Starting from a basic level, the aim is for student to acquire medium-advanced knowledge and to be able to follow the rest of the subjects, as well as to develop a certain degree of autonomy for the personal learning phase that precedes the master's degree. The programming languages taught are R and Python, as they are the most popular and in demand in the professional field.

This module includes the data extraction phase, where student acquires the skills to work with traditional data instructions , which are currently the most common in companies. It also includes the management of environments properly understood as Big Data, such as Hadoop or Spark, and data collection techniques in social networks such as Twitter or Facebook, web scanning or image collection.

Finally, visualisation techniques will be addressed using the tools most in demand in today's business environment.

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

The aim is for student to develop a critical statistical sense, which is necessary in the business environment. Traditional statistical methods such as regression models, decision trees or dimensionality reduction are used. More recent methodologies such as Machine Learning and Deep Learning are also taught. Among others, Random Forest, K-Means, Natural Language Processing or Neural Networks are studied.

statistical analysis from data (8 ECTS credit)

1. Review of probability and random variables
2. Bernoulli, binomial, Poisson, negative binomial, exponential, normal distribution
3. 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
4. Inferential statistics. sample and population. Central limit theorem. Point and interval estimation. Maximum likelihood estimators. Booststrapping. Student's t-distribution and chi-square. 
5. Hypothesis testing. Power, power ratio, significance level and sample size. Interpretation of the p-value. Difference between statistically significant and technically significant.
6. Analysis of variance
7. Multiple linear regression and logistic regression
8. Factor analysis and PCA
9. Time series 

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 and Deep 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.

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. Application to the capstone project

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.