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# VIDEO TUTORIAL Learning Data Analysis with R

119,00â‚¬

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

• 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

### Data Analysis Course 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 course 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 training will lay the foundations for deeper applications of data analysis, and pave the way for advanced data science.

### What will you learn in this course?

Course plan
Chapter 1
Importing Data in Table Format
Chapter 2
Handling the Temporal Component
Chapter 3
Importing Raster Data
Chapter 4
Exporting Data
Chapter 5
Descriptive Statistics
Chapter 6
Manipulating Vector Data
Chapter 7
Manipulating Raster Data

### Detailed course plan

Chapter 1 : Importing Data in Table Format
21m13s

Lesson 1The Course Overview
Lesson 4Fixed-Width Format
Lesson 5Importing with readLines(The Last Resort)
Chapter 2 : Handling the Temporal Component
13m56s

Lesson 2Importing Vector Data (ESRI shp and GeoJSON)
Lesson 3Transforming from data.frame to SpatialPointsDataFrame
Lesson 4Understanding Projections
Lesson 5Basic time/dates formats
Chapter 3 : Importing Raster Data
18m13s

Lesson 1Introducing the Raster Format
Lesson 3Mosaicking
Lesson 4Stacking to Include the Temporal Component
Chapter 4 : Exporting Data
10m56s

Lesson 1Exporting Data in Tables
Lesson 2Exporting Vector Data (ESRI shp File)
Lesson 3Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids)
Lesson 4Exporting Data for WebGIS Systems (GeoJSON, KML)
Chapter 5 : Descriptive Statistics
23m50s

Lesson 1Preparing the Dataset
Lesson 2Measuring Spread (Standard Deviation and Standard Distance)
Lesson 3Understanding Your Data with Plots
Lesson 4Plotting for Multivariate Data
Lesson 5Finding Outliers
Chapter 6 : Manipulating Vector Data
16m32s

Lesson 1Introduction
Lesson 3Intersection
Lesson 4Buffer and Distance
Lesson 5Union and Overlay
Chapter 7 : Manipulating Raster Data
21m42s

Lesson 1Introduction
Lesson 2Converting Vector/Table Data into Raster
Lesson 3Subsetting and Selection
Lesson 4Filtering
Lesson 5Raster Calculator
Chapter 8 : Visualizing Spatial Data
28m22s

Lesson 1Plotting Basics
Lesson 3Color Scale
Lesson 4Creating Multivariate Plots
Lesson 5Handling the Temporal Component
Chapter 9 : Interactive Maps
26m23s

Lesson 1Introduction
Lesson 2Plotting Vector Data on Google Maps
Lesson 4Plotting Raster Data on Google Maps
Lesson 5Using Leaflet to Plot on Open Street Maps
Chapter 10 : Creating Global Economic Maps with Open Data
22m13s

Lesson 1Introduction
Lesson 2Importing Data from the World Bank
Lesson 4Concluding Remarks
Chapter 11 : Point Pattern Analysis of Crime in the UK
34m16s

Lesson 1Theoretical Background
Lesson 2Introduction
Lesson 3Intensity and Density
Lesson 4Spatial Distribution
Lesson 5Modelling
Chapter 12 : Cluster Analysis of Earthquake Data
32m14s

Lesson 1Theoretical Background
Lesson 2Data Preparation
Lesson 3K-Means Clustering
Lesson 4Optimal Number of Clusters
Lesson 5Hierarchical Clustering
Lesson 6Concluding
Chapter 13 : Time Series Analysis of Wind Speed Data
28m32s

Lesson 1Theoretical Background
Lesson 3Subsetting and Temporal Functions
Lesson 4Decomposition and Correlation
Lesson 5Forecasting
Chapter 14 : Geostatistics
35m09s

Lesson 1Theoretical Background
Lesson 2Data Preparation
Lesson 3Mapping with Deterministic Estimators
Lesson 4Analyzing Trend and Checking Normality
Lesson 5Variogram Analysis
Lesson 6Mapping with kriging
Chapter 15 : Regression and Statistical Learning
22m12s

Lesson 1Theoretical Background
Lesson 2Dataset
Lesson 3Linear Regression
Lesson 4Regression Trees
Lesson 5Support Vector Machines

With which software version is this tutorial compatible with?

R

What is the required level to follow this tutorial ?

beginner

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