Data Science and Statistics for Environmental Professionals

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

4.20 (28 reviews)

Data Science and Statistics for Environmental Professionals


2 hours


Jul 2021

Last Update
Regular Price

What you will 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


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



  • 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.



Environmental Data Management

Statistics & Data Science

Data Management Languages and Tools

Statistics & Data Science Glossary

Organizing and Cleaning Data: Data Wrangling

Organizing and Cleaning Data: Data Wrangling

Missing Values, Outliers & Non-Detects - Part 1

Missing Values, Outliers & Non-Detects - Part 2

Missing Values, Outliers & Non-Detects - Part 3


Exploratory Data Analysis: Getting to Know your Data

Exploratory Data Analysis - EDA

Types of Data

Characteristics of Environmental Data


Exploratory Data Analysis: Summary Statistics

Measures of Location

Measures of Variability

Measures of Shape

Summary Statistics Pocketbook | Case Study Exercise


Exploratory Data Analysis: Visual Methods



Graphical Methods Pocketbook | Case Study Exercise

Time Series Plots


Ioanna22 August 2021

This course was really helpful. I gained knowledge about basic statistics methods and programming languages used in the management of environmental data as I expected. I totally recommend it.

Sunita23 June 2021

The course is very informative and designed well for environmental professionals. The inferences drawn using such data analysis will yield better planning of environmental impacts


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