R Data Analysis With Projects – Hands On 3-In-1
Last updated 7/2018
MP4 | Video: h264, 1280x720 | Audio: AAC, 44.1 KHz
Language: 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, 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.
Homepage
https://www.udemy.com/course/r-data-analysis-with-projects-hands-on-3-in-1/
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