Share a real-world example of a situation in which a data scientist would analyze data. What type of data would the data scientist analyze? Think of ways a data scientist could use a scatterplot to identify clusters and outliers and what they could mean.
Explain why you chose that career path for the data scientist. Summarize what you found out about clusters and outliers in that career choice.
(Completely original and like an 8th grade wrote it)
A data scientist working for a retail company could analyze data to understand customer purchasing behavior. The data scientist would analyze information such as customer demographics, purchase history, and shopping habits to identify patterns and trends.
Using a scatterplot, the data scientist could plot variables such as customer age and average purchase amount to identify clusters of customers who tend to spend more or less than the average. Outliers in this case could represent high-spending customers who may be worth targeting for specific marketing campaigns or low-spending customers who may need to be incentivized to increase their purchases.
I chose the career path of a data scientist because I have always been interested in using data to solve problems and make informed decisions. I believe that the ability to extract meaningful insights from data is a valuable skill that can make a significant impact in various industries.
In analyzing customer purchasing behavior, I found that there were distinct clusters of customers based on their spending habits. By targeting these clusters with personalized marketing strategies, the retail company could potentially increase sales and customer loyalty. Outliers, such as high-spending customers, could also provide valuable insights for creating targeted promotions and improving overall customer experience.