Tools for Data Visualization in R, Python, and Julia

Visualize your Social Media Analytics
In an earlier blog post on Making the Business Case for Text Analytics , I had spoken of the importance of Social Media Analytics and specifically Text Analytics within the context of Social Media.for big and small business. Social Media plays a critical role in today’s world in understanding, measuring and influencing the real time perception of your company and/or brand. Social Media contains a wealth of information which needs to be analyzed and understood in a broader social and demographic context including, trend identification and receiving feedback from segments beyond what the traditional marketer or customer service center is accustomed to for receiving feedback. Given the sheer volume of data and the large number of users talking (posting, tweeting, etc.) about any given topic, visualization can be used very effectively in Social Media Analytics to effectively sort through the clutter and make sense of what is being said. Visualization enabled Analytics can be used to identify the trends and key influencers that may not otherwise evident.

Top 20 R Machine Learning and Data Science packages
1. e1071: Functions for latent class analysis
2. rpart: Recursive Partitioning and Regression Trees
3. igraph: A collection of network analysis tools
4. nnet: Feed-forward Neural Networks and Multinomial Log-Linear Models
5. randomForest: Breiman and Cutler’s random forests for classification and regression
6. caret: package short for Classification And REgression Training
7. kernlab: Kernel-based Machine Learning Lab
8. glmnet: Lasso and elastic-net regularized generalized linear models
9. ROCR: Visualizing the performance of scoring classifiers
10. gbm: Generalized Boosted Regression Models
11. party: A Laboratory for Recursive Partitioning
12. arules: Mining Association Rules and Frequent Itemsets
13. tree: Classification and regression trees
14. klaR: Classification and visualization
15. Rweka: R/Weka interface
16. ipred: Improved Predictors
17. lars: Least Angle Regression, Lasso and Forward Stagewise
18. earth: Multivariate Adaptive Regression Spline Models
19. CORElearn: Classification, regression, feature evaluation and ordinal evaluation
20. mboost: Model-Based Boosting

rapier — Convert R Code to a Web API
I’m excited to announce a new R package: rapier, a package that enables you to convert your existing R code into web APIs by merely adding a couple of special comments.

Modeling Interest Rates Meucci Style
Here is my port of the code and the resulting chart showing JGB rates, log-rates, and shadow rates (derived from the inverse call transformation)