Public Engagement for Scientists (Coursera)

Offered by Illinois Tech,
Public Engagement for Scientists (Coursera)

This course presents strategies for scientists to use when engaging a variety of audiences with scientific information. Students will learn to communicate their knowledge through both textual and visual strategies, as well as practice document preparation using appropriate formatting, style, and graphics.

Class Deals by MOOC List - Click here and see Coursera's Active Discounts, Deals, and Promo Codes.

Written assignments, discussion questions, and communication exercises will provide students with a better understanding of the relationship between scientists and diverse audiences, whether in the workplace, laboratory, or other environments.
Upon successful completion of this course, you will be able to:

  • Explain key strategies for communicating scientific information to a variety of audiences.
  • Analyze a communicative situation and its context to determine audience needs.
  • Design visual and textual forms of scientific communication that meet the needs of a diverse range of audiences.
  • Prepare documents using formatting, style, and graphics that are appropriate for the situation.

Software Requirements: R (preferably RStudio), document creation software (Microsoft Word, LaTeX, Google Docs, etc.)

Syllabus

Module 1: Scientists and Everyone Else: Key Communication Challenges
Welcome to Public Engagement for Scientists! Module 1 delves into the multifaceted role of scientists in society, highlighting the challenges and responsibilities in communicating scientific knowledge. It aims to equip students with a nuanced understanding of what constitutes scientific expertise, the interplay between science and the public sphere, and the inherent communication challenges faced by scientists today.

Module 2: Foundations I: Translating Tough Material
This module is designed to enhance students' skills in tailoring written communication effectively for diverse audiences and purposes. It focuses on the art of revising and editing texts to match the audience's vocabulary level, ensuring conciseness and precision, and utilizing visual elements like typesetting and color for enhanced comprehension. The course combines theoretical knowledge with practical exercises, enabling students to master the intricacies of impactful written communication.

Module 3: Foundations II: Data Visualization, Part 1
This module dives into the skills required for effective data management and visualization. It focuses on the foundational techniques of cleaning and structuring both quantitative and qualitative data, understanding and selecting appropriate types of data visualizations for different rhetorical purposes, and crafting meaningful captions, legends, titles, and labels to enhance the interpretability and impact of data presentations.

Module 4: Foundations II: Data Visualization, Part 2
This module is designed to provide students with an in-depth understanding of the interplay between human visual perception and data visualization, alongside the ethical considerations inherent in the field. It covers the psychological and physiological aspects of how the human eye processes visual information, particularly in the context of data presentation. Additionally, the module delves into the ethical dimensions of data visualization, highlighting potential pitfalls and the responsibilities of data communicators in representing information truthfully and responsibly. Students will learn to critically analyze visual data presentations, both in terms of how effectively they convey information and their adherence to ethical standards.

Module 5: Communicating Science to Researchers
This module is tailored to provide students with a comprehensive understanding of the objectives and expectations within the research community. It aims to demystify the purposes and methodologies behind key forms of scientific communication, namely research papers and technical reports. Students will explore the goals driving academic research, delve into the structure and function of research papers, and understand how these papers serve as critical tools for communicating complex information. The module also covers technical reports, explaining their purpose, structure, and how they differ from research papers in conveying detailed information. Through this module, students will develop a clearer perspective on the nuances of scientific communication and its role in advancing knowledge in the research community.

Module 6: Communicating Science to your Peers
This module focuses on the critical aspects of communication within a professional scientific setting. It is designed to equip students with the skills necessary to communicate effectively with peers, emphasizing the importance of concision and precision in delivering clear and impactful messages. The module delves into the key rhetorical features of internal documentation, illustrating how to effectively convey complex information in a manner that is easily comprehensible to team members. Additionally, students will learn the art of crafting effective messages for peer-to-peer communication, a vital skill for successful collaboration and project management.

Module 7: Communicating Science to other Professionals
This module is crafted to enhance students' understanding and skills in tailoring communication effectively based on audience needs. It emphasizes the strategic selection and reframing of information to ensure clarity, relevance, and engagement for various audiences. Students will learn how to identify and analyze the specific needs and expectations of their audience, guiding their decisions on what information to include and how to present it. The module also covers techniques for adjusting the complexity of communication, known as editing up or down, to suit the audience's level of understanding and interest.

Module 8: Communicating Science to the General Public
This module is designed to equip students with a comprehensive understanding of the multiple factors that influence public opinion and the essentials of constructing a persuasive public argument. It begins by exploring the various elements that shape public opinion, including cultural, social, political, and psychological factors. Students will then delve into the fundamentals of building a strong public argument, learning how to develop a logical structure, use rhetorical techniques, and engage the audience effectively. A key focus of the module is on the strategic use of data in supporting arguments, teaching students how to integrate statistical evidence to enhance persuasiveness and credibility. Additionally, the course examines the impact of document design on argument presentation, including the use of layout, typography, and visual elements to reinforce the message and facilitate comprehension.

Summative Course Assessment
This module contains the summative course assessment that has been designed to evaluate your understanding of the course material and assess your ability to apply the knowledge you have acquired throughout the course.

Go to Class
MOOC List is learner-supported. When you buy through links on our site, we may earn an affiliate commission.

