- Duration: Two academic courses.
- Start: October 2020
- End: February 2022
- Workload: 120 credits
- Format Part time.
- Program cost: 9.500 eur
Master in Business Analytics: Opportunities in the field of business analytics
The ESN Master in Business Analytics program is designed for both recent graduates and professionals, interested in gaining a competitive advantage through the predictive potential of data.
The ESN Master in Business Analytics is a program designed to provide the tools and techniques to become an expert in this rapidly evolving and high-demand field.
It’s not just collecting data, you have to maximize its value
Our Master in Business Analytics program allows you to learn from top-level managers and professionals the latest trends in data management and analysis.
The ESN Master in Business Analytics will provide you with the technical and quantitative experience, plus the strategic mindset necessary to support data-driven business decision-making.
With the increase in data available through new technologies, many industries, like banking, health, retail and e-commerce, recognize the importance of analytics and increasingly hire professionals specialized in data analysis, with the aim of creating value and promoting decision-making.
Global Big Data and Business Analytics revenues expected to increase rapidly, until reaching 274.3 billion dollars in 2022 (Source: IDC). This will inevitably lead to accelerated market growth and an increase in demand for qualified professionals in the area of data analytics.
- Business and management consultants.
- Business Analysts.
- Data Analysts.
- Business Intelligence Analysts.
- Metrics and analytics specialists.
- Operational research consultants.
- Solution architects.
Objectives of the Master in Business Analytics
- Build data-driven organizations. Train participants to become qualified professionals capable of managing, analyze and use data in making strategic, tactical and operational decisions under uncertainty.
- Master advanced analytics for decision making. Strong understanding of the mathematics and statistics on which advanced analytical methods are based. You will be able to collect, manage and make sense of large data sets using tools for data analysis and visualization.
- Understand the business context. Develop a deep understanding of how a company works, including operations management, supply chain management, and marketing.
- Gain problem solving skills. Learn to identify problems and drive change within an organization.
- Gain communication skills, both oral and written. Over the course of the program, you will make several presentations and write reports to integrate analysis into business strategy.
- Gain teamwork skills. You will work in groups and establish global connections that will enrich your experience.
- Gain leadership skills. Prepare participants to achieve an efficient leadership role within the digital transformation, with the aim of creating value for their organizations.
- Know the ethical and social responsibility implications. You will assess the risks and implications of your recommendations, adopting a strategic approach to social challenges.
During the course of the program, you will gain the necessary technical knowledge to analyze different types of data sets effectively. You will acquire unmatched analytical and business management skills for decision making.
- Business Analytics
- Predictive Analytics
- Data science programming with R
- Data science programming with Python
- Probability and statistics
- Data visualization
- Data manipulation
- Data base SQL
- Data base NoSQL
- Machine Learning
- Deep Learning
- Deep Reinforcement Learning
- Artificial Intelligence
- Natural Language Processing
- Customer Analytics
- Marketing Analytics
- Operations Analytics
- People Analytics
- Accounting Analytics
- Leadership skills
- Negotiation and persuasion skills
- Design Thinking
- Value proposition
The professors of the program are active professionals in the Data Analytics sector. Coming from the business world and members of the Barcelona Data Institute, they have created, formed and managed data teams in all kinds of organizations: from successful startups to notable multinationals.
Program director: José Luis Morales.
Teachers and speakers
- Ramon Morote. Chief Data Officer. Naturgy (Gas Natural).
- Ángel J. Narciso. Senior Business Analyst. Academic Coordinator in Data.Barcelona.
- Pablo Monleón. Senior Data Analyst. CaixaBank.
- Enric Barba. President of the Innovation and Technology Club. CECOT.
- Carmen Herrero. Lead Data Scientist. Mango.
- Rubén Masó. Chief Data Officer. Grupo Panstar.
- Francisco Ortega. Data Miner. Arquia Banca.
- Arnau Muns. Statistical Forecast – Supply Chain – Global Analytics Center of Excellence. Sanofi.
- David Olmo. Data Scientist. Data Engineer & Python Developer. Raw Data.
- Maria Miklosik. Head of Corporate Business Process Management. GFT Technologies.
