812379 Data mining and data management in aquatic ecology


Type
Lecture and exercise
Semester hours
1
Lecturer (assistant)
Straif, Michael , Melcher, Andreas
Organisation
Offered in
Sommersemester 2020
Languages of instruction
Englisch

Content

Formulation of scientific questions
Systematic data collection, digital data collection: spatial data (geo-referenced data), temporal data (time series)
Data tpye and format: metric, ordinal, categorial data
Data management: data base structure, data bse software (Excel, Access), data import and export, integration of different spatial and temporal scales

You will review a variety of advanced statistical techniques and discuss situations in which each technique would be used, the assumptions made by each method, how to set up the analysis, as well as how to interpret the results. This includes a broad range of techniques for predicting both continuous and categorical outcomes, as well as methods to cluster cases, create statistical groupings of variables, and find similar cases using a large set of variables. You will gain an understanding of when and why to use these various techniques as well as how to apply them with confidence and interpret your output.
Various analytical software: Spss, R
Data exploration and quality assurance
Data distribution
Graphical analyses solutions
Factor Analysis
• Explain the basic theory of factor analysis and the steps in factor analysis
• Explain the assumptions and requirements of factor analysis
• Specify a factor analysis and interpret the output Cluster Analysis
• Explain the basic theory of different cluster analysis
(hierarchical, k-Means, Two-Step) and the steps in doing a cluster analysis
• Explain the approach of K-Means cluster analysis
• Specify a K-Means cluster analysis and interpret the output
Decision trees
• A general introduction to trees analysis
• Specify different tree analysis and interpret the output

Presentation of results (Powerpoint)

Previous knowledge expected

Basics in statistics. Knowledge of MS excel and/or other statistic software.

Objective (expected results of study and acquired competences)

After successful completion of this lecture, participants are able to:

•Understand fundamentals of multidimensional and applied ecosystem statistics and modeling at different spatial scales, from micro-habitat to catchment scale;
•Analyse and discuss ecological and environmental data-sets by using different modeling methods;
•Select appropriate field-survey techniques, sampling strategies and data management schemes for specific research and management goals;
•To sample data in a standardized way in the field and;
•Know how to manage their own data (e.g. MS Access);
•Demonstrate critical thinking in interpreting and deriving conclusions from environmental and ecological data-sets.
You can find more details like the schedule or information about exams on the course-page in BOKUonline.