Statistics for social sciences I Statistics for social sciences II

1. Understand the importance of transforming large volumes of data into relevant information for decision making and business development in organizations, companies and individuals. 2. Learn the basic techniques of preprocessing and visualization of data. Gain knowledge on methods to work with missing and atypical data. Acquire the ability to use of dimension reduction techniques. 3. Gain knowledge on the main methods of supervised learning in regression and their usefulness in prediction problems. Distinguish between linear and non-linear models and understand the importance of model selection methods. 4. Become familiar with the usual supervised learning procedures for classification. Understand the most common classifiers and their limitations. Gain knowledge in advanced methods for classification and their benefits in business. 5. Be able to identify the appropriate Big Data techniques in real business problems: customer classification, scoring, risk management, fraud detection, bankruptcy prediction, etc.

1. Introduction. 2. Data collection, sampling and preprocessing. 2.1. Types of data. 2.2. Sampling. 2.3. Data visualization tools. 2.4. Missing values. 2.5. Outlier detection and treatment. 2.6. Data transformations. 2.7. Dimension reduction. 2.8. Application: Risk management in the stock market. 3. Supervised learning: regression. 3.1. Linear and polynomial regression. 3.2. Cross-validation. 3.3. Model selection and regularization methods (ridge and lasso). 3.4. Nonlinear models, splines and generalized additive models. 3.5. Application: credit-scoring prediction. 4. Supervised learning: classification. 4.1. Bayes classifiers 4.2. Logistic regression. 4.3. K-nearest neighbors. 4.4. Random forest. 4.5. Support-vector machines. 4.6. Boosting. 4.7. Application: Credit risk. 4.8. Application: Fraud detection. 4.9. Application: Bankruptcy prediction

THEORETICAL PRACTICAL CLASSES Knowledge and concepts students must acquire. Student receive course notes and will have basic reference texts to facilitatefollowing the classes and carrying out follow up work.Students partake in exercises to resolve practical problems and participatein workshops and an evaluation tests, all geared towards acquiring the necessary capabilities.Subjects with 6 ECTS are44 hours as a general rule/ 100% classroom instruction. TUTORING SESSIONS Individualized attendance (individual tutoring) or in-group (group tutoring) for students with a teacher. Subjects with 6 credits have 4 hours of tutoring/ 100% on- site attendance. STUDENT INDIVIDUAL WORK OR GROUP WORK Subjects with 6 credits have 98 hours/0% on-site.