This course is about learning Business Intelligence & Analytical tool called Tableau, which has been in leaders position since 4 years Business Intelligence, Analytics, Data Visualisation, Tableau desktop, Tableau server, Tableau & Hadoop, Tableau & R, are the common terminologies used to find this course We have included course content in form of powerpoint presentation, datasets used for visualisation, 2 live case study projects for download, interview questions, sample resumes/profiles for job seekers This course is extremely exhaustive & hence will last for more than 25 hours Course is structured to start with introduction to the tool & the principles behind data visualisation. From there Tableau desktop is explained thoroughly including analytical concepts behind applicable visualisation. Finally course ends with explanation on Tableau server & the final 2 use cases as projects along with interview questions for job seekers Jobs are abundant for Tableau & salaries are very promising & highest in this domain. Also this course is very exhaustive which includes Statistics, Forecasting, Regression models, K-means Clustering, Text Mining, Hadoop & R required for Tableau. Also included are Tableau Desktop & Server concepts in one course.

In this blog, we will discuss about clustering of customer activities for 24 hours by using K-means clustering feature in Tableau 10. This type of clustering helps you create statistically-based segments that provide insights about similarities in different groups and performance of the groups when compared to each other. You can use clustering on any type of visualization ranging from scatter plots to text tables and even maps. In our previous blog post – "Call Detail Record Analysis – K-means Clustering with R", we have discussed about CDR analysis using unsupervised K-means clustering algorithm. A daily activity file from Dandelion API is used as a data source, where the file contains CDR records generated by the Telecom Italia cellular network over the city of Milano.

Note: The following is a guest post by Roman Schindlauer, a product manager at Dato. With the release of Tableau 10 beta 3, we're happy to announce a partnership between Dato and Tableau. With Tableau 10, you can define calculated fields in Python, and leverage the power of machine learning through Dato Predictive Services directly from your visualizations. This partnership enables a number of new possibilities. To leverage the integration, you'll need to have access to an existing Dato Predictive Service, which can be set up to run in the Cloud (on AWS EC2 nodes) or on-premise.