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Business Analytics with R

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April 5, 2023

Business Analytics with R

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BusinessData ScienceFinance

Learn how to use R for business analytics in this comprehensive course. Covering six units, you’ll explore data visualization, data manipulation, regression analysis, and more. By the end of the course, you’ll be able to use R to extract meaningful insights from complex data sets.

What Will I Learn?

  • R programming basics
  • Importing and cleaning data
  • Creating visualizations with ggplot2
  • Building regression models
  • Evaluating model performance
  • Creating predictive models
  • Using tidyverse for data manipulation
  • Working with time series data
  • Data imputation techniques
  • Best practices for data analysis
Course Curriculum
Unit 1: Introduction to R and RStudio
This unit provides an introduction to R programming and the RStudio environment. You'll learn how to set up your workspace, use RStudio's interface, and create your first R script.
  • How to set up your RStudio environment
  • Understanding R data structures (vectors, matrices, data frames)
  • Basic R syntax and commands
  • How to load and install R packages
Unit 2: Data Import and Manipulation
You'll learn how to import data from various sources into R, including CSV and Excel files. You'll also learn how to manipulate and clean data using R's built-in functions and the tidyverse package.
  • How to import data from CSV, Excel, and other sources
  • Cleaning and transforming data using the dplyr package
  • Merging and joining datasets
  • Using regular expressions to extract and manipulate text data
Unit 3: Data Visualization
Data visualization is an important aspect of business analytics, and this unit teaches you how to create effective visualizations using R's ggplot2 package. You'll learn how to create scatter plots, bar charts, and more.
  • Creating basic plots (scatter plots, histograms, bar charts)
  • Customizing plots using ggplot2 themes and aesthetics
  • Creating multi-panel plots and facets
  • Using visualization to identify patterns and outliers in data
Unit 4: Exploratory Data Analysis
Exploratory data analysis is the process of analyzing data to summarize its main characteristics. This unit covers techniques such as summarizing data, identifying outliers, and creating histograms.
  • Understanding data distributions and measures of central tendency
  • Using summary statistics and visualizations to describe data
  • Detecting and handling missing values and outliers
  • Creating and interpreting box plots and density plots
Unit 5: Regression Analysis
Regression analysis is a statistical method used to analyze the relationship between two or more variables. In this unit, you'll learn how to perform linear regression analysis in R, including interpreting the output and evaluating the model's accuracy.
  • Understanding linear regression and correlation
  • Interpreting regression output (coefficients, intercepts, R-squared)
  • Evaluating model accuracy using residual plots and other diagnostics
  • Creating and interpreting multiple regression models
Unit 6: Predictive Modeling
Predictive modeling is the process of using data to make predictions about future events. This unit covers techniques such as decision trees, random forests, and k-nearest neighbors, and teaches you how to use these techniques in R.
  • Understanding decision trees and random forests
  • Creating and evaluating classification models using k-nearest neighbors
  • Using cross-validation to evaluate model accuracy
  • Understanding the trade-offs between model accuracy and complexity
$580.00

Material Includes

  • The course includes video lectures, hands-on exercises, quizzes, and a final project.
Durations: 65 hours
Lectures: 24
Students: Max 35
Level: Expert
Language: English
Certificate: No

Material Includes

  • The course includes video lectures, hands-on exercises, quizzes, and a final project.

Requirements

  • Students should have a basic understanding of statistics and data analysis. They should also have access to a computer with R and RStudio installed. All necessary software is available for free.

Audience

  • This course is designed for business professionals, data analysts, and anyone looking to develop skills in using R for business analytics.