Empathy and Data in Risk Management (Coursera)

Empathy and Data in Risk Management (Coursera)

Risk Management and Innovation develops your ability to conduct empathy-driven and data-driven analysis in the domain of risk management. This course focuses on the process of managing enterprise risk, in which understanding both data and stakeholder enriches each step.

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

The course introduces the three lines of defense, tools to identify and assess risks, risk responses, key risk indicators, and risk reporting. You will use tools like risk statements, bowtie analysis, matrices, and heat maps to apply these concepts, and use data visualization in Tableau to practice applying data analysis to better manage risks.

Syllabus

WEEK 1
Course Orientation
Risk Management and Innovation develops your ability to conduct empathy-driven and data-driven analysis in the domain of risk management. This course focuses on the process of managing enterprise risk, in which understanding both data and stakeholder enriches each step. The course introduces the three lines of defense, tools to identify and assess risks, risk responses, key risk indicators, and risk reporting. You will use tools like risk statements, bowtie analysis, matrices, and heat maps to apply these concepts, and use data visualization in Tableau to practice applying data analysis to better manage risks.
Module 1: There’s Risk, Now What Should We Do about It?
In this module, you will examine risk management in organizations, with a focus on understanding stakeholders, such as customers, employees, and society at large. The course introduces frameworks to identify, assess, respond to, and report on risk within an organization, and tools to link these risk management activities to data analysis and stakeholder needs. This course illustrates these concepts in two important areas: 1) Cybersecurity, and 2) diversity, equity, and inclusion. Finally, the course provides practice with data visualization tools.

WEEK 2
Module 2: How Bad Could It Be? Issues in Measuring Risk
In this module, you will examine ways to measure risk. This module introduces key risk indicators and the tradeoffs between using qualitative or quantitative indicators. You will learn how to use bowtie analysis to identify measures of risk. You will also transition from risk measurement to risk assessment and different approaches to define risk likelihood and risk magnitude.

WEEK 3
Module 3: Communicating Risk
In this module, you will explore artificial intelligence, algorithms, and risk management. This module will introduce the basic types of algorithms and the special problems represented by the use of algorithms in organizations. You will discuss psychological issues in using inputs from algorithms, including situations leading to over-utilization or underutilization of algorithmic input.

WEEK 4
Module 4: Risk Management in a Changing World
In this module, you will examine risk reporting and monitoring. This module discusses the use of heat maps and risk dashboards to communicate risk information to stakeholders. You will also discuss the importance of risk monitoring and ways organizations evaluate how risks change. Finally, you will examine common principles of persuasion as a way to enhance the effectiveness of communication.

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

Related Courses

Use Tableau for your Data Science Workflow (Coursera) Coursera
Edureka

Use Tableau for your Data Science Workflow (Coursera)

Learn Tableau fundamentals, advanced visualizations, and integration with data science tools. Elevate your skills in creating impactful dashboards. Enroll now for hands-on experience and master the art of data storytelling. Unlock the potential for advanced analytics, exploring correlations and trends within your data. Build interactive dashboards that tell compelling data stories, utilizing filters, parameters, and actions for user engagement.

Nov 30th 2026
1 Week
Big Data Analysis Deep Dive (Coursera) Coursera
Alibaba Cloud Academy

Big Data Analysis Deep Dive (Coursera)

The job market for architects, engineers, and analytics professionals with Big Data expertise continues to increase. The Academy’s Big Data Career path focuses on the fundamental tools and techniques needed to pursue a career in Big Data. This course includes: data processing with python, writing and reading SQL queries, transmitting data with MaxCompute, analyzing data with Quick BI, using Hive, Hadoop, and spark on E-MapReduce, and how to visualize data with data dashboards. Work through our course material, learn different aspects of the Big Data field, and get certified as a Big Data Professional!

Nov 23rd 2026
5-12 Weeks
Healthcare Information Design and Visualizations (Coursera) Coursera
Northeastern University

Healthcare Information Design and Visualizations (Coursera)

Introduces processes and design principles for creating meaningful displays of information that support effective business decision-making. Studies how to collect and process data; create visualizations (both static and interactive); and use them to provide insight into a problem, situation, or opportunity. Introduces methods to critique visualizations along with ways to answer the elusive question: “What makes a visualization effective?”

