Master in Business Analytics & 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

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

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

This course focuses on the management of organizations. It will examine the skills and functions needed by managers to run efficient and effective organizations. Themes such as motivation, communication, leadership and strategy will be covered.

3 CH | 4 ECTS

This course addresses the modern management process including decision-making and planning, motivating others and efficient communication. This course more specifically focuses on exploring the rationale of organizational design and structure to achieve firm objectives; positive employer-employee relations; managerial ethics and the use of control systems.

1 CH | 2 ECTS

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 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

This course establishes a framework for developing and maintaining new product development capabilities in the corporate world. It allows the practical development of new products and defines the conditions under which they can be successfully launched. The course is designed to integrate the various elements of the program within a practical problem-solving environment. Students are expected to read extensively, complete all assignments, participate in discussions and cooperate in the identification and application of their learning to new situations.

3 CH | 4 ECTS

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

Organizations are increasingly aware of the importance of intellectual and human capital. Leadership and trust are two drivers of human capital development. Trust is at the heart of organizational and personal issues and rests on rational and emotional foundations and on contractual and affective exchanges. To become a leader, you have to know yourself, be aware of your strengths and weaknesses and understand others. This course focuses on effective communication, support, teamwork, leadership and team-building.

1 CH | 2 ECTS

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

This course explains blockchain technology and its impact on four economic sectors: Fintech, healthcare, logistics, and energy. It explores how this technology can address some of the most pressing problems in society such as trust, transparency, human rights, inclusive participation and fair trade. Students will complete a project applying blockchain technology to solve a problem in an economic sector of their choice or create a new venture that produces breakthrough innovation. Students will also gain deeper insight into the general-purpose technologies that will shape the 21st century and provide a blueprint for the value-based economy.

3 CH | 4 ECTS

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

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

The Online Reputation Management seminar focuses on all relevant aspects of public digital image management. The seminar discusses online corporate reputation management in the era of social networks, user reviews and instant communication.

1 CH | 2 ECTS