FREE
Dr Viacheslav Vasenev

Data analysis and statistics in ecology аnd landscape study

  • MOOC
  • Self-Paced
  • English
  • Statistiques et analyse de données

À propos du cours

The course “Data analysis and statistics in ecology and landscape study” (DASELS) aims to provide the basic knowledge and train necessary skills in collecting, processing and presenting scientific data. The lectures of the course cover the broad topics from the introduction to data analysis and general framework of statistical hypothesis’ testing to advanced statistical tools, like multiple and logistic regressions. All the lectures involve vivid examples from current research in environmental and landscape sciences. Seminars are focused on data analysis and statistics in ecology and landscape study using R program. The seminars include the following sections: the introduction to R; the descriptive statistics; the base graphic capabilities; analysis of outliers and checking a normal distribution; comparing two samples and correlation; comparing multiple samples and simple linear regression. The main aim of seminars is to learn how to apply statistics in practice for analyzing data in environmental and landscape study research with descriptive statistics, graphs and basic statistical tests using free software R.

Who is this course for?

The course is recommended for students and researchers, focused on ecology and agriculture, as well as to a broad range of experts, working with data analysis.

What do I need to know?

The DASELS course aims developing knowledge and skills in data analysis starting from the basic level, therefore there are no requirements for the initial competences in math and statistics. Background in landscape and environmental science is assumed and will facilitate better understanding and mastering the practical exercises.

What will you have learnt?

After taking the course, students will obtain theoretical and practical knowledge in basic and more advanced data analysis. They will be able to implement statistical tools for the processing and analysis of research results.

Course Structure

Chapter 1. Descriptive statistics

Chapter 2. Data processing

Chapter 3. Introduction to data analysis

Additional literature for self-study

D. M. Diez, C.D. Barr, M. Cetinkaya-Rundel . OpenIntro Statistics. 2014. openintro.org

J. Leek. The elements of data analytiс style. http://leanpub.com/datastyle

Dmitriev E.A. Mathematical statistics in soil science. MSU edition. 1995.

R. Lyman Ott & Michael Longnecker. An introduction to statistical methods and data analysis. 6th edition

Hans-Peter Pifo. Statistics for bachelors in Agriculture and Renewable Energy sources. Hochenheim. 288 P.