Learn how to perform analysis for prediction using Python and how to interpret the results.
- [Instructor] Let's jump right in.…We're going to set up our decisions tree in Python,…and so I've already declared our package statements…and the first cell, we'll be bringing in pandas and numpy.…These are two packages that you'll experience often…if you do a lot in Python.…We're also bringing in pydotplus, which offers…some additional functionality for graphing,…and we're bringing in sklearn to help split our data…and create our tree.…My machine already has some of these pre-installed…and you'll want to install them as well and…to do so, visit the link on the screen and follow…the instructions found there.…
Let's take a moment and I'll show you how to install…pydotplus, the other installation is a standard dmg,…but if you want to install packages, like pydotplus,…you can do so with command pip, install and then…the name of the package itself, pydotplus and then run that,…and that will, that will do the installation for you…for that particular package.…So, let's run this line,…and next let's connect to our data source.…
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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