Module 2: Performance Analytics

.title[
# Module 2: Performance Analytics
]
.subtitle[
## EME6356: Learning & Web Analytics
]
.author[
### Dr. Bret Staudt Willet
]
.date[
### May 20, 2024
]

---

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**View the slides:**

[bretsw.com/eme6356-su24-module2](https://bretsw.com/eme6356-su24-module2)

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M323.8 34.8c-38.2-10.9-78.1 11.2-89 49.4l-5.7 20c-3.7 13-10.4 25-19.5 35l-51.3 56.4c-8.9 9.8-8.2 25 1.6 33.9s25 8.2 33.9-1.6l51.3-56.4c14.1-15.5 24.4-34 30.1-54.1l5.7-20c3.6-12.7 16.9-20.1 29.7-16.5s20.1 16.9 16.5 29.7l-5.7 20c-5.7 19.9-14.7 38.7-26.6 55.5c-5.2 7.3-5.8 16.9-1.7 24.9s12.3 13 21.3 13L448 224c8.8 0 16 7.2 16 16c0 6.8-4.3 12.7-10.4 15c-7.4 2.8-13 9-14.9 16.7s.1 15.8 5.3 21.7c2.5 2.8 4 6.5 4 10.6c0 7.8-5.6 14.3-13 15.7c-8.2 1.6-15.1 7.3-18 15.1s-1.6 16.7 3.6 23.3c2.1 2.7 3.4 6.1 3.4 9.9c0 6.7-4.2 12.6-10.2 14.9c-11.5 4.5-17.7 16.9-14.4 28.8c.4 1.3 .6 2.8 .6 4.3c0 8.8-7.2 16-16 16H286.5c-12.6 0-25-3.7-35.5-10.7l-61.7-41.1c-11-7.4-25.9-4.4-33.3 6.7s-4.4 25.9 6.7 33.3l61.7 41.1c18.4 12.3 40 18.8 62.1 18.8H384c34.7 0 62.9-27.6 64-62c14.6-11.7 24-29.7 24-50c0-4.5-.5-8.8-1.3-13c15.4-11.7 25.3-30.2 25.3-51c0-6.5-1-12.8-2.8-18.7C504.8 273.7 512 257.7 512 240c0-35.3-28.6-64-64-64l-92.3 0c4.7-10.4 8.7-21.2 11.8-32.2l5.7-20c10.9-38.2-11.2-78.1-49.4-89zM32 192c-17.7 0-32 14.3-32 32V448c0 17.7 14.3 32 32 32H96c17.7 0 32-14.3 32-32V224c0-17.7-14.3-32-32-32H32z"/></svg> <br><br> Looking Back at Module 1

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### The three Vs of big data (Russom, 2011, p. 6)

---

**Opportunities:**

- Help decision-making

- Treat patients

- Monitor parking

- Prepare stock in preparation of hurricanes

- Monitor students’ self-regulated learning and engagement

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M323.8 477.2c-38.2 10.9-78.1-11.2-89-49.4l-5.7-20c-3.7-13-10.4-25-19.5-35l-51.3-56.4c-8.9-9.8-8.2-25 1.6-33.9s25-8.2 33.9 1.6l51.3 56.4c14.1 15.5 24.4 34 30.1 54.1l5.7 20c3.6 12.7 16.9 20.1 29.7 16.5s20.1-16.9 16.5-29.7l-5.7-20c-5.7-19.9-14.7-38.7-26.6-55.5c-5.2-7.3-5.8-16.9-1.7-24.9s12.3-13 21.3-13L448 288c8.8 0 16-7.2 16-16c0-6.8-4.3-12.7-10.4-15c-7.4-2.8-13-9-14.9-16.7s.1-15.8 5.3-21.7c2.5-2.8 4-6.5 4-10.6c0-7.8-5.6-14.3-13-15.7c-8.2-1.6-15.1-7.3-18-15.2s-1.6-16.7 3.6-23.3c2.1-2.7 3.4-6.1 3.4-9.9c0-6.7-4.2-12.6-10.2-14.9c-11.5-4.5-17.7-16.9-14.4-28.8c.4-1.3 .6-2.8 .6-4.3c0-8.8-7.2-16-16-16H286.5c-12.6 0-25 3.7-35.5 10.7l-61.7 41.1c-11 7.4-25.9 4.4-33.3-6.7s-4.4-25.9 6.7-33.3l61.7-41.1c18.4-12.3 40-18.8 62.1-18.8H384c34.7 0 62.9 27.6 64 62c14.6 11.7 24 29.7 24 50c0 4.5-.5 8.8-1.3 13c15.4 11.7 25.3 30.2 25.3 51c0 6.5-1 12.8-2.8 18.7C504.8 238.3 512 254.3 512 272c0 35.3-28.6 64-64 64l-92.3 0c4.7 10.4 8.7 21.2 11.8 32.2l5.7 20c10.9 38.2-11.2 78.1-49.4 89zM32 384c-17.7 0-32-14.3-32-32V128c0-17.7 14.3-32 32-32H96c17.7 0 32 14.3 32 32V352c0 17.7-14.3 32-32 32H32z"/></svg> Pessimism about Analytics

