R Data Mining Projects هذه الدورة تدرس باللغة الإنجليزية

تفاصيل الدورة

The R language is a powerful open source functional programming language. At its core, R is a statistical programming language that provides impressive tools for data mining and analysis. It enables you to create high-level graphics and offers an interface to other languages. This means R is best suited to producing data and visual analytics through customization scripts and commands, instead of the typical statistical tools that provide tick boxes and drop-down menus for users.

This video course explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields. We will teach you about R and its application to data mining, and give you relevant and useful information you can use to develop and improve your applications. It will help you complete complex data mining cases and guide you through handling issues you might encounter during projects.

About The Author

Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and an econometrician. He is currently leading the data science and machine learning practice for Ma Foi Analytics, Bangalore, India. Ma Foi Analytics is an advanced analytics provider for Tomorrow's Cognitive Insights Ecology, using a combination of cutting-edge artificial intelligence, proprietary big data platform, and data science expertise. He holds a patent for enhancing planogram design for the retail industry. Pradeepta has published and presented research papers at IIM Ahmedabad, India. He is a visiting faculty at various leading B-schools and regularly gives talks on data science and machine learning.

Pradeepta has spent more than 10 years in his domain and has solved various projects relating to classification, regression, pattern recognition, time series forecasting, and unstructured data analysis using text mining procedures, spanning across domains such as healthcare, insurance, retail and e-commerce, manufacturing, and so on.