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

Description

Packt’s Video Learning Paths are a series of individual video products put together in a logical and stepwise manner such that each video builds on the skills learned in the video before 

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.

What You Will Learn!

• Get to know the basic concepts of R: the data frame and data manipulation
• Work with complex data sets and understand how to process data sets
• Explore graphs and the statistical measure in graphs
• Apply data management steps to handle large datasets
• Implement various dimension reduction techniques to handle large datasets
• Create predictive models in order to build a recommendation engine
• Acquire knowledge about the neural network concept drawn from computer science and its applications in data mining

Who Should Attend!

• This course is ideal for data analysts from novice to intermediate level. You should have prior knowledge of basic statistics and some programming language experience in any tool or platform. Familiarity with R will be an added advantage.

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