Degree in Business Intelligence and Analytics

  • Students must have developed the learning skills needed to undertake further study with a high degree of autonomy.
  • Know the tools to plan, manage, implement and evaluate production systems and operations.
  • Know different production problems and their relationship with other company processes.
  • Know the different financing tools and be able to assess the interaction between the investment and financing decisions of the company.
  • Classify the different types of information according to their legal nature and evaluate the legal risks and responsibilities of the data protection delegate and other actors in charge of database management.
  • Know the basic legal and ethical framework for conducting activities involving the processing of information, personal data and macrodata, as well as for e-commerce and e-contracting.
  • Evaluate the internal control system within the framework of accounting information systems.
  • Know the basic concepts of logic, algorithmics, computational complexity and their application to business intelligence.
  • Identify customer value in the digital environment.
  • Identify customer behaviour in the digital environment.
  • Make marketing mix decisions in the digital environment.
  • Make strategic marketing decisions in digital environments.
  • Apply market research techniques to digital environments.
  • Analyse the customer's digital information and brands.
  • Identify customer marketing information in the digital environment.
  • Understand the keys to the operation of the market and the effects of its different structures through studies based on the collection and analysis of data.
  • Know the principles of economic analysis and its application to the diagnosis and resolution of problems based on data analysis.
  • Extract internal and external information and use it to estimate the parameters that define productive investments.
  • Relate, using supervised and unsupervised algorithms, the different elements that interact in the decisions of individuals.
  • Understand the systemic nature of the digital company.
  • Understand and evaluate the characteristics and usefulness of the different corporate and competitive strategies of digital companies.
  • Set goals and design strategies in digital companies taking account of the implications and needs deriving from them.
  • Reach strategic diagnoses in complex and uncertain environments using appropriate methodologies.
  • Make decisions under certainty and uncertainty.
  • Express situations of uncertainty and randomness using mathematical, synthetic and graphic languages.
  • Apply methods and techniques of analysis, synthesis and graphical representation by means of software tools.
  • Establish a system of business management indicators.
  • Design and implement cost allocation models based on the digital records of accounting information systems.
  • Use analytical and quantitative methods to analyse and interpret the financial statements of organisations.
  • Evaluate the economic and financial consequences of recording operations in information systems.
  • Implement the accounting cycle in digital accounting records and prepare financial information from those records.
  • Be able to work in a team demonstrating commitment to quality, ethics, equality and social responsibility.
  • Be able to define, solve and present complex problems systemically.
  • Be able to use ICT, both in academia and in professional practice.
  • Be able to learn autonomously.
  • Be able to analyse and search for information from diverse sources.
  • Demonstrate skills for analysis and synthesis.
  • Understand the impact of economic, political-legal, socio-cultural, technological and environmental variables on business activity.
  • Be able to plan, organise, monitor and evaluate the implementation of business strategies.
  • Be able to apply analytical and mathematical methods for the analysis of economic and business problems.
  • Know and know how to properly use the appropriate quantitative and qualitative methods to reason analytically, evaluate results and predict economic and financial magnitudes.
  • Be able to make autonomous decisions in digital environments characterised by the abundance and dynamism of data.
  • Be able to access and manage information in different formats for subsequent analysis in order to obtain knowledge through data.
  • Be able to produce models, calculations and reports, and to plan tasks in the specific field of business intelligence and analytics.
  • Be able to solve problems and to communicate and spread knowledge, skills and abilities, taking account of the ethical, egalitarian and professional responsibility of the activity of business intelligence and analytics.
  • Acquire basic training that can be used to learn new methods and technologies and to adapt to new situations in academic and professional areas.
  • Students must have acquired knowledge and understanding in a specific field of study, on the basis of general secondary education and at a level that includes mainly knowledge drawn from advanced textbooks, but also some cutting-edge knowledge in their field of study.
  • Students must be able to apply their knowledge to their work or vocation in a professional manner and have acquired the competences required for the preparation and defence of arguments and for problem solving in their field of study.
  • Students must have the ability to gather and interpret relevant data (usually in their field of study) to make judgements that take relevant social, scientific or ethical issues into consideration.
  • Students must be able to communicate information, ideas, problems and solutions to both expert and lay audiences.
  • Demonstrate critical thinking about classic models and models of behaviour.
  • Know the principles of behavioural theory.
  • Know the types of spatial and spatial-temporal data.
  • Use software to collect and analyse survey data.
  • Apply probability and non-probability sampling.
  • Plan and design a sample research.
  • Know and apply the different methods of investment valuation.
  • Manage download APIs and capture and manipulate unstructured data values.
  • Apply unsupervised and semi-supervised machine learning techniques using software.
  • Apply supervised machine learning techniques using software.
  • Make predictions using appropriate software tools to manage time series.
  • Distinguish between the explanatory and predictive approaches in data analysis and in business.
  • Use software tools to solve problems under uncertainty.
  • Identify the basic probability distributions encountered in real problems.
  • Manage and distinguish the concepts of universe, population, sample, parameters and estimators in real problems.
  • Use data mining software.
  • Communicate the results of analyses effectively.
  • Reorganise and restructure variables and databases.
  • Know the different types of data.
  • Tackle problems of management and coordination of the different components of the logistics system by selecting and applying relevant analytical methodologies, strategies and technologies to decision-making.