Today, DSAU27 hit the ground running with the commencement of Dashboard Week. To kick things off, we were given data from the Himalayan Database (based on Elizabeth Hawley’s expedition archives). The dataset covers expedition details, climber information, and information on over 400 Himalayan mountain peaks. With so much information and not much time to work, our first challenge was deciding which story to explore… or in this case, which mountain to climb.
First Steps
With the dataset split across 4 tables, my first thought was to explore and view the data in Alteryx. From here, I could easily understand how tables related to one another and which fields were critical for my story. The next step was to load the data into Tableau and build a simple data model, leveraging relationships between tables. It may have been worthwhile dropping the columns that wouldn’t be relevant to my analysis, but I unfortunately kept everything. Consequently, this slowed my work as I was frequently losing and searching for fields within Tableau.
Insights
I wanted to take this dashboard in a unique direction, so I focused my attention on the first climbers to successfully summit all recorded Himalayan Mountain Peaks. I was curious to know if there were any trends relating to when different mountains were first conquered by ambitious climbers, and whether there were any commonalities between these individuals. Climbers successfully conquered many mountain peaks for the first time during the 1950s. Starting in 2000, more climbers began summiting new mountains without hired assistance. This may suggest more advanced climbers are conquering new territory during this era. Additionally, these climbers come from diverse backgrounds, holding various occupations and ranging in age from 13 to 73! Some of these climbers have even summited more than 5 different mountains before any other expedition could.
You can view the full dashboard on Tableau Public. You’re welcome to explore these insights for yourself or use the different filters and interactive elements to find new insights. Any feedback is also welcome. Thanks for reading.