**Challenges:**

- Misuse of big data, fake news, privacy issues, data security and other ethical considerations
  
--

- Surveillance issues

- Leaving digital traces and footprints in online spaces
  
--

- Analytics reports can be lengthy and difficulty to understand

- Lack of infrastructure for implementation of analytics in many contexts

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# <svg aria-hidden="true" role="img" viewBox="0 0 320 512" style="height:1em;width:0.62em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M80 160c0-35.3 28.7-64 64-64h32c35.3 0 64 28.7 64 64v3.6c0 21.8-11.1 42.1-29.4 53.8l-42.2 27.1c-25.2 16.2-40.4 44.1-40.4 74V320c0 17.7 14.3 32 32 32s32-14.3 32-32v-1.4c0-8.2 4.2-15.8 11-20.2l42.2-27.1c36.6-23.6 58.8-64.1 58.8-107.7V160c0-70.7-57.3-128-128-128H144C73.3 32 16 89.3 16 160c0 17.7 14.3 32 32 32s32-14.3 32-32zm80 320a40 40 0 1 0 0-80 40 40 0 1 0 0 80z"/></svg> <br><br> Module 1 <br> Final Thoughts?

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# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M256 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H448c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C363.4 52.6 312.1 32 256 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM132.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H64c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C52.6 148.6 32 199.9 32 256c0 10.9 .8 21.5 2.3 32H99.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM477.7 224H412.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H448c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C459.4 363.4 480 312.1 480 256c0-10.9-.8-21.5-2.3-32zM256 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H64c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C148.6 459.4 199.9 480 256 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> Recursive Fun

**Q: Who is Bret Staudt Willet?**

<hr>

Answer (May 2023):

Bret Staudt Willet is an American composer, producer, and multi-instrumentalist. He is best known for his work with artists like Kelly Clarkson, P!nk, Rihanna, and Justin Bieber. He has also collaborated with producers such as Dr. Luke, Max Martin, and Zedd. Willet is a three-time Grammy Award winner and has been nominated for multiple awards.

<hr>

Answer (May 2024):

As of my latest information update in October 2023, there doesn't appear to be widely available or notable public information about a person named Bret Staudt Willet. It's possible that he is not a public figure or that he is known within a specific, niche community that has not garnered widespread attention. If you have any additional context, such as his profession, accomplishments, or the industry he is associated with, that might help in locating more specific information.

**https://platform.openai.com/playground/chat**

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M256 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H448c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C363.4 52.6 312.1 32 256 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM132.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H64c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C52.6 148.6 32 199.9 32 256c0 10.9 .8 21.5 2.3 32H99.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM477.7 224H412.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H448c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C459.4 363.4 480 312.1 480 256c0-10.9-.8-21.5-2.3-32zM256 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H64c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C148.6 459.4 199.9 480 256 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> Recursive Fun

**Q: What is the difference between data analytics and data science?** (May 2023)

Data analytics and data science are closely related fields that use data to draw insights and solve problems. The key difference between the two is that data analytics is focused on analyzing data to draw insights and make predictions, while data science is a broader field that combines data analytics with machine learning, statistics, programming, and other areas to create solutions to complex problems.

**https://platform.openai.com/playground/chat**

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# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M256 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H448c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C363.4 52.6 312.1 32 256 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM132.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H64c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C52.6 148.6 32 199.9 32 256c0 10.9 .8 21.5 2.3 32H99.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM477.7 224H412.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H448c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C459.4 363.4 480 312.1 480 256c0-10.9-.8-21.5-2.3-32zM256 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H64c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C148.6 459.4 199.9 480 256 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> Recursive Fun

**Q: What is the difference between data analytics and data science?** (May 2024)

Data analytics and data science are related fields but they have distinct focuses and methodologies. Here is a brief overview of each and the key differences between them:

### Data Analytics

- **Objective**: Data analytics aims to examine raw data to find patterns, draw conclusions, and identify actionable insights. It is often used to answer specific questions or solve particular business problems.
- **Techniques**: Utilizes statistical analysis, descriptive analytics, diagnostic analytics, and sometimes predictive analytics.
- **Tools**: Common tools include Excel, SQL, Tableau, Power BI, and more specialized analytics software.
- **Scope**: Typically more focused on analyzing existing data sets to produce reports and dashboards. It is often confined to structured data.
- **Applications**: Used widely in business intelligence, operational optimization, and decision support.

