8/26/2023 0 Comments Regression analysis rstudio![]() ![]() Since the 17 th century, scientists have recognised experimental and theoretical science as the basic research paradigms for understanding nature. However, it will pay off!īe aware that the beginning of the Digital Age may be dated around the year 2002, when more than 50% of the data worldwide available had been stored digitally in contrast to data stored in analogues forms ( Hilbert and López 2011). We will cover a lot of ground and it will need stamina and dedication to work through all of that. We will develop programming skills by learning and applying the statistical programming languages Rand Python. For our purpose this means that we need to learn how to load/save, manipulate, visualise, and analyse (geospatial) data. Last but not least we must develop programming skills. The main focus of the present E-Learning project is on statistics: Yes, we will learn statistics! In addition we need to know about mathematics and statistics, which is known as the arts of collecting, analysing, interpreting, presenting (visualizing), and organising data. Source: Modified after blog of Drew Conway (2010, )Īs shown above in the Venn diagram modified after Drew Conway (2010) to do data science we need a substantive expertise and domain knowledge, which in our case is the field of Earth Sciences, respectively Geosciences. Data Science itself is an interdisciplinary field about processes and systems to extract knowledge from data applying various methods drawn from a broad field of different scientific disciplines, such as mathematics, statistics, and computer science, among others. In a more general sense the project is all about Data Science. This project bundle is all about processing and understanding data, with a special focus on geospatial data. In the exercises, you will be asked to programmatically solve problems based on either toy datasets or real-world datasets. Some of the subsections conclude with a hands-on programming exercise. However, if you are already familiar with a particular topic, feel free to skip that lesson and focus on the lessons that are of interest to you. These lessons build on each other, so it is recommended that you work through them in the order they are presented. The coding will cover the entire analysis pipeline, from data import, through data cleaning and wrangling, to exploratory data analysis, including visualisation, modelling, and interpretation of results.Įach section is made up of a number of subsections and lessons that cover a specific topic. The methods are applied to real world datasets in form of hands-on coding exercises using the programming languages R and Python. The datasets that will be analysed are related to field of Environmental Earth Science, including Climatology, Hydrology, Paleoclimatology, Geochemistry, Remote Sensing, among others. ![]() ![]() The E-Learning modules teach fundamental and advanced mathematical and statistical concepts and methods for data analysis. Both projects are organised in four main chapters: ![]()
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