Data Science and Statistics for Environmental Professionals

Basic course to learn environmental data management: solid waste, air pollution, effluent discharge, groundwater, etc.

Are you an environmental professional interested in improving your Data Management skills?

What you’ll learn

  • 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.

Course Content

  • Introduction –> 4 lectures • 18min.
  • Organizing and Cleaning Data: Data Wrangling –> 5 lectures • 35min.
  • Exploratory Data Analysis: Getting to Know your Data –> 4 lectures • 27min.
  • Exploratory Data Analysis: Summary Statistics –> 5 lectures • 25min.
  • Exploratory Data Analysis: Visual Methods –> 4 lectures • 28min.

Data Science and Statistics for Environmental Professionals

Requirements

  • No specific prior knowledge required.
  • Familiarity with statistics concepts is helpful.

Are you an environmental professional interested in improving your Data Management skills?


This course explains the importance to understand Data Science and Statistics concepts for environmental data management and help 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 the Data Science and Statistics and:

  • You want to learn the basics 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 analyzes 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 fundamentals 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 details, on the other hand, throughout the course the students will be stimulate to use the tool(s) that they fell more confortable 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.