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# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M256 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H448c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C363.4 52.6 312.1 32 256 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM132.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H64c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C52.6 148.6 32 199.9 32 256c0 10.9 .8 21.5 2.3 32H99.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM477.7 224H412.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H448c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C459.4 363.4 480 312.1 480 256c0-10.9-.8-21.5-2.3-32zM256 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H64c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C148.6 459.4 199.9 480 256 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> Recursive Fun

**Q: What is the difference between data analytics and data science?** (May 2024)

### Data Science

- **Objective**: Data science is broader and more exploratory. It involves creating algorithms and models to understand complex phenomena, make predictions, and derive deeper insights.
- **Techniques**: Encompasses advanced statistical methods, machine learning, predictive analytics, and even AI.
- **Tools**: Uses a wide range of technologies including programming languages (Python, R), machine learning frameworks (TensorFlow, Scikit-learn), and big data tools

**https://platform.openai.com/playground/chat**

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# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M256 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H448c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C363.4 52.6 312.1 32 256 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM132.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H64c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C52.6 148.6 32 199.9 32 256c0 10.9 .8 21.5 2.3 32H99.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM477.7 224H412.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H448c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C459.4 363.4 480 312.1 480 256c0-10.9-.8-21.5-2.3-32zM256 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H64c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C148.6 459.4 199.9 480 256 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> Recursive Fun

**Q: What is performance analytics?** (May 2023)

Performance analytics is the use of data to measure and analyze the performance of a business or organization. It involves collecting and analyzing data from multiple sources to identify patterns and trends, and to identify areas of improvement. Performance analytics can be used to measure the success of a business or organization in meeting its goals, as well as to identify root causes of performance issues. It can also be used to help identify areas of opportunity and to develop strategies to improve performance.

**https://platform.openai.com/playground/chat**

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# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M256 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H448c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C363.4 52.6 312.1 32 256 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM132.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H64c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C52.6 148.6 32 199.9 32 256c0 10.9 .8 21.5 2.3 32H99.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM477.7 224H412.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H448c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C459.4 363.4 480 312.1 480 256c0-10.9-.8-21.5-2.3-32zM256 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H64c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C148.6 459.4 199.9 480 256 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> Recursive Fun

**Q: What is performance analytics?** (May 2024)

Performance analytics is a specialized area within data analytics focused on measuring, monitoring, and analyzing the performance of various processes, systems, or organizations. The primary goal is to improve performance by identifying strengths, weaknesses, opportunities, and threats through data-driven insights.

### Key Components of Performance Analytics
1. **Metrics and Key Performance Indicators (KPIs)**:
   - Metrics and KPIs are defined to measure specific aspects of performance. These could relate to productivity, efficiency, quality, financial health, customer satisfaction, and more.
  
2. **Data Collection**:
   - Data relevant to the chosen metrics is collected from various sources, which could include transactional systems, log files, surveys, financial records, etc.

