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Chantal Labbé and Marc Fredette

Do big data and UX speak to you? This MOOC will give you the methods and tools to analyze the whole spectrum of data we handle in UX, from qualitative user research and quantitative user testing data analysis to big data Web analytics.

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Do big data and UX speak to you? This MOOC will give you the methods and tools to analyze the whole spectrum of data we handle in UX, from qualitative user research and quantitative user testing data analysis to big data Web analytics.

You will be able to leverage insights from this data to make empirically-based recommendations towards frictionless, optimal user experiences.

Taught by award-winning faculty members, this course is an introduction to the statistical methods and tools useful to UX data analysis. No need to be a math whiz, this course was designed to be accessible to everyone.

No previous stats knowledge is needed. You will need to use MS Excel to do the final exam and complete the certification. Join us in the journey to unlock the insights of UX data, through the UX Design and Evaluation MicroMasters, or as an individual course.

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What's inside

Learning objectives

  • Descriptive statistics
  • Study design
  • Bias sources
  • Hypothesis testing
  • Means of comparison
  • Two-way anova
  • Applying it all to ux

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Examines a variety of useful statistical methods and tools for analyzing user experience
Taught by faculty members who are leaders in this field
Explores empirical methods that yield substantial results
Does not require previous knowledge in statistics
Applicable to professionals in user experience, design, and product management

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Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in UX Data Analysis with these activities:
Review Concepts: From Data Collection to Interpretation
Revisiting the steps from data collection to interpretation will strengthen your grasp of the overall process and prepare you for the course content.
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  • Review the various methods of data collection in UX.
  • Discuss best practices for data collection.
  • Describe the different statistical methods used for data analysis.
  • Explain how to interpret data analysis results.
Tableau Tutorial
Review the basics of Tableau to prepare for the course content on data visualization.
Show steps
  • Find an online tutorial on Tableau basics.
  • Follow the steps in the tutorial to create a simple dashboard.
  • Experiment with different data sources and visualizations.
Review Statistical Distributions
Review and practice working with different statistical distributions commonly used in UX, e.g., normal, binomial, Poisson.
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  • Identify the key characteristics of different statistical distributions.
  • Practice calculating probabilities and percentiles for different distributions.
  • Apply statistical distributions to solve real-world UX problems.
Eight other activities
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Peer Discussion: Statistical Methods in UX
Engaging in discussions with peers about statistical methods in UX will reinforce and broaden your understanding.
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  • Identify a topic for discussion related to statistical methods in UX.
  • Prepare talking points and examples.
  • Organize and lead a peer discussion session.
  • Facilitate a respectful and engaging discussion.
  • Summarize the key points and insights gained from the discussion.
Hypothesis Testing Practice
Reinforce your understanding of hypothesis testing by completing practice exercises.
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  • Review the lecture notes and textbook chapter on hypothesis testing.
  • Find online practice problems or use the ones provided in the course materials.
  • Solve the practice problems and check your answers against the provided solutions.
Hypothesis Testing Practice
Solving practice problems helps solidify the theoretical concepts covered in the course, particularly in challenging areas like hypothesis testing.
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  • Review the concepts of hypothesis testing, null and alternative hypotheses, and statistical significance.
  • Work through a variety of practice problems.
  • Interpret the results of hypothesis tests and draw conclusions.
Data Analysis Study Group
Collaborate with peers to discuss and analyze UX data.
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  • Form a study group with 2-3 other students.
  • Choose a dataset to analyze together.
  • Meet regularly to discuss your findings and insights.
UX Data Analysis with Python Tutorial
Python is a common language for UX data analysis, and this tutorial will introduce you to the process of analyzing UX data using Python.
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  • Install Python and necessary libraries.
  • Read, clean, and explore UX data using Python.
  • Perform statistical analysis using Python.
  • Visualize UX data using Python.
Case Study: Analyzing UX Data to Improve User Experience
Applying UX data analysis techniques to a real-world case study will help you synthesize your learnings and apply them practically.
Show steps
  • Collect UX data from a live project or a publicly available dataset.
  • Analyze the data using techniques learned in the course.
  • Identify and prioritize areas for improvement in the user experience.
  • Present your findings and recommendations in a written report.
UX Data Analysis Project
Building a UX data analysis project will provide hands-on experience and allow for experimentation with different techniques.
Show steps
  • Define the project scope and goals.
  • Design a plan for data collection and analysis.
  • Collect and analyze data.
  • Develop recommendations based on the analysis.
  • Present the findings and recommendations.
UX Design Case Study
Apply your knowledge of UX data analysis to design a user experience for a real-world problem.
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  • Identify a specific problem or area for improvement in a user experience.
  • Conduct user research to gather data on the problem.
  • Analyze the data and identify insights that can be used to improve the user experience.
  • Design and prototype a solution to the problem.
  • Test your solution with users and gather feedback.
  • Write a case study that documents your process and findings.

