1. Learning outcomes (goals and aims of the discipline):
The goal of the discipline is to obtain basic theoretical knowledge and practical skills in data collecting, processing and analysis, carrying out experimental and analytical research in the sphere of landscape architecture The aims of the discipline include the following:
- to learn the main stages of world notion development, obtain the basic knowledge on the key current scientific paradigms;
- to master the structure of scientific research work, planning and carrying out scientific experiments;
- to learn the main terminology implemented in contemporary soil, environmental and landscape applied science;
- to master the methodology of data collection and analysis and results’ interpretation;
- to learn the basics of mathematical statistics (descriptive statistics, correlation analysis, analysis of variance);
- to master the techniques to visualize the results of scientific research, to learn how to make graphs, tables, figures, maps and schemes and how to make presentations;
- to learn up-to-date approaches to search and analyze scientific information, including conference thesis, scientific publications, books, to be able to use the major bases of knowledge
Syllabus
Chapter topics Practical works
Methodology of scientific research  Observations
 Experiments
 Modelling
Collecting and organization of research data  Data types
 Data sampling
 Datasets
Introduction into descriptive statistics
 Mean, median, mode
 Sample and population
 Scatter plots
 Variance
 Histograms
 Box & Whiskers
Statistical hypothesis  Probability
 Distribution
 Normal and non-normal distribution
 Significance level
T-test  One-sample T test
 Paired T-test
 T-test for independent sample
Correlation  Pearson correlation
 Spearmen correlation
 Graphical interpretation of correlation
Simple linear regression  Regression equations
 Determination coefficient
Multiple regression  Regression models
 Fitting regression models
 Logistic regression

а) main literature:

1. D. M. Diez, C.D. Barr, M. Cetinkaya-Rundel . OpenIntro Statistics. 2014. openintro.org
2. J. Leek. The elements of data analytiс style. http://leanpub.com/datastyle
3. Dmitriev E.A. Mathematical statistics in soil science. MSU edition. 1995.
4. R. Lyman Ott & Michael Longnecker. An introduction to statistical methods and data analysis. 6th edition
5. Hans-Peter Pifo. Statistics for bachelors in Agriculture and Renewable Energy sources. Hochenheim. 288 P.

b) supplementary literature:

6. Aller L., T. Bennett, J. H. Lehr, R. J. Petty, and G. Hackett. 1987. DRASTIC: A standardized system for evaluating ground water pollution potential using hydrogeological settings. EPA/600/2-87/035. Washington, D.C.: Environmental Agency.
7. ArcGis 9. Что такое ArcGis? Официальное руководство ESRI. США. 2004.-127 с.
8. Bailey, T. C., and A. C. Gatrell. 1995. Interactive spatial data analysis. Harlow, UK: Longman.
9. Batty, M. J. 1997. The computable city. International Planning Studies 2: 155–73.
10. Batty, M. J., and P. A. Longley. 1994. Fractal cities: A geometry of form anfunction. San Diego, Calif.: Academic Press.
11. Benenson, I. 2004. Agent-based modeling: From individual residential to urban residential dynamics. In Spatially integrated social science, ed. M. Goodchild and D. J. Janelle, 67–94. New York: Oxford University Press.
12. Berger T. Agent-based spatial models applied to agriculture: a simulation tool for technology diffusion, resource use changes and policy analysis. 2001. Agricultural Economics. # 25. P. 245–260.
13. Carey, G. F., ed. 1995. Finite element modeling of environmental problems: Surface and subsurface flow and transport. New York: John Wiley and Sons.
14. Crosier, S. J., M. F. Goodchild, L. L. Hill, and T. R. Smith. 2003. Developing an infrastructure for sharing environmental models. Environment and Planning B: Planning and Design 30: 487–501.
15. Dibble, C., and P. G. Feldman. 2004. The GeoGraph 3D Computational Laboratory: network and terrain landscapes for RePast. Journal of Artificial Societies and Social Simulation 7(1). Available: jasss.soc.surrey.ac.uk/ 7/1/7.html.
16. Engelen G., White R., De Nij T. Environment Explorer: Spatial Support System for the Integrated Assessment of Socio-Economic and Environmental Policies in the Netherlands. 2003. Integrated Assessment. V. 4, #. 2. P. 97–105.
17. Fotheringham, A. S., and M. E. O’Kelly. 1989. Spatial interaction models: Formulations and applications. Boston: Kluwer.
18. Goodchild M.F. GIS and modeling overview. In: GIS, Spatial Analysis and Modeling. Maguire D.J. , Batty M., Goodchild M.F. (Eds). ESRI Press, Redlands. P. 2-17.
19. Goodchild, M. F., and J. Proctor. 1997. Scale in a digital geographic world. Geographical and Environmental Modeling 1: 5–23.
20. Goodchild, M. F., B. O. Parks, and L. J. Steyaert. 1993. Environmental modeling with GIS. New York: Oxford University Press.
21. Haining, R. P. 2003. Spatial data analysis: Theory and practice. New York: Cambridge University Press.
22. Langran, G. 1993. Time in geographic information systems. London: Taylor and Francis.McHarg, I. L. 1969. Design with nature. Garden City, N.Y.: Natural History Press.
23. Modeling the Spatial Dynamics of Regional Land-Use: The CLUE-S Model Environmental Management V. 30, # 3, P. 391–405.
24. O’Sullivan, D., and D. J. Unwin. 2003. Geographic information analysis. New York: John Wiley and Sons.
25. Peuquet, D. 2002. Representations of space and time. New York: Guilford.
26. Tomlin, C. D. 1990. Geographic information systems and cartographic modeling. Englewood Cliffs, N.J.: Prentice Hall.
27. Worboys, M. F., and M. Duckham. 2004. GIS: A computing perspective. New York: Taylor and Francis.
28. Zeiler, M. 1999. Modeling our world: The ESRI guide to geodatabase design. Redlands, Calif.: ESRI Press.