Learn about how to perform a cluster analysis using Python and how to interpret the results.
- [Instructor] In OR, we grouped our customer…data into three consumer cohorts for segmentation.…And here in Python, we're going to crack…the hood a little bit more on this overall concept.…So, I've brought our packages in.…Some of the usual suspects you've seen before…in this course and you'll often use some…of the pandas, numpy, netplotlib.…There's also Archian's Algorithm.…There are two different approaches our…cluster analyzes can take, there's a flat cluster, which is…where you can specify how many clusters you want.…
And there's a taxonomy clustering where…the algorithm decides for us.…Our algorithm here, takes the former approach.…Similar to what we did in OR, we're…going to specify how many groups are made.…So let's go ahead and bring our packages,…so I'm going to shift, enter here.…And let's connect to our data, so I'm going to…select this second cell and shift, enter.…And let's have a look at our data now real quick.…So I'm going to type in myClusterData and the head…command and pass in a value of three so we'll get…
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
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