Tags » Data Analysis

Model Interpretability in Data Analysis

by Paulo C. Rios, Jr.

When we want to understand our data, we often have a result that depends on a number of features. For example, the different physical properties of a chemical and whether that chemical will produce a given result we desire such as the physical properties of a wine and a good quality wine as a result.  191 more words

Data Science

More sitcoms analysed and compared

I’ve now added Will and Grace and The Office to this analysis, and made an interactive Shiny app to display the data obtained from processing the scripts. 532 more words

Comedy

1st Circular: Indonesia R Meet Up

Karena ternyata sudah banyak yang “terungkap” sebagai Pengguna R (pada tahap beginners hingga advanced), sudah saatnya merancang acara R meet up. Contohnya seperti ini: http://r-users-group.meetup.com/ 119 more words

R Programming

Model Flexibility in Data Analysis


by Paulo C. Rios,  Jr.

One of the our main goals in data analysis is to model the data so that we can understand it.  Some data models are more restrictive, while others are more flexible. 161 more words

Data Science

Big data helps Conservation International proactively respond to species threats in tropical forests

This latest BriefingsDirect big data innovation discussion examines how Conservation International (CI) in Arlington, Virginia uses new technology to pursue more data about what’s going on in tropical forests and other ecosystems around the world. 2,027 more words

Dana Gardner

Modern Data Analysis: Inference and Prediction in Data Science

by Paulo C. Rios, Jr.

In complex datasets we often want to find out how a certain result is dependent on a number of features. For example, if a new direct-marketing campaign is delivered, we want to find out which individuals will respond positively to mailing, based on demographic features of each individual. 222 more words

Data Science

The rise of Modern Data Analysis, leaving classical BI behind

by Paulo C. Rios,  Jr.

In the 1990s cutting-edge statistical tools to analyze data became available as the result of increases in computational power. The highly technical nature of these techniques also meant that their use was restricted to experts in statistics and computer science who had the training required to understand and implement them. 112 more words

Data Science