Data science is basically the deep study about the data, It is an interdisciplinary field about the processes and system to know exact knowledge about the forms of data it may be structured or unstructured.
Prepping #Data for #Analysis using R : For success or failure of data science project , analysis of data is most important aspects .Many of the routine steps can be automated in a principled manner more http://www.opendatascience.com/conferences/odsc-west-2015-nina-zumel-john-mount-prepping-data-for-analysis-using-r/
In this session Juliet explains how she solves problems at Cloudera using PySpark, and why she chooses PySpark. . See complete session http://www.opendatascience.com/conferences/juliet-houghland-interview-bdf-2015/
Techniques for Transforming Categorical Data into Numerical Values http://www.opendatascience.com/blog/techniques-for-transforming-categorical-data-into-numerical-values/ See more similar article here #PredictiveAnalytics #DeepLearning #DataVisualization
7 Machine Learning Mistakes to Avoid : Here we have discussed some common mistakes that We do mostly and We should avoid it. #ArtificialIntelligence #BigData #PredictiveAnalytic See see full article visit here http://www.opendatascience.com /blog/machine-learning-mistakes-to-avoid/
******** JULIA ******* DESCRIPTION: Julia is a high-level, high-performance dynamic programming language for technical computing, with syntax that is familiar to users of other technical computing environments.