by Dr. Bharatendra Rai | Released May 2017 | ISBN: 9781787125100

Master R's advanced techniques to solve real-world problems in data analysis and gain valuable insights from your data About This VideoDelve into data visualization and regression-based methods with R/RStudio. Conduct independent data analyses. Tackle multiple linear regression with RLearn the essentials of ROC curvesExplore multinomial logistic regression with categorical response variables at three levelsMaster prediction and model assessment.In DetailWith 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. If you have a basic understanding of data analysis concepts and want to take your skills to the next level, this video is for you. Spanning over four hours, it contains carefully 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.Throughout the video, readers will use the various topics they've learned about to analyze real-world datasets from various industry sectors. By the end of the tutorial, readers will have a thorough understanding of advanced data analysis concepts and how to implement them in R. Show and hide more

1. Chapter 1 : Data Visualization with R/RStudio
   • The Course Overview 00:03:24
   • Getting Started and Data Exploration with R/RStudio 00:28:17
   • Introduction to Visualization 00:20:29
   • Interactive Visualization 00:10:35
   • Geographic Plots 00:10:04
   • Advanced Visualization 00:11:00
2. Chapter 2 : Data Partitioning, Multiple Linear Regression, and Multicollinearity
   • Getting Introductory Concepts 00:06:46
   • Data Partitioning with R 00:13:49
   • Multiple Linear Regression with R 00:11:59
   • Multicollinearity Issues 00:07:31
3. Chapter 3 : Logistic Regression
   • Logistic Regression with Categorical Response Variables at two Levels 00:13:46
   • Logistic Regression Model and Interpretation 00:04:24
   • Misclassification Error and Confusion Matrix 00:06:41
   • ROC Curves 00:06:02
   • Prediction and Model Assessment 00:08:43
4. Chapter 4 : Multinomial Logistic Regression
   • Multinomial Logistic Regression with Categorical Response Variables at 3Levels 00:07:29
   • Multinomial Logistic Regression Model and Its Interpretation 00:08:14
   • Misclassification Error and Confusion Matrix 00:06:34
   • Prediction and Model Assessment 00:09:55
5. Chapter 5 : Ordinal Logistic Regression
   • Ordinal Logistic Regression with R 00:12:55
   • Ordinal Logistic Regression Model and Interpretation 00:04:41
   • The Misclassification Error and Confusion Matrix 00:04:28
   • Prediction and Model Assessment 00:05:52
Show and hide more