Explore the core principles of decision science and its significance in modern business. Develop a foundational understanding of decision modeling techniques, problem-framing strategies, and the ethical considerations surrounding data-driven decision-making.

Introduction to Decision Science

Descriptive versus prescriptive analytics

Common biases in Decision Science

Ordering the steps in decision-making

Introduction to decision-making

Decision-making techniques

Decision-making frameworks

Framing and problem definition

Project stakeholders

Business problem context

Problem framing techniques

Ethical considerations in decision-making

Spotting fairness failures

Decision Science Ethics

Foundations of Decision Science

Gain practical skills in implementing decision science projects efficiently. Learn to define success criteria, assess data sources, perform quality checks, select relevant features, prioritize models, and foster a data-driven culture within your organization.

Defining success

Minimum Viable Product

Databases and quality checks

Location, location… database?

Which data delivers?

Model prioritization

Baseline before brilliance

Tweak the data, boost the model

Tracking performance

Matching metrics

Metrics to meaning

Efficient Decision Science

Discover the diverse applications of decision science across various industries, including marketing, finance, and operations. Master effective communication strategies for presenting data-driven insights to stakeholders and build consensus around data-informed decisions. Explore future trends and advancements in the field.

Decision science in business

What counts as decision science?

Six steps

Scoping for success

Communicating data-driven insights

Storytelling success

Options

Technical specialists

Executives

Operational staff

Speak their language

Building a data-driven culture

Culture check

Five steps

Step by step

The future of decision science

Future-forward or falling behind?

AI in Decision-Making

Integration of Behavioral Economics

Big Data Analytics

New frontiers

Wrap up

Decision Science in Action

Are you working in data science and looking to make a real impact on business decisions? This course is here to help!
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You’ll learn how to align data with strategic goals by mastering key decision science concepts and frameworks. Gain the skills to efficiently solve problems, communicate insights effectively, and foster a data-driven culture within your organization.

<h2>Learn Decision Science Fundamentals!</h2>
<p>Dive into the fundamentals of decision science, exploring core principles, methodologies, and real-world applications. You will develop a strong understanding of decision modeling, problem framing, and the ethical considerations involved in data-driven decision-making.</p>

<h2>Implement Like a Pro</h2>
<p>You will discover how to efficiently implement decision science projects, from defining clear success criteria to building a data-driven culture within your organization. The next chapter will equip you with practical techniques for data source assessment, quality checks, feature selection, and model prioritization.</p>

<h2>Master Decision Science Applications</h2>
<p>Expand your knowledge by exploring the diverse applications of decision science across various industries, including marketing, finance, and operations. You will practice and develop effective communication strategies to present data-driven insights to stakeholders and build consensus around data-informed decisions.</p>

<h2>Explore Future Trends</h2>
<p>Stay ahead of the curve by examining emerging trends and advancements in decision science. You will gain valuable insights into the integration of artificial intelligence (AI) in decision-making, the power of big data analytics, and the evolving role of human-in-the-loop decision-making.</p>


Solidify your decision science skills by designing data-informed frameworks and implementing efficient solutions. 

Demystifying Decision Science

Fundamentos de la ciencia de la decisión

A bank uses AI algorithms to optimize pricing strategies for loans, balancing competitiveness and profitability

A pharmaceutical company uses AI to analyze clinical trial data and identify potential side effects of new drugs more efficiently

Loss aversion principles are applied to retirement savings plans, encouraging employees to contribute by framing missed contributions as potential losses

Customer surveys are designed to reduce bias and capture more accurate responses on satisfaction

Social media sentiment is analyzed to understand reactions to a bank’s new product launch

A national healthcare dataset is used to uncover patterns in disease progression across millions of patients

New frontiers

Demystifying Decision Science

Ejercicio interactivo práctico