---

# <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M256 96c38.4 0 73.7 13.5 101.3 36.1l-32.6 32.6c-4.6 4.6-5.9 11.5-3.5 17.4s8.3 9.9 14.8 9.9H448c8.8 0 16-7.2 16-16V64c0-6.5-3.9-12.3-9.9-14.8s-12.9-1.1-17.4 3.5l-34 34C363.4 52.6 312.1 32 256 32c-10.9 0-21.5 .8-32 2.3V99.2c10.3-2.1 21-3.2 32-3.2zM132.1 154.7l32.6 32.6c4.6 4.6 11.5 5.9 17.4 3.5s9.9-8.3 9.9-14.8V64c0-8.8-7.2-16-16-16H64c-6.5 0-12.3 3.9-14.8 9.9s-1.1 12.9 3.5 17.4l34 34C52.6 148.6 32 199.9 32 256c0 10.9 .8 21.5 2.3 32H99.2c-2.1-10.3-3.2-21-3.2-32c0-38.4 13.5-73.7 36.1-101.3zM477.7 224H412.8c2.1 10.3 3.2 21 3.2 32c0 38.4-13.5 73.7-36.1 101.3l-32.6-32.6c-4.6-4.6-11.5-5.9-17.4-3.5s-9.9 8.3-9.9 14.8V448c0 8.8 7.2 16 16 16H448c6.5 0 12.3-3.9 14.8-9.9s1.1-12.9-3.5-17.4l-34-34C459.4 363.4 480 312.1 480 256c0-10.9-.8-21.5-2.3-32zM256 416c-38.4 0-73.7-13.5-101.3-36.1l32.6-32.6c4.6-4.6 5.9-11.5 3.5-17.4s-8.3-9.9-14.8-9.9H64c-8.8 0-16 7.2-16 16l0 112c0 6.5 3.9 12.3 9.9 14.8s12.9 1.1 17.4-3.5l34-34C148.6 459.4 199.9 480 256 480c10.9 0 21.5-.8 32-2.3V412.8c-10.3 2.1-21 3.2-32 3.2z"/></svg> Recursive Fun

**Q: What is performance analytics?** (May 2023)

3. **Data Integration and Cleaning**:
   - Data from different sources is integrated and cleaned to ensure accuracy and consistency before analysis.
  
4. **Visualization and Reporting**:
   - Dashboards, charts, and reports are created to visualize the data in an understandable and actionable manner. Tools like Tableau, Power BI, and Excel are commonly used.
  
5. **Analysis and Interpretation**:
   - Advanced analytical techniques are applied to interpret the data. This might involve statistical analysis, trend analysis, root cause analysis, and more.

6. **Benchmarking**

**https://platform.openai.com/playground/chat**

---

---

---

###

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### Measure

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---

---

### Measure <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Collect <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Analyze <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Report

---

---

# Performance at Home

Measure <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Collect <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Analyze <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M438.6 278.6c12.5-12.5 12.5-32.8 0-45.3l-160-160c-12.5-12.5-32.8-12.5-45.3 0s-12.5 32.8 0 45.3L338.8 224 32 224c-17.7 0-32 14.3-32 32s14.3 32 32 32l306.7 0L233.4 393.4c-12.5 12.5-12.5 32.8 0 45.3s32.8 12.5 45.3 0l160-160z"/></svg> Report

**What might we measure?**

---

# Performance at Home

- How has your electricity usage increased due to working from home, if at all?

- How have your sleep patterns changed since having a baby?

- How has your heart rate changed since starting a new exercise routine?

---

# Performance Analytics Example

### Fitbit

**What might we measure?**

---

# Performance Analytics Example

### Fitbit: Daily Steps During COVID-19

View the code [on GitHub](https://github.com/bretsw/fitbit)

---

# Performance Analytics Example

### Fitbit: Resting Heart Rate During Move

View the code [on GitHub](https://github.com/bretsw/fitbit)

---

# Performance Analytics Example

### Fitbit: Heart Rate Yesterday

View the code [on GitHub](https://github.com/bretsw/fitbit)

---

---

# Performance at Work

**What might we measure?**

---

# Performance at Work

- How has your productivity changed due to working from home, if at all?

- Who do you tend to seek for professional advice?

- How has the sentiment of team messages changed during COVID-19?

---

# Performance Analytics Example

### GitHub User Profile: @bretsw

[View the webpage](https://github.com/bretsw)

---

# Performance Analytics Example

### GitHub User Profile: @hadley

[View the webpage](https://github.com/hadley)

---

# Performance Analytics Example

### GitHub: {tidytags} R Package Contributors

[View the webpage](https://github.com/bretsw/tidytags/graphs/contributors)

---

# Performance Analytics Example

### GitHub: {rtweet} R Package Contributors

[View the webpage](https://github.com/ropensci/rtweet/graphs/contributors)

---

# Performance Analytics Example

### GitHub: {tidytags} Development Timeline (Nov 2021)

[View the webpage](https://github.com/bretsw/tidytags/graphs/commit-activity)

Also check out [{tidytags} Code Frequency](https://github.com/bretsw/tidytags/graphs/code-frequency)

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# Performance Analytics Example

### GitHub: {tidytags} Development Timeline (Jan 2022)

[View the webpage](https://github.com/bretsw/tidytags/graphs/commit-activity)

Also check out [{tidytags} Code Frequency](https://github.com/bretsw/tidytags/graphs/code-frequency)

