Join Chris DallaVilla for an in-depth discussion in this video Next steps, part of The Data Science of Marketing.
- Marketing is changing right in front of our eyes,…and that transformation is being led by data.…Some call data the new oil.…I see it as a tool that if used the right way…will really transform our profession.…You've done an amazing job.…You've learned the fundamentals…of data science for marketing.…So where do you go from here?…I like to think that you have just set up your laboratory.…A laboratory needs the right tools.…There are the modeling tools,…like we've reviewed in this course.…There are your best practices like we've discussed.…
And there's the need to get and store the data.…Going forward, consider which areas…you would like to further develop.…The online library has some great additional resources…for Python, like the Pandas For Data Science course…with Charles Kelly,…and some great additional courses for Tableau,…like the Tableau 10 Essential Training course…with Curt Frye.…I've also set up a webpage you can reference…with additional resources.…Just follow the link on the screen.…I'm excited you get to go forward now…
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
Skill Level Intermediate
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1. Software Installation
2. Data, Exploratory Analysis, and Performance Analysis
3. Inference and Regression Analysis
5. Cluster Analysis
6. Conjoint Analysis
7. Best Practices
Next steps1m 8s
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