Learning Path: R: Real-World Data Mining With R

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Data mining is a growing demand on the market as the world is generating data at an increasing pace. R is a popular programming language for statistics. It is very useful for day-to-day data analysis tasks.

Data mining is a very broad topic and takes some time to learn. This Learning Path will help you to understand the mathematical basics quickly, and then you can directly apply what you’ve learned in R. This Learning Path explores data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields.

This Learning Path is the complete learning process for data-happy people. We begin with a thorough introduction to data mining and how R makes it easy with its many packages. We then move on to exploring data mining techniques, showing you how to apply different mining concepts to various statistical and data applications in a wide range of fields using R’s vast set of algorithms.

The goal of this Learning Path is to help you understand the basics of data mining with R and then get you working on real-world datasets and projects.

This Learning Path is authored by some of the best in their fields.

Romeo Kienzler

Romeo Kienzler is the Chief Data Scientist of the IBM Watson IoT Division and working as an Advisory Architect helping client worldwide to solve their data analysis problems.

He holds an M. Sc. of Information System, Bioinformatics and Applied Statistics from the Swiss Federal Institute of Technology. He works as an Associate Professor for data mining at a Swiss University and his current research focus is on cloud-scale data mining using open source technologies including R, ApacheSpark, SystemML, ApacheFlink, and DeepLearning4J. He also contributes to various open source projects. Additionally, he is currently writing a chapter on Hyperledger for a book on Blockchain technologies.

Pradeepta Mishra

Pradeepta Mishra is a data scientist, predictive modeling expert, deep learning and machine learning practitioner, and econometrician. He currently leads 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, a proprietary big data platform, and data science expertise. He holds a patent for enhancing the planogram design for the retail industry. Pradeepta has published and presented research papers at IIM Ahmedabad, India. He is a visiting faculty member at various leading B-schools and regularly gives talks on data science and machine learning.

Pradeepta has spent more than 10 years solving 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.

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