Join Chris DallaVilla for an in-depth discussion in this video Pros and cons, part of The Data Science of Marketing.
powerful is maybe the wrong word but they both offer a larger range of data science options and capabilities for statistical modeling when compared to Tableau. I'm not sure what the statistics are for R's usage compared to that of Python but I would guess R experiences a line share. It's just a statistical modeling powerhouse. And there's a very robust community creating and maintaining open source packages. R is great for addressing most data analysis needs but requires a significant investment of time to become truly proficient.
Now, that's not to say that even a managerial use case should not have the fundamental understanding of the platform because there are just times where it's simply the best choice. Python has many strengths. It's a well tested language. It continues to evolve and it also has a strong following and a strong community behind it. One of the advantages of the Jupyter platform, for example, is that it can interpret many languages. Not just Python. So, if your so inclined, you could explore other languages using that environment as well. Similar to R though it does have a significant learning curve.
If you want to be the best in the world at Python for marketing data science, one of the elite, it's going to take you some time. But picking up fundamentals is well within everyone taking this course's ability to do. And again, sometimes Python is just the right choice. So that brings us to Tableau. This platform is highly motivated to remove some of the learning curve of these other tools. After all, it's a gooey application with a large for-profit company behind it. And they're aim is, impart at least, to make data analysis accessible to many. There are some deficiencies though.
Some of the statistical modeling you can do in R and Python simply is not available in Tableau. So, while it does an amazing job at ease of use and it really does streamline the ability to create interesting data visualizations. It lacks some of the power under the hood. Which is something to consider when choosing the right platform for your analysis.
In this course, discover how to gain valuable insights from large data sets using specific languages and tools. Follow Chris DallaVilla as he walks through how to use R, Python, and Tableau to perform data modeling and assess performance. As Chris dives into these concepts, he shares specific case studies that come directly from his own work with clients. Plus, he shares three essential—and practical—best practices for data-driven marketing that you can use to bolster your organization's marketing performance.
- Installing R, Python, and Tableau
- Navigating the UI for R, Python, and Tableau
- Using R, Python, and Tableau
- Exploratory analysis
- Performing regression analysis
- Performing a cluster analysis
- Performing a conjoint assessment
- Stakeholder alignment