- Eduard Blasi. Vice president 3º. Spanish Professional Privacy Association (APEP).
- Amanda Figueras. Consultant of data management and visualization indicator systems. Visirius.
- Antxon Pous. CIO. Skeyon Lab.
- Experiential learning: Practical analysis of analytical case studies, collaboration with companies on real projects, participation in data analysis teams.
- Analytical consulting practices: Possibility of doing paid business internships related to consulting projects in data analysis, that will pose a real-world analytical challenge to complement the training received.
- Introduction to Data Analysis: Use of statistical tools and visualizations to present, analyze, and interpret data. Emphasis is placed on the applications of statistical tools and their uses for organizational decision-making. Development of Excel skills to manipulate, analyze and visualize data in a spreadsheet.
- SQL for data analysis: Use of structured query language (SQL) to extract and analyze data stored in databases. SQL is an essential skill for any data professional in their daily work.
- Data Visualization – Power BI – Qlik – Data Studio: Design and visualization principles for creating impactful data visualizations, building scorecards, and telling stories with data.
- Python Programming for Data Science: Python has quickly become the reference language in the space of data analysis.
- Information management in the company. Management information and control. The data-driven organization: The aim of these three modules is to understand the information cycle in the company, the types of decisions that accompany the data in the dynamic context of the company, and the characteristics of a data-driven or data-driven organization.
- Marketing Analytics: Marketing Analytics capabilities are becoming increasingly important as a core strategy for driving business performance. Quantitative marketing technical skills are provided for immediate implementation.
- Customer Relationship Management (CRM) helps companies successfully implement strategies, practices, and technologies to profitably and retain customers profitably. Proper transaction data management helps build strong long-term customer relationships.
- Construction of Business Performance Indicators (KPIs): The main goal is for each participant to build business performance indicators from a multidimensional perspective, and working on the ability to interpret results to link them to business goals.
- Key Objectives and Outcomes (OKRs): Work on the complementarity of OKRs with respect to KPIs. It will explain what OKRs are, what business perspective they provide in terms of indicators, and how they are implemented in a business organization.
- Strategic Tools: SWOT Analysis: SWOT analysis is a strategic planning tool that can reveal a wealth of information: it helps identify one’s own strengths and weaknesses, as well as any threats or opportunities that may exist in a particular business situation.
- Skills 2.0 – LinkedIn: Expertly updating the resume on the leading professional social network, helps to effectively promote skills, so that they have an impact on companies and recruiters.
- Skills 2.0 – Trello: Use Trello to manage personal tasks, team-based projects, and business operations.
- Skills 2.0 – Productivity: Use of digital tools that contribute to the productivity of the company, to make it more competitive.
- Entrepreneurship – Who is our client?
- Data-Based Market Segmentation Analysis: Data-based market segmentation analysis, developing skills that meet customer needs and gain a competitive advantage.
- Data Analysis: Develop data analysis and business modeling skills. Gain the ability to apply statistics and data analysis tools to various business applications.
- Predictive analytics: Learn how to use predictive modeling and its applications to maximize the effectiveness of business actions (such as marketing actions) and drive business revenue.
- Python Programming for Data Science II: Learn how to analyze data with Python. Prepare data for analysis, perform statistical analysis, create meaningful visualizations, predict future trends from data, and more.
- Data visualization: Visualization of business data and creation of powerful Business Intelligence reports. Creating high-impact data analysis visualizations and dashboards to help you see and understand your data. apply predictive analytics to improve business decision making.
- Advanced SQL: As data collection has increased exponentially, so has the demand for professionals trained to use and interact with data, and provide knowledge to make better decisions and optimize business management. Emphasis will be placed on hands-on learning.
- Data management and governance. Data projects. Methodology and discovery of use cases: The aim of these three modules is to understand what data governance is, why it is important in any organization, its disciplines and contents. Know how to apply it to an organization. Understand the basics of a data project and its keys.
- Structured thinking and communication for data analytics professionals: Structured thinking and communication are one of the most important skills for data analytics professionals today.
- Decision trees: Decision trees are one of the most widely used techniques in all data-driven businesses. Not only can it help us with prediction and classification, but it is also a very effective tool for understanding the behavior of various variables.
