH | 4 ECTS

Management Information Systems & ERP

This course focuses on the role of information systems in organizations. Students are provided with an overview of information systems and their main components. Through real life examples, students will come to appreciate why information systems are so essential in businesses today and how they can help businesses derive value from data. This course provides a more in-depth view of enterprise resource planning with a focus on supply chain management, knowledge management systems and customer relationship management systems.

3 CH | 4 ECTS

Business Analytics

This course introduces students to the main concepts of business analytics, with an emphasis on the specific applications of these concepts as well as how to implement them in a business environment. Students will learn how to make informed data analysis decisions; draw conclusions from data; examine the underlying statistical principles in business analytics; and apply predictive algorithms in business analytics frameworks.

3 CH | 4 ECTS

Data-Driven Management

This course will provide the theoretical framework for applying formal asset management techniques to the treatment of data. Students will learn how to identify data assets, measure their quality and derive their business value. Specifically, they will learn how to create an effective framework for evaluating the business value of different data assets; become familiar with data standards for quality; assess different techniques to measure data quality and value; understand how to harness data across a large organization; develop the skills to make data-driven decisions; and leverage analytic toolkits to address different business opportunities.

3 CH | 4 ECTS

Masterclass III

This masterclass, the third and final of three held throughout the year, will be dedicated to the acquisition of specific practical skills relating to data science for business.

1 CH | 2 ECTS

GRADUATION (Requirements)

Final Project

Students will also be required to submit a final project (6 ECTS/4CH) at the end of their studies and to attend field trips, company visits and fairs as part of the experiential learning method.

4 CH | 6 ECTS

THREE TERMS/ONE YEAR

Fees per term €4,600

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