Online Master in Analytics Management, Big Data and Artificial Intelligence

Online training will involve a teaching load of between 6 and 8 hours per week. Once the Master's Final Project has been defined, the participant must include 2 more hours of dedication per week.

Block 1: Analytics Strategy

Objectives: Align analytics with the company strategy. Create an Analytics strategy to generate value for the company.

• Business Process Review. Needs and Value of the Analytical Model.
• Value contribution by process.
• Cost/benefit analysis of analytics by process.
• Planning, Prioritization and BMC Examples and Strategic Objectives.

Block 2: Data Collection, Management and Governance

Objectives: Understand the challenges of obtaining quality data and its treatment. Define and manage a governance committee and its functions.

• Types of data and sources of collection.
• Data extraction, quality and processing.
• Design and implementation of a data strategy aligned with the organization's strategy. Professional profiles needed to do it successfully.
• Data office and data governance committee to be able to work with quality data.

Block 3: Digital Analytics

Objectives: Understand and put into practice the basic analytics of the digital world as well as the analysis of the structure of social networks

• Basic digital analytics: Google, Meta, etc. Structural analysis of social networks (SNA).
• Analyze data types to define market niches and make informed decisions. Evaluate your own and your competitors' digital positioning, including organic positioning strategies.
• Create efficient dashboards to connect organic data with effective digital marketing actions.

Block 4: Main Analytics

Objectives: Understand basic analytics based on statistics and direct methods

• Develop analytical reasoning to solve problems and make data-driven decisions.
• Apply descriptive and inferential statistics to interpret data, estimate and test hypotheses.
• Select appropriate prediction models according to business needs and characteristics.

Block 5: Artificial Intelligence

Objectives: Understand and put into practice the main Artificial Intelligence techniques applied to analytics.

• Supervised and unsupervised learning techniques.
• Learn about the areas of artificial intelligence. Machine learning and its possibilities, understand and implement models.
• Know the management criteria for accepting a model. Be aware of how to improve competitiveness through data. Be able to apply functional knowledge to your own case.

Block 6: Generative AI

Objectives: Provide guidance for planning, executing and evaluating generative AI projects, including ROI measurement.

• Fundamental concepts and main models and architectures: generative adversarial networks (GANs) and transformers (GPT, BERT). Review of business applications.
• Management of teams and profiles required to develop AI projects (data scientists, AI engineers, business specialists)
• Ethics in generative AI: biases in models, data privacy, transparency and fairness.

Block 7: Analytics Project Management

Objectives: Know how to direct and manage an analytics project and know how to estimate its cost

• Agile project management techniques
• Economic evaluation of projects

Block 8: Application to business areas

Objectives: Understand the value and know how to apply analytics to the marketing and operations environment.

• Analytics applied to Marketing
• Analytics applied to Operations
• Analytics applied to Finance

Block 9: New Skills

• Decision making and cognitive biases
• Evaluating evidence in decision making
• Data visualization
• Management and leadership of highly qualified teams

Final project

Carrying out a project is one of the key methodologies of the EADA pedagogical model, as it allows the transfer of learning to a business reality. It involves identifying the organisations or companies that need to carry out a strategic plan in the area of Analytics and Big Data in order for it to have a real impact on the company.

Project ideas are proposed by the program participants themselves and, after being validated by the Program Management, are developed as a team with the help of a tutor assigned for this purpose.

The project is defended before an academic tribunal made up of finance and strategy professionals and managers from the Analytics and Big Data area, on the last day of the program, online.

Management Skills Module (Collbató Residential Training Campus)

Personal leadership

• Self-knowledge
• Cognitive styles and associated behaviors
• Foundations of Personal Leadership.
• Skills development
• Learning and unlearning.
• Leadership models (situational leadership, authentic leadership, trust-based leadership...)
• Communication as a fundamental instrument of leadership
• The development and leadership of high-performance teams (stages of a team, roles in teams, learning in teams...)
• Leadership and Change