R is a statistical computing language that is widely used in data analysis and statistics and is a highly regarded skill that is desired in candidates aspiring to get into the industry. If you too are an individual aspiring to or already in the data analysis or statistics industry looking to improve your skills in the area, this course in data analysis with R might just be the one you were looking for. This course is set to thoroughly educate you on R to use it effectively in your data analysis activities.
Data is the foundation in data analysis, data science and statistics, and this course will start off by teaching you how to import data from tables, FTP sites and read.lines, along with guidelines on how to clean the downloaded data to detect and rectify inaccurate data. You will then move learn about vector and raster data and on how to import, export and manipulate it in R as per your requirements. The descriptive aspects of statistics such as the dataset, spread, data plots, multivariate data and outliers will also be duly covered through this course.
Information has to be presented in an effective way that provides maximum data on the subject looked for. Many data visualization methods exist for effective information representation and this course is set to disclose many of these methods, with special attention to spatial data visualization. This course will also provide you an opportunity to improve your data analysis skills by examining the point pattern, cluster and time series analysis. By the end of this course, you will have an outstanding knowledge on R to tremendously improve the quality of your data analysis.
Global Edulink offers the most convenient path to gain recognised skills and training that will give you the opportunity to put into practice your knowledge and expertise in an IT or corporate environment. You can study at your own pace at Global Edulink and you will be provided with all the necessary material, tutorials, qualified course instructor, narrated e-learning modules and free resources which include Free CV writing pack, free career support and course demo to make your learning experience more enriching and rewarding.
The course will be directly delivered to you, and you have 12 months access to the online learning platform from the date you joined the course. The course is self-paced and you can complete it in stages, revisiting the lectures at any time.
The course is assessed online with a final, multiple-choice test, which is marked automatically. You will know instantly whether you have passed the course.
This certificate will enhance your resume for a number of jobs in the job market. You can also use this certificate to study further in the area or to make substantial progress in your career. Listed below are few of the jobs this certificate will benefit you in, along with the average UK salary per annum according to https://www.payscale.com/career-path-planner,
Code Files | |||
Learning Data Analysis with R – Code Files | |||
Section 1: Importing Data in Table Format | |||
1.1. The Course Overview | 00:00:00 | ||
1.2. Importing Data from Tables (read.table) | 00:00:00 | ||
1.3. Downloading Open Data from FTP Sites | 00:00:00 | ||
1.4. Fixed-Width Format | 00:00:00 | ||
1.5. Importing with read.lines (The Last Resort) | 00:00:00 | ||
1.6. Cleaning Your Data | 00:00:00 | ||
Section 2: Handling the Temporal Component | |||
2.1. Loading the Required Packages | 00:00:00 | ||
2.2. Importing Vector Data (ESRI shp and GeoJSON) | 00:00:00 | ||
2.3. Transforming from data.frame to SpatialPointsDataFrame | 00:00:00 | ||
2.4. Understanding Projections | 00:00:00 | ||
2.5. Basic time/dates formats | 00:00:00 | ||
Section 3: Importing Raster Data | |||
3.1. Introducing the Raster Format | 00:00:00 | ||
3.2. Reading Raster Data in NetCDF | 00:00:00 | ||
3.3. Mosaicking | 00:00:00 | ||
3.4. Stacking to Include the Temporal Component | 00:00:00 | ||
Section 4: Exporting Data | |||
4.1. Exporting Data in Tables | 00:00:00 | ||
4.2. Exporting Vector Data (ESRI shp File) | 00:00:00 | ||
