Course Unit Code | 548-0092/01 |
---|
Number of ECTS Credits Allocated | 3 ECTS credits |
---|
Type of Course Unit * | Compulsory |
---|
Level of Course Unit * | First Cycle |
---|
Year of Study * | Second Year |
---|
Semester when the Course Unit is delivered | Summer Semester |
---|
Mode of Delivery | Face-to-face |
---|
Language of Instruction | Czech |
---|
Prerequisites and Co-Requisites | Course succeeds to compulsory courses of previous semester |
---|
Name of Lecturer(s) | Personal ID | Name |
---|
| IVA026 | prof. Ing. Igor Ivan, Ph.D. |
Summary |
---|
This course is focused on a practical work with data, its pre-processing, formatting and transformations. Practical introduction of basic statistical methods of data dimensions reduction and other multivariate methods such as factor, discriminant and cluster analysis and decision trees. Also basic principles of data mining are introduced. The aspect of results interpretation and maximal mining of information from data and geodata are key aspects of this course. |
Learning Outcomes of the Course Unit |
---|
- Student demonstrates knowledge:
• basics of working with the program R
• selected statistical methods
- Students can:
• edit the data matrix
• apply selected statistical methods
• reduce the size of the input data matrix
- Student is able to:
• interpret the results
• extract the maximum information from the supplied data and geodata |
Course Contents |
---|
1) Methods of pre-processing of data and geodata for further processing
2) Errors in data and geodata and dealing with errors
3) Basics in R
4) R and geodata
5) Principal component analysis and interpretation of results
6) Factor analysis and interpretation of results
7) Discriminant analysis and interpretation of results
8) Cluster analysis and interpretation of results
9) Decision trees and interpretation of results
10)Basics of data mining |
Recommended or Required Reading |
---|
Required Reading: |
---|
ZUUR, A. F., IENO, E. N., MEESTERS, E. (2009): A Beginner's Guide to R. Springer, 236 p.
DALGAARD, P. (2008): Introductory Statistics with R. Springer, 380 p.
LOVELACE, R., NOWOSAD, R.,MUENCHOW, J.(2019): Geocomputation with R. Boca Raton: CRC Press, Taylor & Francis Group. The R series. ISBN 978-1-138-30451-2.
SPECTOR, P. (2008): Data manipulation with R. New York: Springer. Use R!. ISBN 978-0-387-74730-9.
STINEROCK, R. N. (2018): Statistics with R: a beginner's guide. Los Angeles: SAGE. ISBN 978-1-4739-2489-5. |
BROM, O. (2015): SPSS – Praktická analýza dat. Computer Press, 320 stran.
PUNCH, K. F. (2008): Základy kvantitativního šetření. Praha: Portál, 152 stran.
ZVÁRA, K. (2013): Biomedicínská statistika IV: Základy statistiky v prostředí. Karolinum, 260 stran.
DALGAARD, P. (2008): Introductory Statistics with R. Springer, 380 p. |
Recommended Reading: |
---|
EVERITT, B., HOTHORN, T. (2011): An Introduction to Applied Multivariate Analysis with R. Springer, 288 p.
SCHUMACKER, R.,E., TOMEK, S. (2013): Understanding statistics using R. New York: Springer. ISBN 978-1-4614-6226-2.
WICKHAM, H., GROLEMUND, G. (2016): R for data science: import, tidy, transform, visualize, and model data. Sebastopol: O’Reilly Media. ISBN 978-1-4919-1039-9.
LAROSE, C. D., LAROSE, D.T. (2019): Data science using Python and R. Hoboken: Wiley. Wiley series on methods and applications in data mining. ISBN 978-1-119-52681-0.
TATTAR, P., RAMAIAH, S., MANJUNATH, B. G.(2016): A course in statistics with R. Chichester: Wiley. ISBN 9781119152750. |
SPIWOK, V. (2015): STATISTICKA ANALYSA DAT V R. VSCHT Praha, 125 stran.
HENDL, J. (2004): Přehled statistických metod zpracování dat. Praha , Portál. ISBN 80-7178-820-1.
HENDL, J. a kol. (2014): Statistika v aplikacích. Praha, Portál. ISBN 978-80-262-0700-9.
EVERITT, B., HOTHORN, T. (2011): An Introduction to Applied Multivariate Analysis with R. Springer, 288 p. |
Planned learning activities and teaching methods |
---|
Tutorials |
Assesment methods and criteria |
---|
Task Title | Task Type | Maximum Number of Points (Act. for Subtasks) | Minimum Number of Points for Task Passing |
---|
Graded credit | Graded credit | 100 | 51 |