Last updated 7/2018MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHzLanguage: English | Size: 6.76 GB | Duration: 13h 58m
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, -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 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, -series analysis, Association mining, PCA (Principal Component Analysis), handling missing data, sennt 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 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 Eeering 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, chal, defense, and so on, in the areas of SPC, design of expents, quality eeering, 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 Eeering & System Safety, Quality Eeering, 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 Eeering. 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 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 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 Ee Lecture 117 Production Ready Recommender Ees 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 Sennt Analysis Lecture 133 Sennt Analysis Upon Tweets – Polarity Analysis Lecture 134 Sennt 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. 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