4.3. Exporting Rasters in Various Formats (GeoTIFF, ASCII Grids) | 00:00:00 | ||
4.4. Exporting Data for WebGIS Systems (GeoJSON, KML) | 00:00:00 | ||
Section 5: Descriptive Statistics | |||
5.1. Preparing the Dataset | 00:00:00 | ||
5.2. Measuring Spread (Standard Deviation and Standard Distance) | 00:00:00 | ||
5.3. Understanding Your Data with Plots | 00:00:00 | ||
5.4. Plotting for Multivariate Data | 00:00:00 | ||
5.5. Finding Outliers | 00:00:00 | ||
Section 6: Manipulating Vector Data | |||
6.1. Introduction | 00:00:00 | ||
6.2. Re-Projecting Your Data | 00:00:00 | ||
6.3. Intersection | 00:00:00 | ||
6.4. Buffer and Distance | 00:00:00 | ||
6.5. Union and Overlay | 00:00:00 | ||
Section 7: Manipulating Raster Data | |||
7.1. Introduction | 00:00:00 | ||
7.2. Converting Vector/Table Data into Raster | 00:00:00 | ||
7.3. Subsetting and Selection | 00:00:00 | ||
7.4. Filtering | 00:00:00 | ||
7.5. Raster Calculator | 00:00:00 | ||
Section 8: Visualizing Spatial Data | |||
8.1. Plotting Basics | 00:00:00 | ||
8.2. Adding Layers | 00:00:00 | ||
8.3. Color Scale | 00:00:00 | ||
8.4. Creating Multivariate Plots | 00:00:00 | ||
8.5. Handling the Temporal Component | 00:00:00 | ||
Section 9: Interactive Maps | |||
9.1. Introduction | 00:00:00 | ||
9.2. Plotting Vector Data on Google Maps | 00:00:00 | ||
9.3. Adding Layers | 00:00:00 | ||
9.4. Plotting Raster Data on Google Maps | 00:00:00 | ||
9.5. Using Leaflet to Plot on Open Street Maps | 00:00:00 | ||
Section 10: Creating Global Economic Maps With Open Data | |||
10.1. Introduction | 00:00:00 | ||
10.2. Importing Data from the World Bank | 00:00:00 | ||
10.3. Adding Geocoding Information | 00:00:00 | ||
10.4. Concluding Remarks | 00:00:00 | ||
Section 11: Point Pattern Analysis of Crime in the UK | |||
11.1. Theoretical Background | 00:00:00 | ||
11.2. Introduction | 00:00:00 | ||
11.3. Intensity and Density | 00:00:00 | ||
11.4. Spatial Distribution | 00:00:00 | ||
11.5. Modelling | 00:00:00 | ||
Section 12: Cluster Analysis of Earthquake Data | |||
12.1. Theoretical Background | 00:00:00 | ||
12.2. Data Preparation | 00:00:00 | ||
12.3. K-Means Clustering | 00:00:00 | ||
12.4. Optimal Number of Clusters | 00:00:00 | ||
12.5. Hierarchical Clustering | 00:00:00 | ||
12.6. Concluding | 00:00:00 | ||
Section 13: Time Series Analysis of Wind Speed Data | |||
13.1. Theoretical Background | 00:00:00 | ||
13.2. Reading Time-Series in R | 00:00:00 | ||
13.3. Subsetting and Temporal Functions | 00:00:00 | ||
13.4. Decomposition and Correlation | 00:00:00 | ||
13.5. Forecasting | 00:00:00 | ||
Section 14: Geostatistics | |||
14.1. Theoretical Background | 00:00:00 | ||
14.2. Data Preparation | 00:00:00 | ||
14.3. Mapping with Deterministic Estimators | 00:00:00 | ||
14.4. Analyzing Trend and Checking Normality | 00:00:00 | ||
14.5. Variogram Analysis | 00:00:00 | ||
14.6. Mapping with kriging | 00:00:00 | ||
Section 15: Regression and Statistical Learning | |||
15.1. Theoretical Background | 00:00:00 | ||
15.2. Dataset | 00:00:00 | ||
15.3. Linear Regression | 00:00:00 | ||
15.4. Regression Trees | 00:00:00 | ||
15.5. Support Vector Machines | 00:00:00 | ||
Mock Exam | |||
Mock Exam : Learning Data Analysis with R | 00:40:00 | ||
Exam | |||
Exam : Learning Data Analysis with R | 00:40:00 | ||
Certificate Download Guide | |||
Certificate Download Guide | 00:03:00 |
Jayden Duncan
A very enjoyable course. Even if you are new to R, it is not intimidating. The course has been simplified to suit all levels of learners.
Chase Stewart
The course offered an overview of R and touched briefly on concepts and high-level theory. It doesn’t move fast, so it allows you to get comfortable with the speed. It is high-quality and practical content.
Dakota Jordan
This was the first course I took in analytics. While I was surprised to learn it was not at a beginner level, I decided to study it. The course does not focus too much on the basics, but that was fine with me. The course takes you to the next level of R but it was well-paced and explained very well.