- Accounting Analytics – Financial analytics: Using financial information to make decisions within an organization. Topics that are most useful for business analytics are covered.
- Business Process Management (BPM): Create and manage activities (processes) of a company or organization. Thanks to BPM, companies can easily automate the tasks of any department, gaining flexibility, efficiency and competitiveness, and increasing their productivity.
- Strategic Tools: Strategy Canvas: Strategy Canvas helps to explore and research the current market space, to build our own Blue Ocean, where our competitors will never be able to reach and our dominance will be lasting.
- Competitive Strategy: Blue Ocean Strategy: The Blue Ocean Strategy is aimed at developing innovative market creation schemes.
- Skills 2.0 – Data Analysis Interviews: Despite knowing the tools and techniques of data analysis, it can be difficult to complete a job interview. You need to show problem solving skills and technical skill.
- Entrepreneurship – What can we do for our client?
Semesters 3 and 4
- Data engineering: Data modeling. Cloud Datamining. Spark. Data Lakes.
- Data Mining: Learn data mining techniques, both for structured data that fit a clearly defined scheme, and for unstructured data that exist in the form of text in natural language.
- Machine Learning: Machine Learning is restructuring and revolutionizing the world, and causing disruption in all industries and professions worldwide. It’s no longer just a buzzword: many different industries have already discovered the benefits of business process automation and the disruptions of machine learning. We will review the tools and techniques needed to apply machine learning, or Machine Learning, to solve business problems.
- Demand Prediction in Retail Using Machine Learning: The goal is to build an end-to-end machine learning model into a real-world dataset, starting with turning a business goal into a machine learning problem, to build a complete Machine Learning model. The business problem addressed as part of the course is that of demand forecasting.
- Computer Vision: Computer vision systems handle a wide variety and volume of data, specifically images or videos. The goal is to provide an idea of how the underlying techniques work in today’s state-of-the-art computer vision systems, and to guide you through some of the major applications in a practical way.
- Neural Networks: There has been a great boom in the applications of artificial vision and natural language processing today. It provides an idea of how neural networks work, which are the basic components behind any natural language processing application or artificial vision.
- Deep Learning: Our smartphone, our smartwatch, and our car (if it’s a recent model) use artificial intelligence technology. In the near future, Deep Learning technologies with the ability to “self-learn” will be used in almost every aspect of our business and industry. You will learn how to develop business strategies to plan new services and products based on Deep Learning.
- Artificial Intelligence: Artificial Intelligence (AI) is rapidly penetrating all industries, and has a profound impact on virtually every aspect of our lives. The aim is to understand AI, its impact and transformative potential for business and society.
- Customer Analytics: The interaction of the company with its customers and how the data can be used to improve these interactions. Focus and personalization are the central concepts of modern customer-centric marketing. Tools and methods are provided that will allow the data to be leveraged to help shape the relationship with the customer.
- People Analytics: Exploration of the main techniques used to recruit and retain the best professionals. They will explore the cutting-edge techniques used to recruit and retain great people, and demonstrate how these techniques are used to cut cutting-edge companies. They will explain how data and sophisticated analysis apply to people-related issues, such as recruitment, performance appraisal, leadership, hiring and promotion, job design, compensation, and collaboration.
- Operations Analytics: How to model the uncertainties of future demand, how to predict the results of the election of competing strategies, and how to choose the best course of action in the face of risk.
- Organizational Strategy: Porter Five Forces – Goal: Find the competitive advantage by applying Porter’s five forces.
- Product Strategy: Product Life Cycle: Product life cycle management is the art and science of strategically managing product development, manufacturing, sales and marketing, according to the various stages that the product experiences throughout its life.
- Product Strategy: Pricing Strategies: What factors should be considered when pricing a product or service? What strategies should be considered to increase sales and be more profitable?
- Skills 2.0 – Agile: Small, agile companies are increasingly changing the rules of the game in entire industries, and are disrupting traditional business areas as well as common management practices. Agile methods have proven to be very useful. It is important to know them, master them and turn to them in the right situation.
- Skills 2.0 – Project management: Knowing how to manage and administer the resources for the realization of a project is vital for the result to be satisfactory.