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Learning Data Analysis with R

Video Description

Find, process, analyze, manipulate, and crunch data in R

About This Video

  • Harness the power of Open Data to propel your career or business to a new level

  • Manipulate and analyze small and large sets of data with R

  • Practice with real world examples of Data Analysis and build a strong foundation for moving into Data Science

  • In Detail

    R is a programming language and software environment for statistical computing and graphics supported by the R Foundation for Statistical Computing. The R language is widely used among statisticians and data miners for developing statistical software and data analysis.

    This video delivers viewers the ability to conduct data analysis in practical contexts with R, using core language packages and tools. The end 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. It will introduce how CRAN works and will demonstrate why viewers should use them.

    You will start with the most basic importing techniques, to downloading compressed data from the web and learn of more advanced ways to handle even the most difficult datasets to import. Next, you will move on to create static plots, while the second will show how to plot spatial data on interactive web platforms such as Google Maps and Open Street maps. Finally, you will learn to implement your learning with real-world examples of data analysis.

    This video will lay the foundations for deeper applications of data analysis, and pave the way for advanced data science.

    Table of Contents

    1. Chapter 1 : Importing Data in Table Format
      1. The Course Overview 00:04:16
      2. Importing Data from Tables (read.table) 00:02:31
      3. Downloading Open Data from FTP Sites 00:04:03
      4. Fixed-Width Format 00:04:25
      5. Importing with read.lines (The Last Resort) 00:03:21
      6. Cleaning Your Data 00:02:37
    2. Chapter 2 : Handling the Temporal Component
      1. Loading the Required Packages 00:04:09
      2. Importing Vector Data (ESRI shp and GeoJSON) 00:04:03
      3. Transforming from data.frame to SpatialPointsDataFrame 00:02:50
      4. Understanding Projections 00:03:06
      5. Basic time/dates formats 00:03:51
    3. Chapter 3 : Importing Raster Data
      1. Introducing the Raster Format 00:04:59
      2. Reading Raster Data in NetCDF 00:06:10
      3. Mosaicking 00:02:53
      4. Stacking to Include the Temporal Component 00:04:11
    4. Chapter 4 : Exporting Data
      1. Exporting Data in Tables 00:03:12
      2. Exporting Vector Data (ESRI shp File) 00:02:21
      3. Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids) 00:02:43
      4. Exporting Data for WebGIS Systems (GeoJSON, KML) 00:02:40
    5. Chapter 5 : Descriptive Statistics
      1. Preparing the Dataset 00:07:44
      2. Measuring Spread (Standard Deviation and Standard Distance) 00:03:23
      3. Understanding Your Data with Plots 00:05:51
      4. Plotting for Multivariate Data 00:03:02
      5. Finding Outliers 00:03:50
    6. Chapter 6 : Manipulating Vector Data
      1. Introduction 00:03:37
      2. Re-Projecting Your Data 00:02:54
      3. Intersection 00:03:07
      4. Buffer and Distance 00:03:22
      5. Union and Overlay 00:03:32
    7. Chapter 7 : Manipulating Raster Data
      1. Introduction 00:04:44
      2. Converting Vector/Table Data into Raster 00:04:00
      3. Subsetting and Selection 00:03:16
      4. Filtering 00:04:58
      5. Raster Calculator 00:04:44
    8. Chapter 8 : Visualizing Spatial Data
      1. Plotting Basics 00:05:15
      2. Adding Layers 00:05:45
      3. Color Scale 00:04:52
      4. Creating Multivariate Plots 00:09:10
      5. Handling the Temporal Component 00:03:20
    9. Chapter 9 : Interactive Maps
      1. Introduction 00:02:33
      2. Plotting Vector Data on Google Maps 00:05:46
      3. Adding Layers 00:04:41
      4. Plotting Raster Data on Google Maps 00:04:19
      5. Using Leaflet to Plot on Open Street Maps 00:09:04
    10. Chapter 10 : Creating Global Economic Maps with Open Data
      1. Introduction 00:02:22
      2. Importing Data from the World Bank 00:05:09
      3. Adding Geocoding Information 00:05:38
      4. Concluding Remarks 00:03:49
    11. Chapter 11 : Point Pattern Analysis of Crime in the UK
      1. Theoretical Background 00:15:04
      2. Introduction 00:07:37
      3. Intensity and Density 00:07:39
      4. Spatial Distribution 00:10:02
      5. Modelling 00:06:42
    12. Chapter 12 : Cluster Analysis of Earthquake Data
      1. Theoretical Background 00:04:31
      2. Data Preparation 00:05:51
      3. K-Means Clustering 00:05:27
      4. Optimal Number of Clusters 00:05:18
      5. Hierarchical Clustering 00:06:34
      6. Concluding 00:04:33
    13. Chapter 13 : Time Series Analysis of Wind Speed Data
      1. Theoretical Background 00:04:34
      2. Reading Time-Series in R 00:06:38
      3. Subsetting and Temporal Functions 00:05:15
      4. Decomposition and Correlation 00:07:33
      5. Forecasting 00:04:32
    14. Chapter 14 : Geostatistics
      1. Theoretical Background 00:04:42
      2. Data Preparation 00:06:21
      3. Mapping with Deterministic Estimators 00:06:57
      4. Analyzing Trend and Checking Normality 00:04:58
      5. Variogram Analysis 00:05:53
      6. Mapping with kriging 00:06:18
    15. Chapter 15 : Regression and Statistical Learning
      1. Theoretical Background 00:04:09
      2. Dataset 00:02:37
      3. Linear Regression 00:06:07
      4. Regression Trees 00:04:13
      5. Support Vector Machines 00:05:06