Nov 30th 2026
4 Weeks
Data intelligence for businesses and managers (Coursera) Coursera
Institut Mines-Telecom

Data intelligence for businesses and managers (Coursera)

With the proliferation of connected objects (computers, tablets, watches, etc.), huge masses of data are generated every second. This Big Data has led to the emergence of a data economy, where data is the main source of competitive advantage for companies. In this sense, data and its processing tools have become a strategic priority for companies, and the main gas pedal of their digital transformation.

Nov 30th 2026
5-12 Weeks
Python Data Visualization (Coursera) Coursera
Rice University

Python Data Visualization (Coursera)

This if the final course in the specialization which builds upon the knowledge learned in Python Programming Essentials, Python Data Representations, and Python Data Analysis. We will learn how to install external packages for use within Python, acquire data from sources on the Web, and then we will clean, process, analyze, and visualize that data. This course will combine the skills learned throughout the specialization to enable you to write interesting, practical, and useful programs.

Nov 27th 2026
4 Weeks
Finance for Non-Financial Managers (Coursera) Coursera
Emory University

Finance for Non-Financial Managers (Coursera)

Finance is for “Non-financial Managers” who want to understand key financial principles and apply them in a real-world context. Over the course of the program window, you will work your way through a series of nine modules that move from understanding basic financial principles to applying financial analysis and ratios to drive decisions.

Nov 16th 2026
5-12 Weeks
Math for MBA and GMAT Prep (Coursera) Coursera
Emory University

Math for MBA and GMAT Prep (Coursera)

This course gives participants a basic understanding of statistics as they apply in business situations. A fair share of students considering MBA programs come from backgrounds that do not include a large amount of training in mathematics and statistics. Often, students find themselves at a disadvantage when they apply for or enroll in MBA programs. This course will give you the tools to understand how these business statistics are calculated for navigating the built-in formulas that are included in Excel, but also how to apply these formulas in an range of business settings and situations.

Nov 30th 2026
5-12 Weeks
Data Visualization with Python & R for Engineers (Coursera) Coursera
Northeastern University

Data Visualization with Python & R for Engineers (Coursera)

The primary objective of this course is to offer students an opportunity to learn how to use visualization tools and techniques for data exploration, knowledge discovery, data storytelling, and decision making in engineering, healthcare operations, manufacturing, and related applications. This course covers basics of data mining and visualization, and Python. It also introduces students to static visualization charts and techniques that reveal information, patterns, interactions.

Nov 23rd 2026
4 Weeks
From Excel to Power BI (Coursera) Coursera
Knowledge Accelerators

From Excel to Power BI (Coursera)

Are you using Excel to manage, analyze, and visualize your data? Would you like to do more? Perhaps you've considered Power BI as an alternative, but have been intimidated by the idea of working in an advanced environment. The fact is, many of the same tools and mechanisms exist across both these Microsoft products. This means Excel users are actually uniquely positioned to transition to data modeling and visualization in Power BI! Using methods that will feel familiar, you can learn to use Power BI to make data-driven business decisions using large volumes of data.

Nov 30th 2026
1 Week
Practical Python for AI Coding 2 (Coursera) Coursera
Korea Advanced Institute of Science and Technology - KAIST

Practical Python for AI Coding 2 (Coursera)

This course is for a complete novice of Python coding, so no prior knowledge or experience in software coding is required. This course selects, introduces and explains Python syntaxes, functions and libraries that were frequently used in AI coding. In addition, this course introduces vital syntaxes, and functions often used in AI coding and explains the complementary relationship among NumPy, Pandas and TensorFlow, so this course is helpful for even seasoned python users.

Nov 23rd 2026
5-12 Weeks
Meaningful Marketing Insights (Coursera) Coursera
Emory University

Meaningful Marketing Insights (Coursera)

With marketers are poised to be the largest users of data within the organization, there is a need to make sense of the variety of consumer data that the organization collects. Surveys, transaction histories and billing records can all provide insight into consumers’ future behavior, provided that they are interpreted correctly. In Introduction to Marketing Analytics, we introduce the tools that learners will need to convert raw data into marketing insights. The included exercises are conducted using Microsoft Excel, ensuring that learners will have the tools they need to extract information from the data available to them.

Nov 16th 2026
5-12 Weeks