Python for data analysis (5 ECTS credit)

1. Syntax and Structures of data
2. Data storage and manipulation
3. Numpy, Pandas, Matplotlib and Seaborn Libraries
4. Projects

instructions from data (2 ECTS credit)

1. instructions from data relational
   • model entity relationship
   • Standardization
   • SQL
2. Data acquisition
   • OLAP
   • Internet as source for data
   • exchange of information
3. Distributed storage
   • Blockchain
   • Hadoop (HDFS + MapReduce)
   • Real-time processing
4. instructions of NoSQL data
   • Type
   • MongoDB
5. Google Cloud Platform
   • Compute
   • Cloud SQL
   • BigTable
   • DataStore
   • BigQuery

Display (2 ECTS credit)

1. General visualisation concepts
2. Storytelling with data
3. Commercial platforms for visualisation

Collection techniques from data (2 ECTS credit)

1. Data Management
   • Master Data Management (MDM)
   • Extraction of data in environments similar to business (SQL, Hive)
2. Web scraping
3. Images
4. Social media

Big Data Techniques (3 ECTS credit)

1. Computer architecture. Cloud Computing. Cloud Infrastructure. OCI
2. Analytical SQL. Oracla Autonomous Database: ADW, ATP, JSON
3. Big Data Cloud Products
4. AWS

statistical analysis from data (8 ECTS credit)

1. Review of probability, random variables and hypothesis testing
2. Multidimensional random variables. Joint density and mass. Conditional distributions. Covariance and correlation. Expectation of a random vector, variance and covariance matrix. Independence of random variables
3. Analysis of variance
4. Multiple linear regression and logistic regression
5. Lasso and Ridge Regression
6. Principal component analysis
7. Time Series
8. Classification models
9. Grouping techniques

Preparation and cleaning of data (2 ECTS credit)

1. Exploratory data analysis
2. Pre-processing of data
3. Noise and outlier detection
4. Processing of missing values
5. Treatment of the unbalanced problem
6. Structured and unstructured data
7. Evaluation of the distributions of variables

Machine Learning (6 ECTS credit)

1. Introduction to Machine Learning
2. Types of learning: supervised, unsupervised, semi-supervised and reinforcement learning.
3. Frequentist vs. Bayesian models. Parametric vs. non-parametric models.
4. Inference vs. prediction. Overfit vs underfit. Bias vs variance
5. Data processing, missing values and imputation. Feature engineering, feature importance and explainability
6. Markov chains, Naive Bayes and rules (sequence analysis and association analysis)
7. Instance based models (kNN), LDA, SVM, tree based models (decision tree, bagging trees, random forest...) and regularization.
8. Clustering techniques (kMeans, hierarchical...) and dimensional transformation (Isomap, t-SNE, SOM, SVD, PCA...)
9. Network analysis: spectral clustering, node centrality, bipartite network, co-citation, bibliographic coupling
10. Survival analysis: censored and truncated data, Kaplan-Meier estimator, log-rank test...
11. Ensemble learning methods (sequential and parallel ensemble techniques)
12. Case studies: recommender system, time series and Datathon

Deep Learning (3 ECTS credit)

1. Fundamentals of neural networks. Architectures, activation and loss functions, layers and optimization of hyper-parameters and models.
2. Neural networks for data tabular: classification, regression and time series
3. Natural language processing. Text classification and document clustering
4. Image processing
5. Transfer learning
6. Reinforcement learning
7. Cloud processing. Parallel training of a network and deploy models as a service.

Throughout the subject, you will deploy and integrate Generative AI models adapted to your applications and projects. You will use their full potential to produce personalized content and learn the art of prompt engineering. You will also transform LLMs with your own data and learn the most advanced techniques to create effective prompts.

The Master aims to provide a solid training in terms of technical knowledge, but also a business vision, so that once the Master is completed, students can act as a bridge between the executive and technical levels of a project. In this way, they will be taught by professionals from leading companies and multinationals, practical and successful cases, seeking to apply concepts acquired in the first two modules. In addition, we have the collaboration of IESE Business School, the Business School of the University of Navarra.

management of projects and business vision (5 ECTS credit)

1. Project planning: identification, definition and objectives
2. Agile Methodologies
3. Privacy and transparency. Ethics of artificial intelligence
4. Generative AI projects
5. Applications

Workshops with companies (4 ECTS credit)

exhibition of examples and use cases by experts from renowned companies in various sectors. Tools and techniques taught during the program are addressed through real and current projects.

It plays an important role in the program. A practical approach is sought that at the same time provides solutions to real problems and projects proposed by companies with which there are agreements at partnership. It can be co-directed both by these companies and by academics from the University of Navarra, and is an excellent opportunity for students to lead the implementation of projects with an impact on their professional environment.

work End of Master's Degree (18 ECTS credit)

The TFM will consist of an original work in which the competences acquired during the Master's Degree must be put in internship . It can be done in groups and developed in the framework of a business or institution that proposes a project of collection, cleaning, preparation, advanced analytics of data and visualization of the results. It can also be done through a project of entrepreneurship in this field.

Ethical aspects of data processing, as well as the economic and social impact of the results, should be highlighted. The student must demonstrate that they know how to plan a project and carry it out in a real working environment, in such a way that they acquire a very practical experience in the field of Data Science and Big Data.