Předmět Social Web: (Big) Data Mining (JSB454)

Na serveru studentino.cz naleznete nejrůznější studijní materiály: zápisky z přednášek nebo cvičení, vzorové testy, seminární práce, domácí úkoly a další z předmětu JSB454 - Social Web: (Big) Data Mining, Fakulta sociálních věd, Univerzita Karlova v Praze (UK).

Materiál	Typ	Datum	Počet stažení

Cíl

Intended Learning Outcomes | in which way the course should make your life better and/or improve your skills. Upon completion of the course, the students will be able to:understand the intersection of data science, humanities & ICT within the realm of web & social media (big) data miningask meaningful questions, perform basic analytical operations regarding both, structured & unstructured web / social media data and draw conclusions for decision makingunderstand basic concepts and conduct subsequent data preprocessing, analysis & visualization related to social network analysis, web mining, social media mining & text miningtake a positive approach towards data science & computer programming, gain confidence in basic operations and use and/or modify a third party (open) source code and/or an analytical procedure/tooldescribe advanced data mining methods & applications for further self education (and/or subsequent institutional education) and/or professional/academic specialization

Sylabus

lectures are followed by tutorials in order to put knowledge into practice | the exact dates & content of the lectures may be subject to change based on pace & requirements of the course group Lecture #1: Introduction to Data Mining & Data Analysis | Data Science | Digital HumanitiesLecture #2: Big Data | Types of Data | Data Formats | Information Retrieval | Business Intelligence | Law & Ethics of Data MiningLecture #3: Introduction to Web Technologies for Non-Tech Students | Database Systems | Web Programming | Semantic Web | APIsLecture #4: Graph Theory | Social Network Analysis | Statistical Procedures, Apps & ToolsLecture #5: Pseudocoding | Introduction to Programming in Python (& R language comparison) | Data Exploration & PreprocessingLecture #6: Web Scraping | Data Cleaning & Processing | Python Implementation & Libraries, Statistical Procedures, Apps & ToolsLecture #7: Social Media Mining | Data Cleaning & Processing | Python Implementation & Libraries, Statistical Procedures, Apps & ToolsLecture #8: Text Mining | Natural Language Processing | Python Implementation & Libraries, Statistical Procedures, Apps & ToolsLecture #9: Data Visualization | Data Storytelling | Electronic Publishing | Python Implementation & Libraries, Statistical Procedures, Apps & ToolsLecture #10: Student Webinars Week | Introducing Various Free & Open Source Data Mining Software & AppsLecture #11: Machine Learning, Recommender Systems & Other More Advanced Topics | Large-Scale DataSets | MapReduce, Hadoop, NoSQLLecture #12: Course Review | Semestral Projects Consultation & Adjustments | The Remaining 99% of Data Science

Literatura

you are not required to read any of the following, but you might find it handy when looking for inspiration, reference, sample analyses, sample code or when some part of the course takes your interest so that you want to follow up with more in-depth self-directed studyfurther online/paperback study resources, tutorials, libraries, applications & tools will be introduced within specific topics of the course GOLBECK, Jennifer. ANALYZING THE SOCIAL WEB. Amsterdam: Morgan Kaufmann, 2013. ISBN 01-240-5531-1.O'NEIL, Cathy and SCHUTT, Rachel. DOING DATA SCIENCE. Sebastopol, CA: O'Reilly, 2013. ISBN 14-493-5865-9.MCKINNEY, Wes. PYTHON FOR DATA ANALYSIS: DATA WRANGLING WITH PANDAS, NUMPY, AND IPYTHON. Beijing: O'Reilly Media. ISBN 978-1449319793.RUSSELL, Matthew A. MINING THE SOCIAL WEB: DATA MINING FACEBOOK, TWITTER, LINKEDIN, GOOGLE , GITHUB, AND MORE. 2nd ed. Sebastopol: O´Reilly, 2014. ISBN 978-1-449-36761-9.JANERT, Philipp K. DATA ANALYSIS WITH OPEN SOURCE TOOLS. Sebastopol, CA: O'Reilly. ISBN 05-968-0235-8.WASSERMAN, Stanley and Katherine FAUST. SOCIAL NETWORK ANALYSIS: METHODS AND APPLICATIONS. New York: Cambridge University Press, 1994. ISBN 05-213-8707-8.HANSEN, Derek, Ben SCHNEIDERMAN and Marc SMITH. ANALYZING SOCIAL MEDIA NETWORKS WITH NODEXL: INSIGHTS FROM A CONNECTED WORLD. Burlington, MA: Morgan Kaufmann, 2011. ISBN 01-238-2229-7.STEELE, Julie and Noah ILIINSKY. BEAUTIFUL VISUALIZATION. Sebastopol, CA: O'Reilly, 2010. ISBN 14-493-7986-9.FRY, Ben. VISUALIZING DATA. Sebastopol, CA: O´Reilly, 2007. ISBN 05-965-1455-7.RAJARAMAN, Anand and Jeffrey ULLMAN. MINING OF MASSIVE DATASETS. Cambridge: Cambridge University Press, 2012. ISBN 11-070-1535-9.NORTH, Matthew. DATA MINING FOR THE MASSES. Global Text Project, 2012. ISBN 06-156-8437-8.PROVOST, Foster. DATA SCIENCE FOR BUSINESS: WHAT YOU NEED TO KNOW ABOUT DATA MINING AND DATA-ANALYTIC THINKING. Sebastopol, CA: O´Reilly. ISBN 978-1-449-36132-7.MINELLI, Michael, Michael CHAMBERS and DHIRAJ, Ambiga. BIG DATA BIG ANALYTICS: EMERGING BUSINESS INTELLIGENCE AND ANALYTIC TRENDS FOR TODAY'S BUSINESSES. Wiley, 2013. ISBN 111814760XSTATSOFT. ELECTRONIC STATISTICS TEXTBOOK [online]. 2013. https://www.statsoft.com/textbook https://www.python.org/doc/http://www.w3schools.com/https://github.com/http://stackexchange.com/sites#https://developers.facebook.com/docs/https://dev.twitter.com/docshttps://developer.linkedin.com/apishttp://instagram.com/developer/https://developers.google.com/+/https://developers.pinterest.com/https://developer.foursquare.com/http://flowingdata.com/http://www.informationisbeautiful.net/

Garant

Mgr. Jakub Růžička