Big data analytics

Provided by: 

UNIPI, CNR

From: 

M.Sc. in Data Science and BI

Sede: 

Polo Fibonacci, University of Pisa

Lecturers: 

Fosca GIANNOTTI

Semester: 

1

Hours: 

40

Timetable: 

https://www.di.unipi.it/en/education/mds/timetable-mds

Educational Goals: 

In our digital society, every human activity is mediated by information technologies. Therefore, every activity leaves digital traces behind, that can be stored in some repository. Phone call records, transaction records, web search logs, movement trajectories, social media texts and tweets, Every minute, an avalanche of Òbig dataÓ is produced by humans, consciously or not, that represents a novel, accurate digital proxy of social activities at global scale. Big data provide an unprecedented Òsocial microscopeÓ, a novel opportunity to understand the complexity of our societies, and a paradigm shift for the social sciences. Objective of the course is twofold: an introduction to the emergent field of big data analytics and social mining, aimed at acquiring and analyzing big data from multiple sources to the purpose of discovering the patterns and models of human behavior that explain social phenomena and an introduction to the technological scenario of scalable analytics.

Prerequisites: 

The students are expected to be familiar with key management (financial & managerial accounting, cash flow analysis, org design, business processes) and strategy (PorterÕs models, innovation management basics) concepts before starting the course. For management engineering students, the course is highly recommended at 2nd year of the MSc degree (useful complement for PSSP). Recommended for students wishing to apply for Junior Consulting projects. For data science & computer science students, EGI course is recommended.

Programme: 

Module 1: Foundations of competitive intelligence - Systems thinking for management - CI process and Key Intelligence Topics - Sources and collection techniques - Organizing CI in the companies Module 2: Competitor and Market intelligence tools - Competitive benchmarking (to assess competitive cost of operations, to analyze the true capabilities of a rival, as well as its immediate future actions) - Blindspots - Business ecosystems, platforms and business model innovation Module 3: Corporate foresight tools - Technology intelligence tools (i.e. patent analysis) - Scenario analysis tools and techniques - Weak signals and early warning system
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