Welcome to
Data Driven Tales
Where Numbers Meet Narratives
March 20, 2005
Every dataset has a story waiting to be uncovered, and that’s the heart of Data Driven Tales. This blog is a journey into the world of data analytics—a space where raw numbers are transformed into actionable insights, and technical tools become the paintbrushes for crafting meaningful narratives. Here, I’ll share my personal evolution from a high school math teacher and volleyball coach to a budding data analyst, all while embracing the art and science of storytelling through data.
For 28 years, I honed my skills as a math teacher, fostering problem-solving and precision in the classroom while coaching teamwork and strategy on the volleyball court. My passion for collaboration and innovation led me to earn a Master’s degree in Instructional Technology, where I began exploring ways to bring technology into learning. Now, I’m channeling that same drive for growth and learning into the field of data analytics—mastering tools like Excel, SQL, and Tableau, while unraveling the hidden stories within numbers.
In Data Driven Tales, you’ll find more than just technical tips and tutorials. Together, we’ll explore how to bridge the gap between data and decision-making, crafting visualizations and insights that resonate with any audience. I’ll draw inspiration from excellent resources like Storytelling with Data, Luke Barousse, Alex The Analyst, and Coursera, sharing what I learn along the way. Whether you’re a fellow data enthusiast, a professional looking to grow your skills, or simply someone curious about the magic of analytics, this blog is for you.
Join me on this exciting adventure as I decode trends, share tips, and uncover the stories that data has to tell. Together, we’ll discover how the right tools, a touch of creativity, and a passion for storytelling can turn numbers into narratives that inspire and inform. Welcome to Data Driven Tales let’s make data come alive!
Explore the Landscape of Data Analytics Careers
March 20, 2025 – Insights from January 2023
The field of data analytics is vast, encompassing a variety of job titles and specialized roles. A recent chart analyzing job opportunities in the U.S. as of January 2023 reveals fascinating trends in the distribution of roles within the industry. From the data, it’s clear that positions like Data Analyst and Data Scientist lead the way, with 982 and 869 jobs listed, respectively, showcasing their dominance in the analytics field. On the other end of the spectrum, niche roles such as Cloud Engineer and Software Engineer appear far less frequently, with only 4 and 34 jobs, respectively.
This chart provides valuable insights for those navigating or planning their career in data analytics. For example, the high demand for Data Analysts and Data Scientists suggests these roles are excellent starting points for those entering the field or professionals looking to pivot into analytics. Meanwhile, the limited number of listings for specialized roles like Cloud Engineers highlights potential niches for individuals with a keen interest in technical or hybrid fields within analytics. Understanding this landscape can guide aspiring data professionals in targeting roles that align with their skills, interests, and career aspirations.
As someone diving into the world of data analytics, I find this breakdown particularly enlightening. It underscores the importance of mastering versatile tools like Excel, SQL, and Tableau while staying curious and adaptable to emerging trends. Whether you’re exploring a new career or furthering your analytics expertise, this data-driven snapshot serves as a reminder that every role contributes to the broader story of turning information into impactful insights. Let’s use this knowledge to chart a path toward impactful and fulfilling careers in analytics!
April 1, 2025
Unearthing Insights:
Exploring Women’s Basketball Data from the NCAA
When it comes to analyzing basketball metrics, data is your best teammate. For the 2023-2024 NCAA Women’s Basketball season, I embarked on an adventure to obtain, clean, and explore data to uncover trends, insights, and narratives worth sharing. Here’s how the journey unfolded:
Getting the Data from the NCAA Website
The NCAA website serves as a treasure trove for basketball enthusiasts, offering detailed statistics for teams and players throughout the season. Finding the data for the 2023-2024 Women’s Basketball season required some strategic navigation:
- Locate Team Statistics: On the NCAA Women’s Basketball section, I identified team stats such as points per game, field goal percentage, assists, turnovers, and rebounds.
- Download the Data: Many tables offered the option to export data as CSV files. These downloadable files provided the foundation for analysis.
Cleaning the Data in Excel
Once the data was collected, the work truly began. Raw datasets often come with quirks like missing values, inconsistent formats, or unnecessary columns. Here’s how I cleaned the NCAA Women’s Basketball data:
- Standardizing Column Names: I ensured every column had consistent and meaningful titles, such as “Team Name,” “Points Per Game,” or “Turnover Percentage.”
- Removing Unnecessary Columns: Metrics not relevant to my analysis were filtered out, leaving behind only the essentials.
- Sorting and Filtering: I used Excel’s sorting feature to rank teams by metrics such as assist-to-turnover ratio or field goal percentage, making comparisons straightforward.
What’s Next?
This journey into NCAA Women’s Basketball data has been equal parts challenging and rewarding. Armed with structured data and Excel expertise, the possibilities are endless—predictive modeling for championship outcomes, creating visual dashboards, or sharing insights on GitHub for others to learn from.
For fellow data enthusiasts, I encourage you to dive into the world of basketball stats. Whether you’re a sports analyst, Python programmer, or Excel fanatic, the game isn’t just played on the court; it’s brought to life in the numbers. What trends will you uncover?
Who will WIN?
Can we predict the winner?
