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Learning Path: R: Data Analysis and Machine Learning with R

Conquer the wider world of data science with R


With its popularity as a statistical programming language rapidly increasing with each passing day, R is becoming the preferred tool of choice for data analysts and data scientists who want to make sense of large amounts of data as quickly as possible. R has a rich set of libraries that can be used for basic as well as advanced data analysis and machine learning tasks.

So, if you're looking to understand how the R programming environment and packages can be used to for data analysis and machine learning, then you should surely go for this Learning Path.

Packt’s Video Learning Path is 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 it.

This Learning Path starts by organizing the data and then predicting it. You will work through various examples wherein you will explore RStudio and libraries, how to apply linear regression, how to score test sets, and plotting test results on a Cartesian plane. You will also see how to use logistic regression to predict for a classification problem on automobile data. Further, you will learn different ways to use R to generate professional analysis reports. Moving ahead, you will learn various important analysis and machine learning tasks that you can try out with associated and readily available data with the help of examples. Finally, you will learn advanced data analysis concepts such as cluster analysis, time-series analysis, PCA (Principal Component Analysis), sentiment analysis, and spatial data analysis.

By the end of this Learning Path, you will have a solid understanding of how to efficiently perform data analysis and machine learning tasks using R.

About the Author:

For this course, we have combined the best works of these esteemed authors:

  • Tim Hoolihan currently works at DialogTech, a marketing analytics company focused on conversations. He is the senior director of data science there. Prior to that, he was CTO at Level Seven, a regional consulting company in the US Midwest. He is the organizer of the Cleveland R User Group.In his job, he uses deep neural networks to help automate of lot of conversation classification problems. In addition, he works on some side-projects researching other areas of artificial intelligence and machine learning.
  • ViswaViswanathan is an associate professor of computing and decision sciences at the Stillman School of Business in Seton Hall University. After completing his PhD in Artificial Intelligence,Viswa has taught extensively in diverse fields, including operations research, computer science, software engineering, management information systems, and enterprise systems. In addition to teaching at the university, hehas conducted training programs for industry professionals. He has written several peer-reviewed research publications in journals such as Operations Research, IEEE Software, Computers and Industrial Engineering, and International Journal of Artificial Intelligence in Education.
  • ShanthiViswanathan is an experienced technologist who has delivered technology management and enterprise architecture consultations to many enterprise customers. She has worked for Infosys Technologies, Oracle Corporation, and Accenture. As a consultant, Shanthi has helped several large organizations, such as Canon, Cisco, Celgene, Amway, Time Warner Cable, and GE, among others, in areas such as data architecture and analytics, master data management, service-oriented architecture, business process management, and modeling.
  • Dr. Bharatendra Rai is a professor of business statistics and operations management in the Charlton College of Business at UMass Dartmouth. He received his Ph.D. in Industrial Engineering from Wayne State University, Detroit. His two master's degrees include specializations in quality, reliability, and OR from Indian Statistical Institute and another in statistics from Meerut University, India. He teaches courses on topics such as analyzing big data, business analytics,and data mining, Twitter and text analytics, applied decision techniques, operations management, and data science for business. Dr. Rai has won awards for excellence and exemplary teamwork at Ford for his contributions in the area of applied statistics.

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