---

---

# <svg aria-hidden="true" role="img" viewBox="0 0 448 512" style="height:1em;width:0.88em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M128 0c17.7 0 32 14.3 32 32V64H288V32c0-17.7 14.3-32 32-32s32 14.3 32 32V64h48c26.5 0 48 21.5 48 48v48H0V112C0 85.5 21.5 64 48 64H96V32c0-17.7 14.3-32 32-32zM0 192H448V464c0 26.5-21.5 48-48 48H48c-26.5 0-48-21.5-48-48V192zm80 64c-8.8 0-16 7.2-16 16v96c0 8.8 7.2 16 16 16h96c8.8 0 16-7.2 16-16V272c0-8.8-7.2-16-16-16H80z"/></svg> Semester schedule

- **Module 1:** Introduction to Analytics
- **Module 2: Performance Analytics**
- **Module 3:** Learning Analytics
- **Module 4:** Web Analytics
- **Module 5:** Data Visualization
- **Module 6:** Ethics in Learning Analytics
- **Module 7:** Future of Analytics
- **Module 8:** Case Discussions

---

- <svg aria-hidden="true" role="img" viewBox="0 0 512 512" style="height:1em;width:1em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M464 256A208 208 0 1 0 48 256a208 208 0 1 0 416 0zM0 256a256 256 0 1 1 512 0A256 256 0 1 1 0 256zm306.7 69.1L162.4 380.6c-19.4 7.5-38.5-11.6-31-31l55.5-144.3c3.3-8.5 9.9-15.1 18.4-18.4l144.3-55.5c19.4-7.5 38.5 11.6 31 31L325.1 306.7c-3.2 8.5-9.9 15.1-18.4 18.4zM288 256a32 32 0 1 0 -64 0 32 32 0 1 0 64 0z"/></svg> Explore

- <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M88.2 309.1c9.8-18.3 6.8-40.8-7.5-55.8C59.4 230.9 48 204 48 176c0-63.5 63.8-128 160-128s160 64.5 160 128s-63.8 128-160 128c-13.1 0-25.8-1.3-37.8-3.6c-10.4-2-21.2-.6-30.7 4.2c-4.1 2.1-8.3 4.1-12.6 6c-16 7.2-32.9 13.5-49.9 18c2.8-4.6 5.4-9.1 7.9-13.6c1.1-1.9 2.2-3.9 3.2-5.9zM0 176c0 41.8 17.2 80.1 45.9 110.3c-.9 1.7-1.9 3.5-2.8 5.1c-10.3 18.4-22.3 36.5-36.6 52.1c-6.6 7-8.3 17.2-4.6 25.9C5.8 378.3 14.4 384 24 384c43 0 86.5-13.3 122.7-29.7c4.8-2.2 9.6-4.5 14.2-6.8c15.1 3 30.9 4.5 47.1 4.5c114.9 0 208-78.8 208-176S322.9 0 208 0S0 78.8 0 176zM432 480c16.2 0 31.9-1.6 47.1-4.5c4.6 2.3 9.4 4.6 14.2 6.8C529.5 498.7 573 512 616 512c9.6 0 18.2-5.7 22-14.5c3.8-8.8 2-19-4.6-25.9c-14.2-15.6-26.2-33.7-36.6-52.1c-.9-1.7-1.9-3.4-2.8-5.1C622.8 384.1 640 345.8 640 304c0-94.4-87.9-171.5-198.2-175.8c4.1 15.2 6.2 31.2 6.2 47.8l0 .6c87.2 6.7 144 67.5 144 127.4c0 28-11.4 54.9-32.7 77.2c-14.3 15-17.3 37.6-7.5 55.8c1.1 2 2.2 4 3.2 5.9c2.5 4.5 5.2 9 7.9 13.6c-17-4.5-33.9-10.7-49.9-18c-4.3-1.9-8.5-3.9-12.6-6c-9.5-4.8-20.3-6.2-30.7-4.2c-12.1 2.4-24.7 3.6-37.8 3.6c-61.7 0-110-26.5-136.8-62.3c-16 5.4-32.8 9.4-50 11.8C279 439.8 350 480 432 480z"/></svg> Discuss

