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Web analytics and mining

General data

Course ID: WT-EM-WAM Erasmus code / ISCED: 08.2 / (unknown)
Course title: Web analytics and mining Name in Polish: Web analytics and mining
Department: Institute of Media Education and Journalism
Course groups: Courses at UKSW
ECTS credit allocation (and other scores): (not available)
view allocation of credits
Language: English
Subject level:

intermediate

Learning outcome code/codes:

enter learning outcome code/codes

Short description:

This course is an introduction to traffic measurment and internet research. It starts with a technical introduction to network mechanisms and measuring methods. Participants are involved into web traffic analytics, in social media, advertisements, mailings, RSS and smartphone/tablet applications measurement. They learn, how to measure search engine optimalization, how to fetch hidden information from network pictures and how to investigate digital evidence.

Full description:

1. The role of analytics and "big data" for the internet media, marketing and business.

2. Technical background: understanding TCP/IP, HTTP and cookies. Geolocalization.

3. Measuring methods: logs, pixel, redirect, panel, spy. Internet research companies.

4. Web analytics key indicators: hit, pageview, visit, cookies user, registered user, P/V, bounce rate.

5. Advertising analytics. Models FF, CPM, CPC, CPA. CTR and CR indicators. Methods of campaign optimizing.

6. Social media analytics.

7. Measuring alternative media: mailings, RSS, offline applications.

8. SEO/SEM (Search Engine Optimalization / Search Engine Marketing). Authority, sitemaps.

9. Metadata and privacy. IPTC, EXIF, GPS data.

10. Unstructured mining. UX (User Experience), eyetracking, kansei, data profiling.

11. Basics of text mining. Web spiders, text analytics, lexical algorythms.

12. Introduction to statistics. Descriptive statistics, EV, distribution, histograms, correlation, XY analysis, grouping and clustering. Tools.

13. Statistical visualizations.

14. Network investigation. Veryfing IP, domains, tracing routes, certificates.

15. Final test.

Bibliography:

Google, Digital Analytics Fundamentals, Analytics Academy, https://analyticsacademy.withgoogle.com/course01

Schutt R., O'Neil C., Doing Data Science: Straight Talk from the Frontline, Sebastopol 2013. (PL) Badanie danych. Raport z pierwszej linii działań, Gliwice 2014.

Beasley M., Practical Web Analytics for User Experience: How Analytics Can Help You Understand Your Users, 2013. (PL) UX i analiza ruchu w sieci. Praktyczny poradnik, Gliwice 2014.

Frontczak T., Marketing internetowy w wyszukiwarkach, Gliwice 2006.

Kasperski M., Boguska-Torbicz A., Projektowanie stron WWW. Użyteczność w praktyce, Helion 2008.

Kaushik A., Web Analytics 2.0: The Art of Online Accountability and Science of Customer Centricity, 2009. (PL) Web Analytics 2.0. Świadome rozwijanie witryn internetowych, Gliwice 2010.

Kaushik A., Web Analytics: An Hour a Day, 2007. (PL) Godzina dziennie z Web Analytics. Stwórz dobrą strategię e-marketingową, Gliwice 2009.

Markov Z., Larose D., Data Mining The Web. Uncovering Patterns in Web Content, Structure, and Usage, 2007. (PL) Eksploracja zasobów internetowych, Warszawa 2009.

This course is not currently offered.
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