Last updated 6/2017MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 1.38 GB | Duration: 9h 43m

Learn advanced techniques of R to solve real-world problems in data analysis

What you'll learn

Import and export data in various formats in R

Perform advanced statistical data analysis

Visualize your data on Google or OpenStreetMap

Enhance your data analysis skills and learn to handle even the most complex datasets

Learn how to handle vector and raster data in R

Delve into data visualization and regression-based methods with R/RStudio.

Tackle multiple linear regression with R

Explore multinomial logistic regression with categorical response variables at three levels

Requirements

You need to be familiar with the R programming language.

You should have RStudio installed on your system.

Description

There’s 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 perfog 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 ing 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, -series analysis, association mining, PCA, handling missing data, sennt analysis, spatial data analysis with R and QGIS, and advanced data visualization with R’s 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 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.

Overview

Section 1: Learning Data Analysis with R

Lecture 1 The Course Overview

Lecture 2 Importing Data from Tables (read.table)

Lecture 3 ing Open Data from FTP Sites

Lecture 4 Fixed-Width Format

Lecture 5 Importing with read.lines (The Last Resort)

Lecture 6 Cleaning Your Data

Lecture 7 Loading the Required Packages

Lecture 8 Importing Vector Data (ESRI shp and GeoJSON)

Lecture 9 Transfog from data.frame to SpatialPointsDataFrame

Lecture 10 Understanding Projections

Lecture 11 Basic /dates formats

Lecture 12 Introducing the Raster Format

Lecture 13 Reading Raster Data in NetCDF

Lecture 14 Mosaicking

Lecture 15 Stacking to Include the Temporal Component

Lecture 16 Exporting Data in Tables

Lecture 17 Exporting Vector Data (ESRI shp File)

Lecture 18 Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)

Lecture 19 Exporting Data for WebGIS Systems (GeoJSON, KML)

Lecture 20 Preparing the Dataset

Lecture 21 Measuring Spread (Standard Deviation and Standard Distance)

Lecture 22 Understanding Your Data with Plots

Lecture 23 Plotting for Multivariate Data

Lecture 24 Finding Outliers

Lecture 25 Introduction

Lecture 26 Re-Projecting Your Data

Lecture 27 Intersection

Lecture 28 Buffer and Distance

Lecture 29 Union and Overlay

Lecture 30 Introduction

Lecture 31 Converting Vector/Table Data into Raster

Lecture 32 Subsetting and Selection

Lecture 33 Filtering

Lecture 34 Raster Calculator

Lecture 35 Plotting Basics

Lecture 36 Adding Layers

Lecture 37 Color Scale

Lecture 38 Creating Multivariate Plots

Lecture 39 Handling the Temporal Component

Lecture 40 Introduction

Lecture 41 Plotting Vector Data on Google Maps

Lecture 42 Adding Layers

Lecture 43 Plotting Raster Data on Google Maps

Lecture 44 Using Leaflet to Plot on Open Street Maps

Lecture 45 Introduction

Lecture 46 Importing Data from the World Bank

Lecture 47 Adding Geocoding Information

Lecture 48 Concluding Remarks

Lecture 49 Theoretical Background

Lecture 50 Introduction

Lecture 51 Intensity and Density

Lecture 52 Spatial Distribution

Lecture 53 Modelling

Lecture 54 Theoretical Background

Lecture 55 Data Preparation

Lecture 56 K-Means Clustering

Lecture 57 Optimal Number of Clusters

Lecture 58 Hierarchical Clustering

Lecture 59 Concluding

Lecture 60 Theoretical Background

Lecture 61 Reading -Series in R

Lecture 62 Subsetting and Temporal Functions

Lecture 63 Decomposition and Correlation

Lecture 64 Forecasting

Lecture 65 Theoretical Background

Lecture 66 Data Preparation

Lecture 67 Mapping with Deteistic Estimators

Lecture 68 Analyzing Trend and Checking Normality

Lecture 69 Variogram Analysis

Lecture 70 Mapping with kriging

Lecture 71 Theoretical Background

Lecture 72 Dataset

Lecture 73 Linear Regression

Lecture 74 Regression Trees

Lecture 75 Support Vector Machines

Section 2: Mastering Data Analysis with R

Lecture 76 The Course Overview

Lecture 77 Getting Started and Data Exploration with R/RStudio

Lecture 78 Introduction to Visualization

Lecture 79 Interactive Visualization

Lecture 80 Geographic Plots

Lecture 81 Advanced Visualization

Lecture 82 Getting Introductory Concepts

Lecture 83 Data Partitioning with R

Lecture 84 Multiple Linear Regression with R

Lecture 85 Multicollinearity Issues

Lecture 86 Logistic Regression with Categorical Response Variables at two Levels

Lecture 87 Logistic Regression Model and Interpretation

Lecture 88 Misclassification Error and Confusion Matrix

Lecture 89 ROC Curves

Lecture 90 Prediction and Model Assessment

Lecture 91 Multinomial Logistic Regression with Categorical Response Variables at 3Levels

Lecture 92 Multinomial Logistic Regression Model and Its Interpretation

Lecture 93 Misclassification Error and Confusion Matrix

Lecture 94 Prediction and Model Assessment

Lecture 95 Ordinal Logistic Regression with R

Lecture 96 Ordinal Logistic Regression Model and Interpretation

Lecture 97 The Misclassification Error and Confusion Matrix

Lecture 98 Prediction and Model Assessment

This Video Learning Path is for those who are familiar with R and want to learn data analysis from scratch to an advanced level.

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