Get yourself trained on LEARNING PATH: R: with this Online Training LEARNING PATH: R: Advanced Data Analysis with R.
Online Training LEARNING PATH: R: Advanced Data Analysis with R
R offers a large variety of packages and libraries for fast and accurate data analysis and visualization. As a result, its one of the most popularly used languages by data scientists and analysts, or anyone who wants to perform data analysis. With its popularity as a statistical programming language rapidly increasing with each passing day, R is increasingly 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 tasks. R can be called as a good or the best choice for data analysis for the following reasons:Data visualization: This allows analyzing data from angles which are not clear in unorganized or tabulated data. R has many tools that can help in data visualization, analysis, and representationSpecificity: R is a language designed especially for statistical analysis and data reconfiguration. All the R libraries focus on making one thing certain – to make data analysis easier, more approachable and detailed. Any new statistical method is first enabled through R libraries. This makes R a perfect choice for data analysis and projection.Machine learning: At some point in data science, a programmer may need to train the algorithm and bring in automation and learning capabilities to make predictions possible. R provides ample tools to developers to train and evaluate an algorithm and predict future events. Thus, R makes machine learning (a branch of data science) lot more easy and approachable.Availability: R programming language is open source. This makes it highly cost effective for a project of any size. Since it is open source, developments in R happen at a rapid scale and the community of developers is huge. All of this, along with a tremendous amount of learning resources makes R programming a perfect choice to begin learning R programming for data science.Data wrangling: Data wrangling is the process of cleaning messy and complex data sets to enable convenient consumption and further analysis. This is a very important and time taking process in data science. R has an extensive library of tools for database manipulation and wrangling.This comprehensive 2-in-1 course is a handy guide to take your understanding of data analysis with R to the next level. It will give you an exposure to real-world projects that focus on problems in finance, network analysis, social media, and more. From data manipulation to analysis to visualization in R, this comprehensive 2-in-1 course will teach you everything you need to know about building end-to-end data analysis pipelines using R. It will teach you how to deploy advanced data analysis techniques to gather useful business insights from your data. It will help you, use the popular R packages to analyze clusters, time-series data, and more. Contents and OverviewThis training program includes 2 complete courses, carefully chosen to give you the most comprehensive training possible.The first course, R Data Analysis Projects, covers understanding of data analysis with R. It describes how you can put to use your existing knowledge of data analysis in R to build highly efficient, end-to-end data analysis pipelines without any hassle. Youll start by building a content-based recommendation system, followed by building a project on sentiment analysis with tweets. Youll implement time-series modeling for anomaly detection and understand cluster analysis for streaming data. Youll work through projects on performing efficient market data research, building recommendation systems, and analyzing networks accurately, all provided with easy to follow code. With the help of these real-world projects, youll get a better understanding of the challenges faced when building data analysis pipelines, and see how you can overcome them without compromising on the efficiency or accuracy of your systems. The Learning Path covers some popularly used R packages such as dplyr, ggplot2, RShiny, and others, and includes tips on using them effectively.The second course, Mastering Data Analysis with R, covers selected advanced data analysis concepts such as: cluster analysis; time-series analysis; Association mining; PCA (Principal Component Analysis); handling missing data; sentiment analysis; spatial data analysis with R and QGIS; advanced data visualization with R and ggplot2.>. It is an in-depth content balanced with tutorials that put the theory into practice. This course is a practical tutorial to help you get beyond the basics of data analysis with R, using real-world datasets and examples.By the end of this training program youll have a better understanding of data analysis with R as well as advanced data analysis concepts, and will be able to put your knowledge to practical use without any hassle.About the AuthorsGopi Subramanian is a scientist and author with over 18 years of experience in the fields of data mining and machine learning. During the past decade, he has worked extensively in data mining and machine learning, solving a variety of business problems. He has 16 patent applications with the US and Indian patent offices and several publications to his credit. He is the author of Python Data Science Cookbook by Packt Publishing.Dr. Bharatendra Rai is 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. He has over twenty years’ consulting and training experience, including industries such as automotive, cutting tool, electronics, food, software, chemical, defense, and so on, in the areas of SPC, design of experiments, quality engineering, problem solving tools, Six-Sigma, and QMS. His work experience includes extensive research experience over five years at Ford in the areas of quality, reliability, and six-sigma. His research publications include journals such as IEEE Transactions on Reliability, Reliability Engineering & System Safety, Quality Engineering, International Journal of Product Development, International Journal of Business Excellence, and JSSSE. He has been keynote speaker at conferences and presented his research work at conferences such as SAE World Conference, INFORMS Annual Meetings, Industrial Engineering Research Conference, ASQs Annual Quality Congress, Taguchi’s Robust Engineering Symposium, and Canadian RAMS. Dr. Rai has won awards for Excellence and exemplary teamwork at Ford for his contributions in the area of applied statistics. He also received an Employee Recognition Award by FAIA for his Ph.D. dissertation in support of Ford Motor Company. He is certified as ISO 9000 lead assessor from British Standards Institute, ISO 14000 lead assessor from Marsden Environmental International, and Six Sigma Black Belt from ASQ.
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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.
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