The overview of this project is to learn how to decipher raw data in Excel. By learning how to use tools such as using filters, pivot tables, and applying statistical tools we are able to represent an overview of data, and using these tools to break down data to show specific aspects of the data set. We learned some basic and more advanced techniques to derive this data from the original Kickstarter worksheet.
In the first challenge, we are asked to show different types of data from a Kickstarter spreadsheet. In this exercise, we sorted through outcomes of fundraising campaigns based on launch date and goals. This data was taken from the original Kickstarter worksheet and then pivot tables were used to isolate specific sets. After building the pivot tables, graphs and charts were formed to make a visual representation of the data for presentation. I found the most challenging aspect of these lessons was learning how to use pivot tables. I had difficulty with using filters (X,Y axis). It took some time and experimenting to be able to confidently provide correct and accurate data.
Campaign outcomes based on launch dates showed that the Spring and Summer season seems to be the best time of the year to run a successful campaign however, there is an increase of failed launches. During the Fall and Winter months, the number of successful launches compared to failed launches become closer together. The takeaway is that there is a better chance of launching a successful campaign during warmer months, but during the cooler time of year, the chance of launching a failed campaign could be more likely.
Outcomes based on campaign goals showed that there is a correlation between the amount of money being and the percentage of successful or failed campaigns. The graph shows the highest percentage of successful campaigns start with lower dollar amounts and a gradual declination until about $30,000. At that point, the percentage rapidly climbs between $30,000 and $35,000, but starts to taper off from the second peak. Failed campaigns showed the opposite results where the smallest percentage of failed campaigns start with the lower dollar amount, but rises as the amount gets higher. However, there is a sharp decrease in percentage at the $30,000 to $35,000 mark.
I found that the most challenging part of this module/activity was just simply making sure the formulas were correct and consistent with the data I was attempting to represent. From my perspective, repetition is the greatest tool to learn new concepts. I did enjoy learning new ways to perform tasks that I have been doing for years in Excel.
In the Launch Date data, it showed that campaigns started in the warmer months historically tend to be more successful. It also showed that no matter how many campaigns are started, some will fail or be cancelled. The more campaigns launched, the more some will fail.
The data regarding campaigns based on goals show there is a direct correlation between the percentage of successful and failed campaigns based on dollar amounts donated during the campaign.
One of the limitations to this dataset is that it did not account for some statistical data that could affect the outcome. One limitation is that outliers were not calculated. Because of this, the data could be skewed either in one way or another.
Other types of data we could have represented was a breakdown of launch dates or goals by individual countries. We could have also shown how many campaigns met their goals by deadline dates.