Feed: Featured Blog Posts - Data Science Central. Author: Dr. Moloy De. I was deputed to work at Lagos, Nigeria in 2011 to work for a telecom giant there. The project in hand was to develop customer analytics modules using SAS on customer's newly built Oracle data warehouse. We thought about developing following modules. Customer Churn Analysis Calculating Product Propensities Customer Lifetime Value Calculation Customer Segmentation Customer Churn Analysis: A subscriber was defined to have churned if he / she didn't make any revenue generating usage in past three months. Following this definition around 15% subscribers were found to have ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Chris Kachris. Emerging applications like machine learning (ML), big data analytics, and artificial intelligence (AI) has created the need for many companies to hire highly skilled and experienced work force. Demand for data scientists, ML engineers and data engineers is booming and will only increase in the next years. The January report from Indeed, one of the top job sites, showed a 29% increase in demand for data scientists year over year and a 344% increase since 2013. Salaries and OpEx At the same time the personnel cost of this skilled ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Bill Schmarzo. Moo… There is this very intriguing Enterprise Car Rental commercial on TV (I’m old school and still watch my commercials on a television) that shows a glimpse into future driving behaviors enabled by autonomous vehicles (see Figure 1). Figure 1: 'The Future of Transportation'Enterprise Car Rental Commercial As you can see in Figure 1, the autonomous vehicles appear to be randomly crossing the intersection, with cars driving through the intersection without stopping and waiting for the other cars.  Because these cars are literally talking to each other – without ... Read More

Feed: Cambridge Intelligence. Author: Adrian Meredith. Performance is key when you’re building a graph visualization product. No matter what browser or device your application runs on, large, complex datasets shouldn’t take ages to load, and chart interactions must be smooth and responsive.But performance isn’t a standalone feature. It’s a key contributor to one of the most important goals of your application design: good user experience.We’ve worked hard to deliver the most powerful network visualization toolkits so your users get the fast and insightful experience they deserve. What makes the performance rates so impressive? It’s all thanks to the Web Graphics ... Read More

Feed: R-bloggers. Author: R on Jacob Long. It has been a long time coming, but my R packagepanelr is now onCRAN. Since I started work on itwell over a year ago, it has become essential to my own workflow and I hopeit can be useful for others. panel_data object class One key contribution, that I hope can help other developers, is the creation ofa panel_data object class. It is a modified tibble, which is itself amodified data.frame. panel_data frames are grouped by entity, so manyoperations (e.g., mean(), cumsum()) performed by dplyr’s mutate()are groupwise operations. The panel_data frame also works very ... Read More

Feed: R-bloggers. Author: Giorgio Garziano. Category Tags This is the fourth part of our post series about the exploratory analysis of a publicly available dataset reporting earthquakes and similar events within a specific 30 days time span. In this post, we are going to show a cluster analysis of earthquakes located into the California state mainland. In the specific we will show a California map where earthquakes are highlighted by different color based on their magnitude we will determine the optimal number of clusters by means of the elbow method we will take advantage of the kmeans clustering;k-means clustering aims ... Read More

Feed: R-bloggers. Author: free range statistics - R. Like most people, I was surprised at yesterday’s result for the Australian federal election. But I was much less surprised than most people. The two-party-preferred vote for the ALP (currently 49.11%, with a lot of pre-polling still to count) came down at the bottom of my prediction interval but was still inside it; and they look to have a chance of making 68 or 69 seats, which was the lower range of my 80% prediction interval for number of seats. A 95% prediction interval would have comfortably included the result. While I ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Robert R. Tucci. I am pleased to announce that my quantum simulator Qubiter (available at GitHub, BSD license) now has a native TensorFlow Backend-Simulator (see its class `SEO_simulator_tf`, the `tf` stands for TensorFlow). This complements Qubiter's original numpy simulator (contained in its class `SEO_simulator`). A small step for Mankind, a giant leap for me! Hip Hip Hurray! This means that Qubiter can now calculate the evolution of a state vector using CPU, GPU or TPU. Plus it can do back-propagation on a quantum circuit. Here is a jupyter notebook that I ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Ariful Islam. One of our business units wants to target the competitors’ customers with personalized product/offer. To do that, Business needs to understand who the prepaid/postpaid customer of the competitor to push the relevant and personalized product/offer and they don’t have this data. Now, this is a binary classification problem and we want to apply machine learning machine method to predict the likeliness of competitor customer to be prepaid or postpaid. Steps in Data Science project: To solve the problem I followed following steps from data gathering to model selection Gather ... Read More

Feed: Featured Blog Posts - Data Science Central. Author: Mahesh Kumar CV. Gone are the days when insurance and nbfc sector was relying completely on manual processes. Today with the emergence of AI and data-driven business decision making coupled with the application of IoT technologies there is a radical transformation of business processes in insurance and other financial services, like straight through processing becoming mainstream.  Let us look at different use cases: 1. Insurance Models: Consider the recent proliferation of usage-based insurance models. These models are a product of a comprehensive AI applied on data collected by telematics and IOT ... Read More