Dipartimento di InformaticaUniversità degli Studi di Milano

Big data and digital methods2015/16

Ma in Public and corporate communication · University of Milan · Lectures are in English · 3 credits · 40 hours

Archived edition: this page is kept for reference, and some materials or links may no longer be available.

This course introduces the main issues related to the analysis of large amounts of data. The course, taught in English, is composed by two modules: big data (BD) and digital methods (DM)

News

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Course schedule

Lectures will take place at the Edificio 1 of via Conservatorio, according to the following tentative schedule:

Day Hour Place
Wednesday 8:30 - 10:30 aula 26 (DM)
Thursday 8:30 - 12:30 aula 26 (BD)
Friday 8:30 - 10:30 aula 26 (DM)

Any change to the schedule will be announced in class and published in paragraph News of this page.

Office hours

By appointment, room 5015 of the Computer Science Department. It is possible contact the teacher by e-mail, taking care to read in advance the guide prepared by Prof. Sebastiano Vigna and clearly specifying in the message the course name and the academic year. In particular, students are encouraged to always use their academic address (i.e. based on the domain studenti.unimi.it) signing with name and student ID number and recalling that the response time may vary depending on the teacher commitments.

Course material

The BD module is based on the following textbooks: Viktor Mayer-Schönberger and Kenneth Cukier, Big data – A revolution that will transform how we live, work, and think, Aemon Dolan, 2013 (ISBN:978-0-544-00269-2), e Bernard Marr, Big data: Using SMART Big Data, Analytics and Metrics To Make Better Decisions and Improve Performance, Wiley, 2015 (ISBN:978-1-118-96583-2)

The DM module is based on the following material: Rogers, R. 2013. Digital Methods, Cambridge, MIT Press; Hine, C. 2015. Ethnography for the Internet: Embedded, Embodied and Everyday, London Bloomsbury; additional material provided in class. It is strongly recommended to attend class using a laptop, to learn the basics of the Digital Methods Initiative tools package, and to download the free software Gephi. Students not attending class are also asked to study on the book Kozinets, RV 2010. Netnography: Doing Ethnographic Research Online. Los Angeles: Sage.

Syllabus

BD module: overview of the «big data» world. Analysis of the «big data» phenomenon; analyzing big masses of data; data science: an example; working with incoherent data; looking for correlation among data; datafication; the value in data; risks linked to the «big data» phenomenon; possible evolution of the «big data» phenomenon. DM module: big data (opportunities and constrains); digital methods; ethnography of the Internet; netnography; digital ethnography; etnomining and short-term ethnography; useful heuristics; data collection tools; coding/programming scrapers; sentiment analysis; content analysis; quali-quantitative text analysis; online network analysis; introduction to Gephi; digital identity analysis; twitter analysis; data visualization; research design.

Lectures calendar

Lecture Date Topic Material
L01 24/09/2015 BD1: A tour on big data
L02 01/10/2015 BD2: Applications and tools
L03 08/10/2015 BD3: Case studies
L04 15/10/2015 BD4: Data science
L05 22/10/2015 BD5: Pagerank
L06 29/10/2015 BD6: Entity resolution
L07 05/11/2015 BD7: Clustering
L08 12/11/2015 BD8: Recommender systems
L09 19/11/2015 BD9: Machine learning
    L10 26/11/2015 BD9: Machine learning
    l01 23/09/2015 DM01: Big Data (opportunities and constrains)
      l02 25/09/2015 DM02: Digital Methods
        l03 30/09/2015 DM03: Ethnography of the Internet
          l04 02/10/2015 DM04: Netnography
            l05 07/10/2015 DM05: Digital Ethnography
              l06 09/10/2015 DM06: Etnomining & Short-term Ethnography
                l07 14/10/2015 DM07: Useful Heuristics (Community, Public, Crowd and Digital Identity)
                  l08 16/10/2015 DM08: Data Collection Tools
                    l09 21/10/2015 DM09: Coding/Programming Scrapers
                      l10 23/10/2015 DM10: Sentiment Analysis
                        l11 28/10/2015 DM11: Content Analysis
                          l12 30/10/2015 DM12: Quali-Quantitative Text Analysis
                            l13 04/11/2015 DM13: Online Network Analysis
                              l14 06/11/2015 DM14: Introduction to Gephi
                                l15 11/11/2015 DM15: Digital Identity Analysis
                                  l16 13/11/2015 DM16: Twitter Analysis
                                    l17 18/11/2015 DM17: Case study 1
                                      l18 20/11/2015 DM18: Case study 2
                                        l19 25/11/2015 DM19: Data Visualization
                                          l20 27/11/2015 DM20: Research Design (course recap)

                                            Exam modalities

                                            The exam consists either in: i) an oral test on the content of the BD and DM modules, ii) a project per module, or iii) a project combining topics from both modules.