R Data Analysis With Projects – Hands On: 3-In-1

Master R's basic and advanced techniques to solve real-world problems in data analysis and gain valuable insights from y

What you'll learn

Learn to import and export data in various formats in R

Perform advanced statistical data analysis

Visualize your data on Google or Open Street maps

Learn how to handle vector and raster data in R

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

Explore multinomial logistic regression with categorical response variables at three levels

Deploy advanced data analysis techniques to gather useful business insights from your data

Use the popular R packages to analyze clusters, time-series data, and more

Requirements

Basic knowledge of R programming is assumed.

Description

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.
This comprehensive 3-in-1 course delivers you the ability to conduct data analysis in practical contexts with R, using core language packages and tools. The goal is to provide analysts and data scientists a comprehensive learning course on how to manipulate and analyse small and large sets of data with R. You will learn to implement your learning with real-world examples of data analysis. You will also work on three different projects to apply the concepts of data analysis.


This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.


The first course, Learning Data Analysis with R, starts off with covering the most basic importing techniques to download compressed data from the web and will help you learn more advanced ways to handle the most difficult datasets to import. You will then learn how to create static plots and how to plot spatial data on interactive web platforms such as Google Maps and Open Street maps. You will learn to implement your learning with real-world examples of data analysis.


The second course, Mastering Data Analysis with R, 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.


The third course, R Data Analytics Projects, takes you on a data-driven journey that starts with the very basics of R data analysis and machine learning. You will then work on three different projects to apply the concepts of machine learning and data analysis. Each project will help you to understand, explore, visualize, and derive domain- and algorithm-based insights.


By the end of this Learning Path, you'll gain in-depth knowledge of the basic and advanced data analysis concepts in R and will be able to put your learnings into practice.

Meet Your Expert(s):

We have the best work of the following esteemed author(s) to ensure that your learning journey is smooth:



● 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 such as 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 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 of 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.● Raghav Bali is a Data Scientist at Optum, a United Health Group Company. He is part of the Data Science group where his work is enabling United Health Group develop data driven solutions to transform healthcare sector. He primarily works on data science, analytics and development of scalable machine learning based solutions. In his previous role at Intel as a Data Scientist, his work involved research and development of enterprise solutions in the infrastructure domain leveraging cutting edge techniques from machine learning, deep learning and transfer learning. He has also worked in domains such as ERP and finance with some of the leading organizations of the world. Raghav has a master's degree (gold medalist) in Information Technology from International Institute of Information Technology, Bangalore. Raghav has authored several books on Machine Learning and Analytics using R and Python. He is a technology enthusiast who loves reading and playing around with new gadgets and technologies.

● Dipanjan Sarkar is a Data Scientist at Intel, on a mission to make the world more connected and productive. He primarily works on data science, analytics, business intelligence, application development, and building large-scale intelligent systems. He holds a master of technology degree in Information Technology with specializations in Data Science and Software Engineering. He is also an avid supporter of self-learning. He has been an analytics practitioner for several years now, specializing in machine learning, natural language processing, statistical methods and deep learning.

Overview

Section 1: Learning Data Analysis with R

Lecture 1 The Course Overview

Lecture 2 Importing Data from Tables (read.table)

Lecture 3 Downloading 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 Transforming from data.frame to SpatialPointsDataFrame

Lecture 10 Understanding Projections

Lecture 11 Basic time/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 Time-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 Deterministic 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

Section 3: R Data Analytics Projects

Lecture 99 The Course Overview

Lecture 100 Delving into the Basics of R

Lecture 101 Data Structures in R

Lecture 102 Lists and Data Frames

Lecture 103 Working with Functions

Lecture 104 Controlling Code Flow

Lecture 105 Advanced Constructs

Lecture 106 Next Steps with R

Lecture 107 Machine Learning Basics

Lecture 108 Algorithms in Machine Learning

Lecture 109 Supervised Learning Algorithms

Lecture 110 Unsupervised Learning Algorithms

Lecture 111 Market Basket Analysis

Lecture 112 Evaluating a Product Contingency Matrix

Lecture 113 Frequent Itemset Generation

Lecture 114 Association Rule Mining

Lecture 115 Understanding Recommendation Systems

Lecture 116 Building a Recommender Engine

Lecture 117 Production Ready Recommender Engines

Lecture 118 Understanding Credit Risk

Lecture 119 Data Preprocessing

Lecture 120 Data Analysis and Transformation

Lecture 121 Analyzing the Dataset

Lecture 122 Data Preprocessing

Lecture 123 Feature Selection

Lecture 124 Modeling Using Logistic Regression

Lecture 125 Modeling Using Support Vector Machines

Lecture 126 Modeling Using Decision Trees

Lecture 127 Modeling Using Random Forests

Lecture 128 Modeling Using Neural Networks

Lecture 129 Getting Started with Twitter APIs

Lecture 130 Twitter Data Mining

Lecture 131 Hierarchical Clustering and Topic Modeling

Lecture 132 Understanding Sentiment Analysis

Lecture 133 Sentiment Analysis Upon Tweets – Polarity Analysis

Lecture 134 Sentiment Analysis Upon Tweets –Classification-Based Algorithms

This learning path is for data scientists, data analysts, and statisticians who wish to learn how to analyze data with R.