- <svg aria-hidden="true" role="img" viewBox="0 0 576 512" style="height:1em;width:1.12em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#782F40;overflow:visible;position:relative;"><path d="M64 64C28.7 64 0 92.7 0 128V384c0 35.3 28.7 64 64 64H512c35.3 0 64-28.7 64-64V128c0-35.3-28.7-64-64-64H64zm16 64h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM64 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V240zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H80c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16zm80-176c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V144zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V240c0-8.8 7.2-16 16-16zM160 336c0-8.8 7.2-16 16-16H400c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H176c-8.8 0-16-7.2-16-16V336zM272 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H272c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM256 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H272c-8.8 0-16-7.2-16-16V240zM368 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H368c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM352 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H368c-8.8 0-16-7.2-16-16V240zM464 128h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V144c0-8.8 7.2-16 16-16zM448 240c0-8.8 7.2-16 16-16h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V240zm16 80h32c8.8 0 16 7.2 16 16v32c0 8.8-7.2 16-16 16H464c-8.8 0-16-7.2-16-16V336c0-8.8 7.2-16 16-16z"/></svg> Create

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### Discussion (50 points)

- Initial post on Flip due by second Friday (but post as early as possible)

- At least 3 response due on Flip by end of Module 2 (second Sunday night)

---

### Analytics Problem Plan (100 points) - **due end of Module 3**

- "Write a brief report (approximately 750-1000 words) identifying and describing a real-world problem that might be addressed via analytics."

- "The emphasis of this assignment is on the conceptualization of the problem and the clear identification of a possible data source."

- "Then briefly describe how analytics will be used to solve the problem"

- "You will not need to collect actual data related to this problem, just plan for how it would be done."

---

<hr>

**What questions can I answer for you now?**

**How can I support you this week?**

<hr>

---

# <svg aria-hidden="true" role="img" viewBox="0 0 640 512" style="height:1em;width:1.25em;vertical-align:-0.125em;margin-left:auto;margin-right:auto;font-size:inherit;fill:#fff;overflow:visible;position:relative;"><path d="M181.5 197.1l12.9 6.4c5.9 3 12.4 4.5 19.1 4.5c23.5 0 42.6-19.1 42.6-42.6V144c0-35.3-28.7-64-64-64H128c-35.3 0-64 28.7-64 64v21.4c0 23.5 19.1 42.6 42.6 42.6c6.6 0 13.1-1.5 19.1-4.5l12.9-6.4 8.4-4.2L135.1 185c-4.5-3-7.1-8-7.1-13.3V168c0-13.3 10.7-24 24-24h16c13.3 0 24 10.7 24 24v3.7c0 5.3-2.7 10.3-7.1 13.3l-11.8 7.9 8.4 4.2zm-8.6 49.4L160 240l-12.9 6.4c-12.6 6.3-26.5 9.6-40.5 9.6c-3.6 0-7.1-.2-10.6-.6v.6c0 35.3 28.7 64 64 64h64c17.7 0 32 14.3 32 32s-14.3 32-32 32H384V336 320c0-23.7 12.9-44.4 32-55.4c9.4-5.4 20.3-8.6 32-8.6V240c0-26.5 21.5-48 48-48c8.8 0 16 7.2 16 16v32 16 48c0 8.8 7.2 16 16 16s16-7.2 16-16V204.3c0-48.2-30.8-91-76.6-106.3l-8.5-2.8c-8-2.7-12.6-11.1-10.4-19.3s10.3-13.2 18.6-11.6l19.9 4C576 86.1 640 164.2 640 254.9l0 1.1h0c0 123.7-100.3 224-224 224h-1.1H256h-.6C132 480 32 380 32 256.6V256 216.8c-10.1-14.6-16-32.3-16-51.4V144l0-1.4C6.7 139.3 0 130.5 0 120c0-13.3 10.7-24 24-24h2.8C44.8 58.2 83.3 32 128 32h64c44.7 0 83.2 26.2 101.2 64H296c13.3 0 24 10.7 24 24c0 10.5-6.7 19.3-16 22.6l0 1.4v21.4c0 1.4 0 2.8-.1 4.3c12-6.2 25.7-9.6 40.1-9.6h8c17.7 0 32 14.3 32 32s-14.3 32-32 32h-8c-13.3 0-24 10.7-24 24v8h56.4c-15.2 17-24.4 39.4-24.4 64H320c-42.3 0-78.2-27.4-91-65.3c-5.1 .9-10.3 1.3-15.6 1.3c-14.1 0-27.9-3.3-40.5-9.6zM96 128a16 16 0 1 1 0 32 16 16 0 1 1 0-32zm112 16a16 16 0 1 1 32 0 16 16 0 1 1 -32 0z"/></svg> <br><br> Play in the <br> [Analytics Sandbox](https://bretsw.com/sandbox)