Today in class I learned how to take a game day dataset and create different graphs with that data. This lab was helpful because we worked with a partner and we could help each other when we got stuck. #mktgfiasco4000

I really liked the new approach for the labs and how we work with partners. #mktgfiasco4000

Candy production made it clearer to me that trends and changes in production over time can be found by analyzing trends. #mktgfiasco4000

An interesting finding I found was that the data on candy manufacture revealed fluctuating production levels over he course of the observation. #mktgfiasco4000

One interesting thing I found was that electronics had the highest total sales, but clothing had the most transactions. That made me realize that the product with the most purchases is not always the product bringing in the most revenue. #mktgfiasco4000

I found it interesting to explore average North American sales by genre over time. This showed me how marketing insights can come from genre and year. I also learned that checking zero values is important when exploring a dataset. #mktgfiasco4000

This activity taught me how data analysis can uncover patterns. Using boxplots, I compared video game sales across regions and saw how sales distributions varied. Visualizing the data made it easier to understand the differences than simply looking at numbers. #mktgfiasco4000

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I also had trouble finding a good data set for discussion 4 and I couldn’t think of anything relatable so I went with a video game data set. Initially I was gonna go with Netflix movies but the excel sheet was too complicated. So then I did a box plot and a histogram plot.

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The discussion 4 activity was sort of a challenge for me. Since I’m still learning about R and its functions. So what I did was ask the professor for help, and thanks to that I was able to do the assignment and I confess that I learned various things and they make more sense now.

Learned that a boxplot can show you a lot quickly, it turns out body style is a solid predictor of price in car sales data. Didn't expect exploratory analysis to be this useful before even building a model. #mktgfiasco4000

Picked a car sales dataset with 24k records for my marketing stats class this week. Wasn't sure what to look for at first, but once I started running summaries it got easier to see what mattered. #mktgfiasco4000

Another week in the books for MKTG 4000. This week, I thoroughly learned some new key components in coding and using R Markdown. Slowly but surely, the class is starting to make more and more sense. Go Figure! 🤷‍♂️
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After creating my own exploratory data analysis, an interesting marketing insight I gained was analyzing social media engagement across different platforms. In my findings, I have found that Instagram receives the most engagement compared to other platforms like Facebook and Twitter. #mktgfiasco4000

In week 4, while creating my own exploratory data analysis, what I found really interesting was analyzing the relationships between variables and seeing how this shows up on a graph/chart. #mktgfiasco4000

found that artist popularity has a positive relationship with track popularity. This showed me how EDA can help find useful patterns in music data and help marketers understand which artists or songs may get more attention from listeners. #mktgfiasco4000

In this activity, I used a Spotify dataset and explored it using R. I learned how summary statistics and graphs can make a large dataset much easier to understand. Looking at track popularity helped me see how different songs can perform very differently. #mktgfiasco4000

One interesting insight from my analysis was that customers with similar annual incomes can still have very different spending scores. This showed me that marketers should not rely on income alone when creating customer segments or planning targeted advertising. #mktgfiasco4000

For this discussion 4 activity, I explored the Mall Customers dataset from Kaggle. I learned how customer information such as gender, income, age, and spending score can be used to understand different customer groups. Creating graphs made the data easier to interpret. #mktgfiasco4000

Discussion #4 gave me the freedom to chose any data set I wanted and actually use the data to show something. #mktgfiasco4000

I learned a lot in dicussion #4 and I feel like my R studio skills are improving with every assignment. #mktgfiasco4000