Lecture Notes on Data Analysis Journey

Introduction

• Life's journey is often windy and unclear, similar to everyone's unique experiences.
• Discussion of personal experiences transitioning through different phases of life and careers.

Initial Career in Data Analytics

• Completed an undergrad in math.
• Early jobs involved simple tasks in data analytics using Excel:
  • Inputting numbers into spreadsheets.
  • Performing basic calculations.

Transition to Higher Education

• Started working in higher education, with a different onboarding experience:
  • Participated in meetings about business processes (e.g., student enrollment).
  • Learned how to present data in accessible formats (e.g., PDFs).
• Encountered a major system conversion (legacy system to PeopleSoft):
  • Comparison akin to switching from Samsung to iPhone.
  • Needed to report data to state and federal government.

Understanding Data Analysis

• Importance of collaboration with colleagues to understand data processes:
  • Engaged with colleagues to learn about both the old and new system.
  • Required meetings with various departments for collective data understanding.
• Learning about data itself was essential due to:
  • Errors and quirks that exist within the data.
  • Importance of a thorough understanding before drawing conclusions.

Personal Biases in Data Analysis

• Discussed personal "bias" examples:
  • Example of personal biases drawn from family members' food choices.
  • Realized biases in earlier analytics:
    • Making assumptions based on previous reports, leading to overlooked crucial evidence.
    • Combining pressure to appear irreplaceable and making mistakes in analysis due to rushing.

Growth and Evolution Over Time

• After several years, shift in approach:
  • Started taking a comprehensive overview (30,000-foot view) of projects.
  • Engaged in deeper analysis, interacted with colleagues for knowledge.

Moving and New Opportunities

• Moved to Tucson, Arizona, leading to reevaluation of career and education:
  • Enrolled in a Master of Learning Technologies program while working part-time as an analyst.
• Experienced challenges of balancing school, work, and family life.
  • Engaged in group projects leading up to conference presentations.

Conference Experiences

• Became interested in attending and presenting at conferences:
  • Met like-minded individuals and shared knowledge.
• Encountered new perspectives,
  • Katie Stroud’s discussion on storytelling with data resonated personally.
  • Gained insights on the rush for academic publishing versus implementing findings practically.

Bridging the Gap

• Highlighted a rift in educational practice and academic research:
  • Teachers feeling disconnected with researchers, and vice versa.
  • Recognized the potential for data analysis to bridge this gap.

Conclusion

• Emphasis on importance of spending time with people and data:
  • Engage in conversations, ask questions, and explore underlying data.
  • Data analysis is both an art and a science and can strengthen relationships across sectors.
• Encouraged everyone to appreciate the beauty and power of data analysis.

Closing Remarks

• Thanked the audience for their attention.