[Lecture 5] Data Analysis, Interpretation, and Presentation


References

(1) Jenny Preece, Helen Sharp, and Yvonne Rogers (2019) Interaction Design: Beyond Human-Computer Interaction, 5th Edition, Wiley, Chapter 9.
(2) Goodwin, K., & Cooper, A. (2009) Designing for the Digital Age: How to Create Human-Centered Products and Services, Wiley, Chapter 10.

How video games can help with medical research with Hugo Spiers | WIRED Health

AI summarised,

On the latest lecture, we discussed the crucial steps in data analysis, interpretation, and presentation. It is undeniable that data has become an essential part of decision-making in various fields, mainly in the technology and business industry. However, analyzing and interpreting data can be complicated.

In data analysis, you should first ensure the data’s accuracy, consistency, and completeness, as any errors or inconsistencies can significantly affect the results. Afterward, you must identify patterns, trends, and relationships among the data to draw insights and conclusions.

Interpreting data involves making sense of the insights and conclusions drawn from the data analysis. It is vital to provide context, consider various perspectives, and distinguish between correlation and causation to ensure accurate interpretations.

Finally, data presentation is equally essential in ensuring that data becomes engaging and understandable for the audiences. It involves developing effective visualizations and storytelling techniques to communicate insights and conclusions effectively.

In conclusion, data analysis, interpretation, and presentation are critical aspects of utilizing data to make informed and effective decisions. By following the steps mentioned above, you can ensure data accuracy, interpret data effectively, and present essential information in an engaging and understandable way.

References:
Jenny Preece, Helen Sharp, and Yvonne Rogers (2019) Interaction Design: Beyond Human-Computer Interaction, 5th Edition, Wiley, Chapter 9.
Goodwin, K., & Cooper, A. (2009) Designing for the Digital Age: How to Create Human-Centered Products and Services, Wiley, Chapter 10.
How video games can help with medical research with Hugo Spiers | WIRED Health.

[Lecture 4] Data Gathering


References

Jenny Preece, Helen Sharp, and Yvonne Rogers (2019) Interaction Design: Beyond Human-Computer Interaction, 5th Edition, Wiley, Chapter 7.

AI Paragraph (experimental feature from wordpress.com)

In Lecture 4, we delved into the fascinating topic of data gathering. As interaction designers, we must continuously gather information about our target audience, their needs, and their behaviors to create meaningful solutions.

There are various methods that we can use to gather data, from surveys and interviews to observations and analytics. Each method has its strengths and weaknesses, and we must carefully consider our research goals when choosing the most appropriate method.

One crucial aspect of data gathering is ensuring the data we collect is accurate and reliable. We must take steps to avoid biases, such as sampling errors or leading questions, that may skew our findings. Additionally, we need to analyze and interpret the data collected effectively to draw meaningful insights that inform our design decisions.

Data gathering is a continuous process throughout the design cycle, and we need to be open to revisiting our research and making adjustments as we go. By doing so, we can create truly user-centered experiences that meet the needs of our target audience.

As we conclude this lecture, let’s keep in mind the wise words of Jenny Preece, Helen Sharp, and Yvonne Rogers. “Data gathering should be viewed as a collaborative, iterative process in which participants are given the opportunity to contribute through their feedback and experiences.” With this in mind, let us strive to create user-centered designs that positively impact people’s lives.