Get yourself trained on Learning Path: R: with this Online Training Learning Path: R: Powerful Data Analysis with R.
Online Training Learning Path: R: Powerful Data Analysis with R
Theres an increasing number of data being produced every day. This has led to the demand for skilled professionals who can analyze these data and make decisions. R is one of the popular tools which is widely used by data analysts for performing data analysis on real-world data.This Learning Path is the complete learning process to play with data. You will start with the most basic importing techniques for downloading compressed data from the Web. You will get introduced to how CRAN works and will demonstrate why viewers should use them. Next, you will learn to create static plots. Then, you will understand how to plot spatial data on interactive web platforms such as Google Maps and OpenStreetMap. You will learn advanced data analysis concepts such as cluster analysis, time-series analysis, association mining, PCA, handling missing data, sentiment analysis, spatial data analysis with R and QGIS, and advanced data visualization with Rs ggplot2 library. Finally, you will implement the various topics learned so far to analyze real-world datasets from various industry sectors. By the end of this Learning Path, you will learn how to perform data analysis on real-world data.For this course, we have combined the best works of these esteemed authors: Fabio Veronesi Fabio Veronesi obtained a Ph.D. in digital soil mapping from Cranfield University and then moved to ETH Zurich, where he has been working for the past three years as a postdoc. In his career, Dr. Veronesi worked at several topics related to environmental research: digital soil mapping, cartography and shaded relief, renewable energy and transmission line siting. During this time Dr. Veronesi specialized in the application of spatial statistical techniques to environmental data. Dr. Bharatendra Rai Dr. Bharatendra Rai is Professor of Business Statistics and Operations Management in the Charlton College of Business at UMass Dartmouth. 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.
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As a society, we spend hundreds of billions of dollars measuring the return on our financial assets. Yet, at the same time, we still haven’t found convincing ways of measuring the return on our investments in developing people.
And I get it: If my bank account pays me 1% a year, I can measure it to the penny. We’ve been collectively trained to expect neat and precise ROI calculations on everything, so when it’s applied to something as seemingly squishy as how effectively people are learning in the workplace, the natural inclination is to throw up our hands and say it can’t be done. But we need to figure this out. In a world where skills beat capital, the winners and losers of the next 30 years will be determined by their ability to attract and develop great talent.
Fortunately, corporate learning & development (L&D), like most business functions, is evolving quickly. We can embrace some level of ambiguity and have rigor when measuring the ROI of learning. It just might look a little different than an M.B.A. would expect to see in an Excel model.