Analytics the Right Way

via Coursera

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Provider

Coursera
Pricing

Paid Course
Languages

English
Certificate

Certificate Available
Effort

11 weeks, 1 hour a week
Sessions

Self-Paced
Level

Intermediate
Subtitles

English

Found in

Overview

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A practical and strategic guide that equips leaders to make smarter decisions using data. Covers uncertainty, KPIs, hypothesis testing, and operational enablement. This resource equips learners with the tools to understand how data drives business decisions, from foundational concepts to real-world application. It emphasizes structured frameworks for data usage, critical thinking, and effective communication. Designed for leaders and professionals, it bridges the gap between technical and business teams. This resource is ideal for business leaders, analysts, and decision-makers seeking to enhance their data literacy. Readers should have basic business knowledge and a curiosity about analytics. No advanced math or programming is required, but logical thinking and a strategic mindset are beneficial. This course combines decision science with data analytics to help business leaders make informed choices using structured methodologies, hypothesis validation, and performance metrics. This course is based on Analytics the Right Way, by Tim Wilson and Joe Sutherland. From Analytics the Right Way Copyright © 2025 John Wiley & Sons Inc. All rights reserved, including rights for text and data mining and training of artificial intelligence technologies or similar technologies. Used by arrangement with John Wiley & Sons, Inc.

Syllabus

Is This Course Right for You?
- This module helps learners assess whether a data-driven course aligns with their needs by identifying gaps in data usage, evaluating tools for business value, and improving communication with data teams to support better decision-making.
How We Got Here
- This module explores the limitations and misconceptions surrounding data usage, focusing on how data is often influenced by bias, randomness, and practical constraints. Learners will gain an understanding of how to critically evaluate data and recognize the importance of strategic decision-making. The content highlights real-world examples to illustrate how data can shape but also mislead business and scientific decisions.
Making Decisions with Data: Causality and Uncertainty
- This module explores the principles of decision-making using data, with a focus on causality, counterfactual thinking, and managing uncertainty. Learners will develop frameworks to evaluate decisions and minimize regret in complex situations. The content emphasizes practical applications of data analysis in real-world scenarios.
A Structured Approach to Using Data
- This module explores structured frameworks for using data effectively in business decisions, focusing on how data integration works in CRM and ERP systems. It addresses common misconceptions about data and highlights the importance of structured approaches in real-world applications.
Making Decisions Through Performance Measurement
- This module covers the fundamentals of performance measurement, focusing on key performance indicators (KPIs), outcomes versus outputs, and techniques for setting realistic targets. Learners will gain skills in evaluating data-driven decisions and designing effective dashboards for performance tracking.
Making Decisions Through Hypothesis Validation
- This module explores the process of hypothesis validation as a strategic tool for making informed, forward-looking decisions. It teaches learners how to articulate, test, and refine hypotheses to drive actionable insights and improve decision-making in dynamic environments.
Hypothesis Validation with New Evidence
- This module explores the role of evidence in hypothesis validation, examining different types of evidence and their strengths and limitations. Learners will gain insights into how to critically assess data, recognize biases, and make informed decisions based on evidence. The content also highlights the importance of balancing certainty with practical constraints in real-world scenarios.
Descriptive Evidence: Pitfalls and Solutions
- This module focuses on identifying common pitfalls in descriptive analysis, understanding the role of variables and units of analysis, and addressing biases and uncertainties in data. Learners will gain skills in evaluating historical data, recognizing omitted variable bias, and interpreting time-series and noisy data. The module equips students with the tools to improve the reliability of their data-driven insights.
Pitfalls and Solutions for Scientific Evidence
- This module equips learners with the skills to critically evaluate scientific evidence, identify common pitfalls such as selection bias and confounding variables, and understand how to design and interpret controlled experiments for reliable causal inferences.
Operational Enablement Using Data
- This module explores how data and analytics drive operational efficiency, reduce costs, and enhance coordination through structured processes and machine learning. It covers the application of data in business operations, the role of AI in automating decision-making, and the balance between machine and human involvement in complex workflows.
Bringing It All Together
- This module explores how hypothesis validation connects performance measurement with operational enablement, highlighting the importance of data-driven decision-making and organizational effectiveness. Learners will understand how these elements interact in real-world scenarios and how to apply them in business contexts. The module also addresses the role of technology in supporting these processes.