Introduction to Data Analytics: plan, collect data, analyze data, present the results of the analysis
Structured and unstructured data, common representation formats, relational model
Characteristics of social data
Methods for extracting data from social networks: API, scrapers and more
Metrics and KPIs to monitor social media
Social network analysis and its applications
Elements of Machine Learning: supervised and unsupervised methods
Elements of text analysis: classification, sentiment, named entities extraction, topic modeling
Data visualization, reporting and interactive dashboards
Examples and case studies: trend analysis, brand awareness, social monitoring / listening, identification of influencers, etc.
The various topics will be accompanied by practical demonstrations using open and / or commercial software tools, such as Tableau, Netlytics, Scikit-learn, Talkwalker, Brandwatch and others.