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.