Data Science

General Objectives 1. Knowledge of the main application scenarios in analyzing collections of textual data using NLP techniques. 2. Knowledge and understanding of the main methodological and analytical challenges. 3. Knowledge of the main data analysis and machine learning techniques for natural language and the primary tools available to implement them. 4. Understanding of the theoretical foundations underlying advanced techniques for textual data analysis and natural language learning. 5. Ability to translate acquired notions into programs that solve specific problems. 6. Knowledge of the main evaluation techniques and their application to practical scenarios. Specific Objectives Abilities - Identify the most suitable text-mining and/or NLP techniques to address a given problem. - Implement the proposed solution by selecting the most appropriate tools. - Design and conduct experiments to evaluate proposed solutions under realistic conditions. Knowledge and Understanding - Knowledge of the main application scenarios. - Knowledge of the main analysis techniques. - Understanding of the theoretical and methodological assumptions underlying the main techniques. - Knowledge and understanding of the main evaluation techniques and corresponding performance indices. Applying Knowledge and Understanding - Translate application requirements into concrete data-analysis problems. - Identify the most suitable techniques and tools to address those problems. - Qualitatively estimate the scalability of the proposed solutions in advance. Critical and Judgment Skills - Evaluate experimentally the effectiveness, efficiency, and scalability of proposed solutions. Communication Skills - Effectively describe the requirements of a problem and communicate the chosen solutions and their rationale to others. Learning Ability - Develop independent-study skills on course-related topics and critically consult advanced manuals and scientific literature to tackle new scenarios or apply alternative techniques.