Master R Programming: Data Analysis & Visualization for Professionals

What you will learn:

R Programming Fundamentals
RStudio Setup and Configuration
Data Types and Structures
Control Flow (if-else, loops)
Function Creation and Usage
Data Wrangling with dplyr
Data Tidying with tidyr
Data Visualization with ggplot2
Statistical Analysis
Hypothesis Testing
Regression Analysis
Introduction to Machine Learning (caret, randomForest)
Data Presentation and Reporting

Description

Unlock the power of R with our comprehensive online course! Learn data analysis, visualization, and statistical modeling through engaging, hands-on projects. Whether you're new to programming or looking to boost your data science skills, this course is your pathway to mastery.

Dive into real-world datasets spanning diverse fields like business, healthcare, and finance. You'll master essential techniques, from data cleaning and transformation using dplyr and tidyr, to creating compelling visualizations with ggplot2 and conducting statistical analyses.

We'll cover fundamental R programming concepts, data manipulation strategies, advanced statistical methods (including hypothesis testing and regression analysis), and even introduce machine learning with caret and randomForest. Our step-by-step approach ensures you gain practical, applicable skills, and you’ll learn how to present your findings effectively. Gain lifetime access, a certificate of completion, and become a confident data analyst.

Key Highlights:

Practical, project-based learning
Master data cleaning, transformation, and visualization
Conduct statistical analyses and hypothesis testing
Introduction to machine learning techniques
Lifetime access & certificate of completion

Start building your data science career today!

Curriculum

Introduction to R Programming

This section provides a solid foundation in R programming. You'll learn what R is, its history and applications, and how to install and configure R and RStudio. We cover fundamental R syntax and data types, including vectors, matrices, arrays, data frames, and lists. This foundational knowledge is crucial for all subsequent sections.

Control Flow and Functions

Master control flow in R through conditional statements (if-else) and loops (for, while). Learn how to create, use, and understand the arguments and scoping of functions in R. This section equips you with the building blocks for creating efficient and reusable code.

Data Wrangling with dplyr & tidyr

This section focuses on data manipulation and tidying using the powerful dplyr and tidyr packages. Learn techniques for filtering, selecting, mutating, and arranging data, as well as pivoting data using pivot_longer and pivot_wider. Efficient data wrangling is crucial for preparing data for analysis and visualization.

Data Visualization with ggplot2

Explore the world of data visualization with ggplot2. You'll learn to create various plot types, such as scatter plots, bar plots, line plots, and histograms. We'll teach you how to customize your plots with different colors, labels, and themes, and even create interactive plots. This section helps you communicate data insights effectively.

Statistical Analysis and Modeling

This section delves into statistical analysis, covering descriptive statistics (mean, median, standard deviation, quartiles), hypothesis testing (t-tests, chi-squared tests), and regression analysis (linear and multiple regression). You’ll gain the skills to analyze data, test hypotheses, and build predictive models.

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