Related Courses

What is Data Science? (Coursera) Coursera
IBM

What is Data Science? (Coursera)

The art of uncovering the insights and trends in data has been around since ancient times. The ancient Egyptians used census data to increase efficiency in tax collection and they accurately predicted the flooding of the Nile river every year. Since then, people working in data science have carved out a unique and distinct field for the work they do. This field is data science. In this course, we will meet some data science practitioners and we will get an overview of what data science is today.

Jun 22nd 2026
3 Weeks
Data Visualization (Coursera) Coursera
University of Illinois at Urbana-Champaign

Data Visualization (Coursera)

Learn the general concepts of data mining along with basic methodologies and applications. Then dive into one subfield in data mining: pattern discovery. Learn in-depth concepts, methods, and applications of pattern discovery in data mining. We will also introduce methods for pattern-based classification and some interesting applications of pattern discovery. This course provides you the opportunity to learn skills and content to practice and engage in scalable pattern discovery methods on massive transactional data, discuss pattern evaluation measures, and study methods for mining diverse kinds of patterns, sequential patterns, and sub-graph patterns.

Jun 22nd 2026
4 Weeks
Communicating Business Analytics Results (Coursera) Coursera
University of Colorado Boulder

Communicating Business Analytics Results (Coursera)

The analytical process does not end with models than can predict with accuracy or prescribe the best solution to business problems. Developing these models and gaining insights from data do not necessarily lead to successful implementations. This depends on the ability to communicate results to those who make decisions.

Jun 22nd 2026
4 Weeks
Getting started with TensorFlow 2 (Coursera) Coursera
Imperial College London

Getting started with TensorFlow 2 (Coursera)

Welcome to this course on Getting started with TensorFlow 2! In this course you will learn a complete end-to-end workflow for developing deep learning models with Tensorflow, from building, training, evaluating and predicting with models using the Sequential API, validating your models and including regularisation, implementing callbacks, and saving and loading models.

Jun 22nd 2026
5-12 Weeks
Introducción a Data Science: Programación Estadística con R (Coursera) Coursera
Universidad Nacional Autónoma de México

Introducción a Data Science: Programación Estadística con R (Coursera)

Este curso te proporcionará las bases del lenguaje de programación estadística R, la lengua franca de la estadística, el cual te permitirá escribir programas que lean, manipulen y analicen datos cuantitativos. Te explicaremos la instalación del lenguaje; también verás una introducción a los sistemas base de gráficos y al paquete para graficar ggplot2, para visualizar estos datos. Además también abordarás la utilización de uno de los IDEs más populares entre la comunidad de usuarios de R, llamado RStudio.

Jun 22nd 2026
4 Weeks
Regression Models (Coursera) Coursera
Johns Hopkins University

Regression Models (Coursera)

Linear models, as their name implies, relates an outcome to a set of predictors of interest using linear assumptions. Regression models, a subset of linear models, are the most important statistical analysis tool in a data scientist’s toolkit. This course covers regression analysis, least squares and inference using regression models.

Jun 22nd 2026
4 Weeks
The Data Scientist's Toolbox (Coursera) Coursera
Johns Hopkins University

The Data Scientist's Toolbox (Coursera)

In this course you will get an introduction to the main tools and ideas in the data scientist's toolbox. The course gives an overview of the data, questions, and tools that data analysts and data scientists work with. There are two components to this course. The first is a conceptual introduction to the ideas behind turning data into actionable knowledge. The second is a practical introduction to the tools that will be used in the program like version control, markdown, git, GitHub, R, and RStudio.

Jun 22nd 2026
4 Weeks
Statistical Inference (Coursera) Coursera
Johns Hopkins University

Statistical Inference (Coursera)

Statistical inference is the process of drawing conclusions about populations or scientific truths from data. There are many modes of performing inference including statistical modeling, data oriented strategies and explicit use of designs and randomization in analyses. Furthermore, there are broad theories (frequentists, Bayesian, likelihood, design based, …) and numerous complexities (missing data, observed and unobserved confounding, biases) for performing inference.

Jun 22nd 2026
4 Weeks
Introduction to Artificial Intelligence (AI) (Coursera) Coursera
IBM

Introduction to Artificial Intelligence (AI) (Coursera)

In this course you will learn what Artificial Intelligence (AI) is, explore use cases and applications of AI, understand AI concepts and terms like machine learning, deep learning and neural networks. You will be exposed to various issues and concerns surrounding AI such as ethics and bias, & jobs, and get advice from experts about learning and starting a career in AI. You will also demonstrate AI in action with a mini project.

Jun 22nd 2026
4 Weeks
Introduction to Genomic Technologies (Coursera) Coursera
Johns Hopkins University

Introduction to Genomic Technologies (Coursera)

This course introduces you to the basic biology of modern genomics and the experimental tools that we use to measure it. We'll introduce the Central Dogma of Molecular Biology and cover how next-generation sequencing can be used to measure DNA, RNA, and epigenetic patterns. You'll also get an introduction to the key concepts in computing and data science that you'll need to understand how data from next-generation sequencing experiments are generated and analyzed.

Jun 22nd 2026
4 Weeks