Career center

Learners who complete UX Data Analysis will develop knowledge and skills that may be useful to these careers:
User Experience Researcher
User Experience Researchers lead the design thinking process, which focuses on constant iteration and an evidence-based approach to improving user experience. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a User Experience Researcher.
User Researcher
User Researchers gather and analyze data about users to help businesses understand their needs and improve their products and services. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a User Researcher.
UX Designer
UX Designers design and evaluate user interfaces to make them easy to use and enjoyable. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a UX Designer.
Product Manager
Product Managers are responsible for the overall success of a product, from conception to launch and beyond. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a Product Manager.
Data Analyst
Data Analysts collect, analyze, and interpret data to help businesses make informed decisions. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a Data Analyst.
Market Researcher
Market Researchers conduct surveys, focus groups, and other research to gather data about consumers and markets. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a Market Researcher.
Business Analyst
Business Analysts help businesses improve their performance by analyzing data and identifying opportunities for improvement. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a Business Analyst.
Quality Assurance Analyst
Quality Assurance Analysts test software and other products to ensure that they meet quality standards. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as a Quality Assurance Analyst.
Information Architect
Information Architects design and organize websites and other digital products to make them easy to use and find information. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as an Information Architect.
Interaction Designer
Interaction Designers design the way that users interact with products and services. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills can help you build a foundation for a successful career as an Interaction Designer.
Graphic designer
Graphic Designers create visual content for websites, print materials, and other media. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills may be useful for Graphic Designers who want to understand how users interact with visual content.
Web Developer
Web Developers design and develop websites and other web-based applications. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills may be useful for Web Developers who want to understand how users interact with websites.
Content Strategist
Content Strategists plan and manage the creation of content for websites, blogs, and other digital platforms. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills may be useful for Content Strategists who want to understand how users interact with content.
Social Media Manager
Social Media Managers create and manage social media content for businesses and organizations. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills may be useful for Social Media Managers who want to understand how users interact with social media content.
Technical Writer
Technical Writers create documentation for software, hardware, and other technical products. This course introduces the statistical methods and tools that are useful to UX data analysis, including descriptive statistics, study design, hypothesis testing, and more. These skills may be useful for Technical Writers who want to understand how users interact with technical documentation.

Reading list

We've selected 13 books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in UX Data Analysis.
Provides a practical guide to doing data science in the real world. It valuable resource for anyone who wants to learn more about how to use data to solve real-world problems.
Provides a good overview of the basics of data science, including data mining and data analytics. It good resource for anyone who wants to learn more about how data can be used to improve business outcomes.
Provides a practical introduction to data visualization. It valuable resource for anyone who wants to learn more about how to create effective visualizations of data.
Provides a comprehensive overview of deep learning. It valuable resource for anyone who wants to learn more about this rapidly growing field.
Provides a comprehensive overview of the statistical methods used in data mining and machine learning. It valuable resource for anyone who wants to learn more about these topics.
Provides a comprehensive overview of computer vision. It valuable resource for anyone who wants to learn more about this important area of artificial intelligence.
Provides a comprehensive overview of natural language processing. It valuable resource for anyone who wants to learn more about this important area of artificial intelligence.
Provides a comprehensive overview of the big data revolution. It valuable resource for anyone who wants to learn more about how big data is changing the world.
Provides a comprehensive overview of reinforcement learning. It valuable resource for anyone who wants to learn more about this important area of machine learning.
Provides a gentle introduction to machine learning. It valuable resource for anyone who wants to learn more about the basics of machine learning.
Provides a practical guide to UX research methods, including how to design and conduct user studies, analyze data, and present findings.

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