Data analysis is crucial to accurately predict the performance of an application. The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins.
DurationData analysis is crucial to accurately predict the performance of an application. The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins.
Once you are comfortable with the basics, you will read ahead and learn all about data visualization and graphics. You will learn data management techniques such as pivots, aggregations, and dealing with missing values.
With this various case studies and examples, this course gives you the knowledge to confidently start your career in the field of data science. This is a hands-on guide that walks you through concepts with examples using built-in R data.
Every topic is enriched with a supporting example that highlights the concept, followed by activities that will gradually build into a full data science project to showcase skills learned. You will perform an end-to-end analysis, starting a data science portfolio.
Audience
This course is for analysts who are looking to grow their data science skills beyond the tools they have used before, such as MS Excel and other statistical tools.
Objectives
The course begins by getting you started with R, including basic programming and data import, data visualization, pivoting, merging, aggregating, and joins. You will learn how to:
Use the basic programming concepts of R such as loading packages, arithmetic functions, data structures, and flow control
Import data to R from various formats, such as CSV, Excel, and SQL
Clean data by handling missing values and standardizing fields
Perform univariate and bivariate analysis using ggplot2
Create statistical summary and advanced plots, such as histograms, scatter plots, box plots, and interaction plots
Apply data management techniques, such as factors, pivots, aggregation, merging, and dealing with missing values, on the example data sets
Contents
Lesson 1: INTRODUCTION TO R
Using R, RStudio, and Installing Useful Packages
Variable Types and Data Structures
Basic Flow Control
Data Import and Export
Getting Help with R
Lesson 2: DATA VISUALIZATION AND GRAPHICS
Creating Base Plots
ggplot2
Interactive Plots
Lesson 3: DATA MANAGEMENT
Factor Variables
Summarizing data
Splitting, Combining, Merging, and Joining Datasets
Global Knowledge is a worldwide leader in IT and technology training, helping individuals and organizations build the skills they need to succeed in an ever-changing world.
With an international network of offices and training facilities, Global Knowledge has the unique flexibility to deliver a broad portfolio of courses on over 100 countries online, in classrooms, and through a worldwide partner network.
Established in 1995, Global Knowledge has over 1000 employees worldwide and enables the success of more than 200,000 tech professionals each year.
In June 2021, Global Knowledge merged with Skillsoft to create the world’s leading corporate learning company.
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This course is aimed at those who want to incorporate R into their data analysis and business intelligence, particularly to replace existing Excel-based workflows.
You will figure out how to introduce and arrange programming essential for a factual programming condition and depict conventional programming language ideas as they are executed in an elevated level measurable language
This training program will let you learn how to use variables, matrix, functions and other models of R programming required for Data Science. At advance level, you learn to use R programming language in collaboration of Machine learning algorithm for Data Science.
This course is an introduction to using the R software and language, suitable for those who have never encountered R before. No previous statistical knowledge is assumed but participants should be familiar with Microsoft Windows and Excel.
Our R trainer has many years of experience and has written a number of books on R. This course is very hands-on. This means that there is plenty of time to experiment with what you are being taught, try things out for yourself and ask questions.
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