Overview
Environmental Science 620: Environmental Data Analysis introduces data analysis—the discipline of extracting meaning from inherently complex data. This rapidly evolving field draws principles and approaches from a variety of disciplines, such as computer science, chemical and mechanical engineering, and mathematics. Data analysis has a significant impact on many fields—like business, health, and smart cities—and it can play an important role in Earth and environmental sciences. Data analysis provides a rich variety of new tools to assist in gaining greater knowledge of the natural environment and in developing applications to effectively mitigate and adapt to environmental pollution and climate change.
Environmental data analysis presents additional challenges for data science, particularly in terms of complexity, spatial and temporal reasoning, and uncertainty management. In this course, you will conduct a variety of case studies in environmental data analysis to gain a better understanding of the subject. Completing an environmental data analysis course can enhance your understanding of the techniques and tools used to analyze environmental data and make decisions based on evidence. This can help you to take informed and effective action on environmental issues.
Learning outcomes
Upon successful completion of this course, you should be able to
- explain the main concepts of data science, different data source types, and data analysis and treatment. Describe key environmental data qualities, such as volume and velocity, variety, and veracity (accuracy/precision).
- determine the main environmental analysis areas, including the geosphere, hydrosphere, biosphere, and atmosphere.
- explain the main environmental challenges that affect human life. Discuss different characteristics of various types of environmental pollution.
- discuss details of a variety of techniques for detecting and collecting environmental data and when and why to implement them based on their essential characteristics. Additionally, have extensive knowledge of the problems, shortcomings, safety precautions, and modelling and experimental issues associated with the most widely used methods for gathering environmental data.
- discuss and acknowledge a variety of environmental situations or features with a wide range of challenges in environmental science, including thermal comfort, rainfall, deforestation, overpopulation, natural disasters, climate change, and unsustainable waste generation.
- determine and acknowledge the main challenges related to data analysis. Learn the complexity and variability of the underlying data sources in data analysis.
- apply modelling techniques and approaches to analyze environmental problems, including time series analysis, root-finding techniques, integration, interpolation, and numerical differential analysis.
- explain and apply different environmental models, such as the NOGAPS-ALPHA model, Global Environmental Multiscale Model (GEM), European Centre for Medium-Range Weather Forecasts (ECMWF) model, Unified Model (UKMO), Weather Research and Forecasting (WRF) model, fifth-generation mesoscale model, etc.
- develop clear technical writing skills through essays and reports.
Evaluation
To receive credit for ENSC 620, you must achieve a cumulative course grade of at least B– (70 percent), an average grade of at least 60% on the assignments, and at least 60% on the final project.
Your cumulative grade is based on the following assessment activities:
| Activity | Weight |
| Assignment 1 | 10% |
| Assignment 2 | 15% |
| Assignment 3 | 15% |
| Assignment 4 | 30% |
| Final project | 30% |
| Total | 100% |
Materials
Digital course materials
Links to the following course materials will be made available in the course:
Emetere, M. E. (2022). Numerical methods in environmental data analysis. Elsevier.