Data Science and Statistics for Environmental Professionals
Basic course to learn environmental data management: solid waste, air pollution, effluent discharge, groundwater, etc.
Sustainability,Engineering Environmental
Lectures -29
Resources -9
Duration -2 hours
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Course Description
Are you an environmental professional interested in improving your Data Management skills?
This course explains the importance of understanding Data Science and Statistics concepts for environmental data management and helps environmental professionals to draw the best conclusions when analyzing any data set.
This is your course if you work in the environmental field and want to take your first steps with Data Science and Statistics and:
- You want to learn the basic concepts of data science and statistics and how to use them effectively.
- You want to carry out environmental consulting in the field of environmental data analysis and management.
- You want to learn how to use Exploratory Data Analysis techniques to help in the Data Storytelling process.
In this course, you will learn the fundamental principles and concepts of Environmental Data Management using data science and statistical methods and techniques, this will help you to understand the first steps needed when evaluating and analyzing your data set.
To achieve that, you will be encouraged to learn and use software, languages and tools used to evaluate data and extract relevant information out of it, such as:
- R for statistics
- Pro UCL, from EPA
- Visual Sampling Plan
- Excel
- MAROS
- GWSDAT
- Minitab
- Etc.
The software mentioned above will not be explained in detail, on the other hand, throughout the course the students will be stimulated to use the tool(s) that they fell more comfortable with, in order to develop their skills in the tools that make more sense for each one.
Currently, developing these skills of data science statistics to manage your data are important because:
- Big Data: every day the generation and collection of data in every field, including the environmental field, are huge in volume, and it is still growing with time. The amount and complexity of data generated need competent professionals to assess and interpret it effectively.
- Career Improvement: the field of data science and statistics are some of the most popular in the market today, so environmental professionals with these skills are one step ahead.
In summary, the course presents explanations and examples, as well as hands-on exercises for the implementation of Data Science and Statistics to be used in the Environmental Data Management activities of professionals.
Goals
Learn good practices of Environmental Data Management
What is Exploratory Data Analysis
Environmental data characteristics
Software/tools to explore environmental data
How to treat, clean organize environmental data
Graphs for data visualization and visual storytelling
Prerequisites
No specific prior knowledge required
Familiarity with statistics concepts is helpful
Curriculum
Check out the detailed breakdown of what’s inside the course
Introduction
5 Lectures
- Environmental Data Management 06:58 06:58
- Your First Task
- Statistics & Data Science 07:32 07:32
- Data Management Languages and Tools 03:42 03:42
- Statistics & Data Science Glossary
Organizing and Cleaning Data: Data Wrangling
5 Lectures
Exploratory Data Analysis: Getting to Know your Data
4 Lectures
Exploratory Data Analysis: Summary Statistics
7 Lectures
Exploratory Data Analysis: Visual Methods
8 Lectures
Instructor Details
Environmental Academy - AMA
The Environmental Academy is a project designed to bring relevant knowledge to environmental professionals, sharing practical information for those who work with the subject.
The project is an undertaking of Mateus Amorim, an environmental engineer, entrepreneur, and experienced trainer who has been working in the environmental management field for over 8 years.
He has experience in quality management systems, licensing, environmental project execution, and environmental pollution control.
He acts objectively in his projects, seeking to be practical, direct, and efficient in his activities. He is enthusiastic about development and continuous improvement, bringing the characteristics of objectivity and practicality to the growth of himself, his team, and his students.
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