Finding and importing data was much harder than I thought it would be. I'm sure it will get easier once I have something specific to look for! #mktgfiasco4000

A cool insight into my analysis was that at least 80% of customers had at least graduated college. It was interesting to see such a diffeence between college graduates and high school graduates and their shopping habits #mktgfiasco4000

This dataset has taught me that just because it is numeric, doesn't mean that data is ready to use. I needed to rename certain text in order to make it work. #mktgfiasco4000

With this dataset, I learned that building the histograms and bar charts in R showed me visualization is more than making a nice looking graph. It is about the interpretation of the graph. #mktgfiasco4000

A cool insight from this week's EDA is that age and salary do not show much of a direct relationship, but when these two are plotted together and colored by ads viewed, there is a relationship #mktgfiasco4000

This required a little hunting to find a dataset that is available without log in. Performing EDA based on the conversion of users who have viewed an ad allowed me to perform EDA in a marketing context rather than just a homework exercise. #mktgfiasco4000

From this activity I learned that there are many different ways that data can be changed visually to make it easier to understand #mktgfiasco4000

From this activity, I learned to be patient and that it is ok to make mistakes because that is how we learn and are bale to continue #mktgfiasco4000

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The work we were given helped me understand just how much you need to understand how R works as making the different types of graphs with what is essentially the same data was a lot more confusing than i thought. It was very fun to work with either eay.

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This week was a bit more understandable as it was more or less learning how to create different variations of data and graphs using marketing data then understanding what it means.

Exploring flight data in R helped me identify patterns in the number of flights by month and compare average departure delays across airports. This analysis showed me how data visualization can make trends and differences easier to understand. #mktgfiasco4000

Using Kaggle to find datasets helped me see how real-world data can be used to discover trends, support marketing decisions, and better understand consumer behavior. #mktgfiasco4000

This activity taught me that data needs to be organized before it can be analyzed. Preparing the dataset helped me compare influencer subscribers, views, and likes so much easier. #mktgfiasco4000

I learned how exploratory data analysis can help marketers understand social media influencer data. Using a sample of TikTok influencers, I practiced summary statistics and created a scatterplot to compare subscribers and views. #mktgfiasco4000

R programming could advance my career by analyzing large datasets, automating repetitive tasks, and creating useful visualizations. It would save time, reduce errors, and strengthen my data-analysis skills. #mktgfiasco4000

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R is useful because it can analyze large datasets, automate tasks, and create clear visualizations. These features can make data analysis faster and help with better business decisions.

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Three reasons why R programming might add value to my career is because it would help me with data visualization, it would also help save time in making projects, lastly using this software allows for people to know how well certain data will do in the future. If it’s good or not.

I learned about A/B testing and how using 2 or more users optimizes the data process over time for new users. #mktgfiasco4000

Learning how R programming interprets data into visualization. #mktgfiasco4000

R can add value to my career in several ways. First, R handles large amounts of data. Second, it repetitive analysis. Third, R offers tools for statistics and data visualization. I believe it will help me understand marketing performance and make better data-driven decisions.
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