Agile Analytics

  • 4.8
Approx. 15 hours to complete

Course Summary

Learn how to use agile methodologies to improve your analytics projects with this course from the University of Virginia's Darden School of Business.

Key Learning Points

  • Understand how agile principles can be applied to analytics projects
  • Learn how to prioritize and manage tasks using agile techniques
  • Develop a data-driven approach to decision making

Related Topics for further study


Learning Outcomes

  • Apply agile principles to analytics projects
  • Develop a data-driven approach to decision making
  • Effectively manage and prioritize tasks

Prerequisites or good to have knowledge before taking this course

  • Basic knowledge of analytics
  • Familiarity with project management

Course Difficulty Level

Intermediate

Course Format

  • Online self-paced
  • Video lectures
  • Case studies

Similar Courses

  • Agile Project Management
  • Data-Driven Decision Making
  • Agile Leadership

Related Education Paths


Notable People in This Field

  • Agile Alliance
  • Scrum Alliance

Related Books

Description

Few capabilities focus agile like a strong analytics program. Such a program determines where a team should focus from one agile iteration (sprint) to the next. Successful analytics are rarely hard to understand and are often startling in their clarity. In this course, developed at the Darden School of Business at the University of Virginia, you'll learn how to build a strong analytics infrastructure for your team, integrating it with the core of your drive to value.

Knowledge

  • How to naturally, habitually tie your team’s work to actionable analytics that help you drive to user value.
  • How to pair your hypotheses on customer personas and problem with analytics.
  • How to test propositions (a la Lean Startup) so you don’t build features no one wants.
  • How to instrument actionable observation into everything you build (a la Lean UX).

Outline

  • Introduction and Customer Analytics
  • Purposeful Iteration
  • Review: Hypothesis-Driven Development
  • Agile Analytics at HVAC in a Hurry
  • Jobs To Be Done at HVAC in a Hurry
  • Getting Outside the Building With Ivan
  • Focal Point: The User Journey
  • Introducing Enable Quiz
  • Outlining the User Journey
  • Outlining with Pirate Metrics
  • Designing User Habits: The Hook Framework
  • Mapping Analytics: Trent the Technician
  • Mapping Analytics: Ivan the Inside Salesperson
  • Your Analytics Portfolio
  • Course Overview & Requirements
  • User Journeys and Pirate Metrics
  • User Habits and Mapping Analytics
  • Introduction and Customer Analytics
  • Demand Analytics
  • Lean Startup and the Demand Hypothesis
  • Testing Motivation with MVPs
  • Designing Experiments
  • Experiment Design with MVPs
  • Five Experiment Charters
  • The Fake Feature Test
  • Testing Features: Running the Experiment
  • Testing Funnels
  • Testing Cohorts
  • Experiment Design: Testing a Coding Course for Designers & Managers
  • Experiment Execution: Testing a Coding Course for Designers & Managers
  • Interview: Laura Klein on Practice of Lean UX
  • Testing Motivations with MVPs
  • Experiments
  • Testing Demand and Experiment Patterns
  • UX Analytics
  • Analytics All the Time
  • Qualitative Usability Testing
  • The Test You Already Have
  • Pairing Your User Stories with Analytics: Trent the Technician
  • Pairing Your User Stories with Analytics: Ivan the Inside Salesperson
  • Analyzing Dependent Variables with Google Analytics
  • Google Analytics: The Littlest Overview
  • From Design to Code: Trent the Technician
  • From Code to Analytics: Trent the Technician
  • A/B Testing
  • Designing, Coding, and Testing: Ivan the Inside Salesperson
  • Testing Analytics
  • User Stories & Analytics
  • Qualitative and Quantitative Analytics
  • Analytics and Data Science
  • What is Data Science?
  • Facilitating Collaboration with Your Data Science Team
  • Interview: Drew Conway on Data Science
  • Interview: Drew Conway’s Data Science Journey
  • Four Types of Data Executions
  • Interview: Casey Lichtendahl: Data Science and You
  • Interview: Casey Lichtendahl: Closer Look at the Work of Data Science
  • Interview: Casey Lichtendahl: Data at Rest vs. Data in Motion
  • Data Science IRL: Intro to the Casino Jack Case
  • Data Science IRL: Data Wrangling and Exploratory Analysis
  • Data Science IRL: Testing Hypotheses and Designing Interventions
  • Course Close
  • Data Science
  • Data Executions
  • Analytics and Data Science

Summary of User Reviews

Discover the Agile Analytics course by UVA Darden on Coursera, designed to equip learners with the skills and tools to build agile analytics projects. Students have praised the course for its practical approach to learning, enabling them to apply agile analytics to real-world scenarios.

Key Aspect Users Liked About This Course

Practical approach to learning

Pros from User Reviews

  • Instructors provide clear explanations and examples
  • Course content is well-structured and easy to follow
  • Assignments and quizzes help reinforce concepts
  • Real-world case studies provide valuable insights
  • Flexible schedule allows learners to study at their own pace

Cons from User Reviews

  • Some learners may find the course material too basic
  • Limited interaction with instructors and other learners
  • No hands-on experience with specific tools or software
  • Course may not be suitable for those with advanced analytics knowledge
  • Some learners may prefer a more theoretical approach to learning
English
Available now
Approx. 15 hours to complete
Alex Cowan
University of Virginia
Coursera

Instructor

Alex Cowan

  • 4.8 Raiting
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