Basics of computer programming 2700-L-LM-D6POPK-ZTM
Lecture is designed to familiarize students with the methods of working on large data sets. We will present methods and algorithms for information retrieval from structured and unstructured data sets. Data analysis will be presented using the "R" language.
1. Elements and methods database exploration.
2. Methods of Text analysis.
3. Requirements related to data analysis and basic issues of BigData analysis.
4. Various approaches to data analysis.
5. Introduction to semantic analysis of texts, sentiment analysis, classification of texts.
6. Fundamentals of data sets processing - BigData, including data structured and unstructured.
7. Methods of text analysis and practical approach to: importing data, data cleansing, data wrangling, building the model, visualization.
8. Programming techniques: maintenance of table frames, importing data, processing of text data, dates manipulation, data pipes, iterations and loops, building a data analysis model, visualization.
Type of course
Course coordinators
Learning outcomes
After completing the course, students:
KNOWLEDGE:
Know the basic data types of programming language and operations on them: numeric types, dates types, texts, vectors, lists, data frames.
Can calculate the basic parameters of the model: statistic factors, parameters of the linear data model.
Know the principles of data analysis and modeling: know the basic types of data analysis, know available applications of text database analysis, know what the sentiment analysis method, know methods of data analysis searching, refining and analyzing text data, know the available data analysis software.
SKILLS:
After completing the course, students can:
Define data analysis tasks.
Perform data analysis, starting from data import, through cleansing and transformation, to building a model and visualization.
Interpret the results of analysis and visualization.
Apply the basis of the "R" language.
Assessment criteria
Laboratory classes with computer using professional data analysis software - Language R.
Assessment criteria:
Continuous assessment (preparation for classes and activity) 20%
Project 80%
Practical placement
Lack of
Bibliography
1. Hardley Wicham, Garrett Grolemund, Język R, Wydawnictwo Helion, 2018.
2. Julia Silge & David Robinson, Text Mining with R, Wydawnictwo O’Reilly Media, 2017.
3. Jareo P. Lander, R dla każdego, Zaawansowane analizy i grafika statystyczna, APN Promise, Warszawa 2018.
4. Gogołek, Informatyka dla humanistów, Wydawnictwo Trzy Kropki, Warszawa 2012.
5. Marek Gągolewski, Programowanie w języku R, Wydawnictwo Naukowe PWN, 2014.
Additional information
Additional information (registration calendar, class conductors, localization and schedules of classes), might be available in the USOSweb system: