Application Development and Programming

R Programming

Code: 2324

5 days

List Tuition : $2,595.00 USD

Course Overview

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This is a comprehensive course covering R, the world’s most popular programming language for statistical analysis. Even if you have no programming experience and little more than a grounding in the basics of mathematics, you’ll find everything you need to begin using R effectively for statistical analysis.

You’ll start with the basics, like how to handle data and write simple programs, before moving on to more advanced topics, like producing statistical summaries of your data and performing statistical tests and modeling. You’ll even learn how to create impressive data visualizations with R’s basic graphics tools and contributed packages, like ggplot2 and ggvis, as well as interactive 3D visualizations using the rgl package.

Learn how to use R to turn raw data into insight, knowledge, and understanding. This course also introduces you to R, RStudio, and the tidyverse, a collection of R packages designed to work together to make data science fast, fluent, and fun. Suitable for everyone (even those with no previous programming experience), R Programming is designed to get you doing data science as quickly as possible.

Combining detailed explanations with real-world examples and exercises, this 5-day course will provide you with a solid understanding of both statistics and the depth of R’s functionality. Use this training course as your doorway into the growing world of data analysis.

Business Analysts, Technical Managers, and Programmers

This course is loaded with hands-on exercises that take you from theory to practice, as you learn:

  • The fundamentals of programming in R, including how to write data frames, create functions, and use variables, statements, and loops
  •  Statistical concepts like exploratory data analysis, probabilities, hypothesis tests, and regression modeling, and how to execute them in R
  •  How to access R’s thousands of functions, libraries, and data sets
  •  How to draw valid and useful conclusions from your data
  •  How to create publication-quality graphics of your results

You’ll also learn how to:

  • Wrangle—transform your datasets into a form convenient for analysis
  •  Program—learn powerful R tools for solving data problems with greater clarity and ease
  •  Explore—examine your data, generate hypotheses, and quickly test them
  •  Model—provide a low-dimensional summary that captures true "signals" in your dataset
  •  Communicate—learn R Markdown for integrating prose, code, and results

1 - The Language
• Getting Started
• Numerics, Arithmetic, Assignment, and Vectors
• Matrices and Arrays
• Non-numeric Values
• Lists and Data Frames
• Special Values, Classes, and Coercion
• Basic Plotting
• Reading and Writing Files
2 - Programming
• Calling Functions
• Conditions and Loops
• Writing Functions
• Exceptions, Timings, and Visibility
3 - Statistics and Probability
• Elementary Statistics
• Basic Data Visualization
• Probability
• Common Probability Distributions
4 - Statistical Testing and Modeling
• Sampling Distributions and Confidence
• Hypothesis Testing
• Analysis of Variance
• Simple Linear Regression
• Multiple Linear Regression
• Linear Model Selection and Diagnostics
5 - Advanced Graphics
• Advanced Plot Customization
• Going Further with the Grammar of Graphics
• Defining Colors and Plotting in Higher Dimensions
• Interactive 3D Plots

Participants should have the general knowledge of statistics and programming

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