Certificate course in

Data Analysis in Earth, Water and Natural Resources Studies

Why choose this course?

MSc research in the earth, water, and natural resources includes a phase of data reporting and analysis, where the analyst must use appropriate descriptive and inferential statistical methods to answer research questions. Most students in these sciences collect field data; this requires sampling schemes that makes possible the chosen analytical techniques and provides sufficient power to answer research questions.

What is the course content?

This course is to give candidates a head start on data analysis they will need to do research, by learning the principles of data analysis as well as specific techniques according to their research topics. Because of the wide diversity of techniques, half of the module will be taught as directed self-study, from texts, primary literature and relevant computer programmes. The other half are common lectures/computer exercises using the R open-source statistical computing environment.

• Common (1.5 weeks) - Statistical inference for research (review of topic from Research Skills); A data analysis strategy; The R environment for statistical computing; Review of descriptive statistics and exploratory data analysis; Linear modeling and extensions; Selecting appropriate analytical methods; learning techniques from literature (texts and papers); Basic non-spatial and spatial sampling theory, sample design.
• Choice topics - Depending on thesis topic, student can choose a guided self-study in techniques covered by staff, including: Geostatistics, modeling spatial structure, mapping by interpolation; Multivariate modeling including factor analysis, partial least-squares regression; Logistic regression; Weights-of-evidence; Time-series analysis; Fragmentation statistics, pattern analysis; Non-linear modeling, curve fitting. This will be developed into and individual data analysis project, preferably using student's own data or similar provided by instructors.

Students will be prepared to follow a proper sequence to document, describe, explore and analyze their field or lab data, and to design a sound sampling scheme. They will be able to use the R environment for statistical computing at a basic to intermediate level.

Admission requirements

Academic level and background

Applicants for the Certificate programme should have a Bachelor degree or equivalent from a recognised university in a discipline related to the course, preferably combined with working experience in a relevant field.

Some courses in the Certificate programme or separate modules require knowledge of, and skills in, working with GIS and/or digital image processing of remotely sensed data.

Skills in taught or related subjects are a prerequisite for some courses in the Certificate programme or separate modules. Even if the applicant satisfies the overall admission requirements, acceptance is not automatic.

English language

As all courses are given in English, proficiency in the English language is a prerequisite. If you are a national of one of the countries in this list (PDF), you are exempted from an English language test. Please note: the requirements when applying for fellowships may vary according to the regulations of the fellowship provider.

English language tests: minimum requirements

Only internationally recognised test results are accepted.

Computer skills

Applicants lacking computer experience are strongly advised to follow basic courses in their home countries.