Environmental Science is the study of the places (surroundings) where things live. A more modern approach makes Environmental Science an interdisciplinary topic that encompasses biotic and abiotic factors. In other words, it is the study of living organisms, and their surroundings.

An Introduction to Environmental Science would include quite diverse topics, such as:

• Ecology: plant and animal biology.
• Geology: rocks, minerals and soil.
• Hydrology and Oceanography.
• Meteorology: weather, climate and atmosphere.

Data Analytics contribute to Environmental Science in many ways, such as:

• Helping to understand biological communities and their responses to environmental (habitat) factors.
• Understanding species habitat requirements.
• Modelling species response to changes in environmental conditions.

Our services in Environmental Science include: Data Analytics Training Courses, Project Workshops, and Data Analysis. Our books include quantitative ecology, community ecology, and general data science using Excel and R: the statistical programming language.

Ecology & Environmental Science

Ecology and Environmental Science are closely allied scientific disciplines. Both ecology and environmental science explore biotic and abiotic variables. In ecology the primary emphasis is usually the biological species; their relationships to each other and to their environment. In environmental science the emphasis is often on the environment (habitat) foremost, and how this affects the species that live in these habitats.

Data Analytics have a key role in many aspects of ecology and environmental science, for example:

• Quantitative Ecology.
• Understanding species habitat requirements.
• Modelling species response to environmental conditions.
• Exploring how environmental variables affect species distribution.

Ecology & Environmental Science have close ties to Conservation Biology. In many ways conservation biology utilises the tools of ecology & environmental science, to achieve its goals.

Our services in Ecology & Environmental Science include: Data Analytics Training Courses, Project Workshops, and Data Analysis. Our books include quantitative ecology, community ecology, and general data science using Excel and R: the statistical programming language.

Understanding species habitat requirements

Understanding species habitat requirements is a key topic in ecology and conservation biology. Environmental Science can be used in various ways to help with understanding species habitat requirements:

• Exploring biological communities and environmental (habitat) factors.
• Modelling species response to changing environmental conditions.
• Exploring how environmental variables affect species distribution.

Data Analytics are important in understanding species habitat requirements, using analytical tools from quantitative ecology and community ecology. For example, methods of multivariate analysis can be used to explore relationships between biological communities and environmental variables. These environmental variables are often abiotic (that is, chemical or physical characteristics) but can also be the result of biotic interactions (like competition or predation).

When used in the field these methods of analysis tend to measure species realised niche, which is the habitat a species occupies in the face of competition. The fundamental niche is the range of habitat requirements a species could tolerate without competition from other species. It is harder to ascertain the fundamental niche, especially with mobile organisms, but it is possible with combinations of experimental ecology, biological records and data analytics.

Biological communities and environmental factors

The relationships between Biological Communities and Environmental Factors are a primary concern in Conservation Biology. Data Analytics in Environmental Science can help with:

• Understanding species habitat (environmental) requirements.
• Modelling species response to environmental conditions.
• Exploring how environmental variables affect species distribution.

Biological communities arise where species have similar habitat requirements. Data Analytics tools used in Community Ecology can help explore the relationships between the biological species and the environmental factors that make up their habitat.

Environmental variables and species distribution

Environmental Variables can have a major influence on Species Distribution. Every species will have certain habitat requirements, with different species being able to tolerate different ranges of environmental variables.

Data Analytics can help in modelling species response to environmental conditions, and in exploring the relationships between biological communities and environmental factors.

Biological Recording is also important in helping to visualise and monitor species distribution. This can also help in understanding species habitat requirements.

Modelling species response to environmental conditions

Modelling species response to environmental conditions is a key topic in environmental science. Data analytics can be used to explore how environmental variables affect species distribution, and help in understanding species habitat requirements.

Conservation Biology is often concerned with habitats as well as individual species. Data Analytics can be used to explore how biological communities are influenced by environmental conditions.

Modelling can be used to investigate individual species and the influence of environmental conditions, as well as entire communities and how environmental factors affects ecological diversity and community composition.

Environmental Science & Data Analytics Books

Our books on Environmental Science & Data Analytics include: quantitative ecology, community ecology, and general data science using Excel and R: the statistical programming language.

The R program is a powerful and flexible Open Source statistical environment. The R program is used widely by Universities and Professionals for many tasks in Data Analytics, including: data visualisation, predictive analysis, machine learning, and statistical hypothesis testing. Our Data Analytics books use R extensively, as it is such a useful and powerful tool for Environmental Science and other disciplines.

Our environmental science & data analytics books include Statistics for Ecologists, which covers the basics of data analytics as well as being a primer for using the R program. Statistics for Ecologists, provides a good foundation for the study of data analytics in environmental science.

Understanding how biological communities are affected by environmental factors is a key topic in environmental science. Our book Community Ecology covers a range of topics relating to biological communities, including modelling species response to environmental conditions.

Environmental Science & Data Analytics Courses and Workshops

We provide Data Analytics Training Courses and Project Workshops for Environmental Science, as well as ecology, and conservation biology.

Our Training Courses in Data Analytics include a range of topics helpful in Environmental Science, including: data visualisation, predictive analysis, machine learning, statistical hypothesis testing, and R programming. We can also tailor our training courses to produce bespoke training in environmental science data analysis to best suit your requirements.

Environmental Science data Project Workshops help improve your data interpretation and data analysis skills. Project Workshops are a data-driven approach to data analytics for Ecology, Conservation Biology & Environmental Science.

Project Workshops in Data Analytics for Environmental Science are focussed on the topics that you need, including:

• Data import, cleaning and validation.
• Data visualisation.
• Biological communities and environmental factors.
• Predictive analysis and machine learning.

Project Workshops can utilise your own data, giving you the skills you need to complete your current project as well as future ones.

Training courses and project workshops are held in the UK or online.