I am a huge fan of incorporating Tableau into my data analytics projects. While some may use R/python or Tableau, I use both; Tableau allows us to rapidly explore our data in order to find errors that need to be addressed before moving onto downstream modeling and reporting tasks.
As you can imagine, I was thrilled when I recently noticed that Rapidminer has an Tableau Writer Extension. In short, it intends to do exactly what it says; export our ExampleSet to a Tableau extract file for use directly in Tableau.
In this post, I am going to walk through some issues that I recently encountered when attempting to get up and running with the Rasa stack.. I am a big fan of the work they are doing, and by and large, it makes a complex problem, chatbot development, accessible and leverages machine learning under the hood. This is in contrast to tools that levergae simple rule-based approaches.
Below we will be using conda to manage our python environments and ensure that the package dependencies align.
I have been playing with Neo4j quite a bit, mostly for fun as I learn how I figure out when and where I could apply it to solve various analytics problems. Neo4j, at it’s core, is a database, which allows us to query data in a structured way. While the graph model within Neo4j is very flexible, the cypher query language is fantastic. Once you get over the learning curve, with only a few lines of code you can do some really powerful queries.
I have been working on a team that is aiming to implement a Salesforce-based CRM solution for Enrollment Management. From the beginning, we had an aggressive timeline, and the project has taken many twists and turns along the way. While our experience is certainly not unique, and perhaps commonplace, it’s provided us with an opportunity to evaluate some of the fundamental steps that should be set in place prior to continuing down our deployment path considering our go-live date is currently TBD.