The First-Level Short Specialisation Degree in Data Science & Data Management provides training on the methods and tools to use text data – strategically important in the current historical phase. In this view, their collection, analysis, assessment and management represent the competences to define which and what type of knowledge should be extrapolated from text data, so as to operate in the different corporate-organizational, healthcare, political-economic, economic-political, legal, community and public policy sectors.
The goal of the Master is to build a Data Scientist & Data Analyst profile, capable of analysing the different human needs (historically and narratively speaking), anticipating future scenarios, defining strategies, innovating and supporting decision-making processes, monitoring and assessing project effectiveness.
The First-Level Short Specialisation Degree in Data Science & Data Management provides training on theoretical language models; maths-statistical fundamentals for data science; data management; data management and analysis systems; machine learning & IA; big data analytics; data visualization & curation.
The course provides three kinds of contents:
- Knowledge on:
- theoretical-methodological references to language studies;
- data science models and tools;
data analysis & data management.
- Applicative competences to:
- data analysis for target detection and need anticipation, future scenario anticipation, strategy definition, innovation, decision-making processes;
- applications in corporate-organizational, healthcare, political-economic and economic-political, legal, community, and public policy sectors;
- M.A.D.I.T. method and statistical-experimental analysis plan construction methods;
- mastering of statistical-maths fundamentals;
- machine learning & IA, big data analytics, data visualization, data curation, data management.
- Independent judgement to promote:
- analysis of critical aspects concerning data privacy, data protection and data ethics;
- social and ethical liability management;
- data management error analysis and anticipations;
The First-Level Short Specialisation Degree in Data Science & Data Management is addressed to graduates with a Bachelor’s Degree in Economics, Engineering, Computer Sciences, Statistics, Mathematics, Psychology, Biology, Sociological Sciences, Communication, Philosophy, Government and Public Policy Sciences, European and Global Studies, Local Development, Law and Technology, Linguistics, Social Services (only for those with a Master’s Degree).
After concluding the First-Level Short Specialisation Degree, future Data Scientists & Data Analysts can work for public and private institutions with a large quantity of text data, such as government agencies, universities, research institutions, medium-large corporate research centres, mass media, PA institutions and bodies, European planning institutions, banks and foundations, insurance companies, trade and sales chains (large scale distribution trade), consultancy firms.
The First-Level Short Specialisation Degree prepares Data Scientists and Data Analysts in several application fields: marketing, corporate-organizational, political-economic and economic-political, healthcare public policies, legal and communitarian.
The First-Level Short Specialisation Degree in Data Science & Data Management provides in-depth training on the following modules:
Module 1 – Knowledge-based and data-driven analysis
- Formalization of language interactions
- Language analysis theories and models
- Epistemological foundations behind the reference scientific paradigm for the generation of observational data, and reflections on the available approaches (behaviourism, innatism, interactionism, neural network, NLP, linguistics, DPA, etc.)
Module 2 – Data privacy, data protection and data ethics
- Role profile and competences of a Data Scientist and Data Analyst
- Knowledge on ethical questions, privacy law and data protection references, and Data Scientist/Data Analyst role and competences
Module 3 – Text data analysis methods
- Promotion of competences to use the M.A.D.I.T. Method, to design with specific analysis goals, and to use a statistical-experimental method to build rigorous and effective analysis plans
Module 4 – Advanced Statistics for Data Science
- Data management and analysis data
- Development of knowledge/use competences of the maths/statistical fundamentals behind Data Science and data management and analysis systems (R, Python, PowerBl, Tableau, Google Analytics, SAS, etc.)
Module 5 – Machine Learning, IA and Data Science Algorithms
- Big Data Analytics & Real Time Big Data Processing
- Development of IA-related (state of the art, data/information processing; IA-based techniques, tools and systems) and Machine Learning (algorithm development, model selection, deep learning, etc.) knowledge and competences; state of the art strategies to extract knowledge from Big Data Analytics & Real Time Big Data Processing
Module 6 – Data Visualization; Data Curation & Data Management Technology
- Knowledge on the main tools for an effective representation and visualization of text data, and to spread the main tools supporting Data Management and Curation
Module 7 – Data Management
- Development of knowledge related to business process management and text data management models (data collection, data quality, database NoSQL vs SQL, DBSM and DWH J)
Module 8 – Data Strategy & Decision Making and error analysis to generate innovation and change
- Development of use competence on text data to define strategies and decision-making processes, and error analysis competences to bring innovation
Module 9 – Applications in Data Intelligence and Marketing
- Social media applications
- Legal administration applications
- Healthcare system applications
- Community applications
- Sustainability applications
- Workshops to develop project/analysis plans/consideration on text data use in relation to specific analysis goals
The First-Level Short Specialisation Degree’s peculiarities include:
- integration between computer science, statistics, and language use observations, including dialogic
- applications of knowledge and competences to several fields: in corporate-organizational, healthcare, political-economic and economic-political, legal, community, and public policy
- promoting the increase of an effective, efficient and rigorous management of text data generation, thanks to the interaction between the disciplines mentioned above, which allow changing the way we recognize and analyse text data, and therefore to intervene with respect to multiple critical issues found today in analysis (extrapolating knowledge from a large quantity of data; not knowing which data to select; not knowing how to use data for specific goals, etc.)
Future Data Scientists & Data Analysts can develop broad competences in the observation and gathering of text data, and in the implementation of IT and statistical tools to analyse them.
The First-Level Short Specialisation Degree in Data Science & Data Management includes in-person (mainly) and remote lessons, and promotes the interaction between professors and students, through lectures, individual practice and workshops on multiple application cases. The course includes a mandatory 250-hour traineeship/project work and a final exam. A mandatory attendance of 70% of the total hours (300) is required.
The general ranking of merit for the academic year 2023/24 will be published on the Italian page of this First-Level Short Specialisation Degree according to the timing provided in the Call.
Yes, there will be a 250-hour traineeship to observe and develop text data analysis skills in relevant institutions.
The First-Level Short Specialisation Degree does not provide any forms of facilitation. You can check whether you have the requirements to request a scholarship to the University of Padua.
The Course include part of the lessons in person, and part of them online. For specific needs, refer to the course’s Director.
The First-Level Short Specialisation Degree collaborates with a network of enterprises and professionals and helps students to interface with the institutions they would like to work for.