Business Analytics With R: A Comprehensive Guide

Master business analytics using R to make data-driven decisions with real-world applications and statistical modeling.

What you'll learn
How to use R for business analytics, including data manipulation and statistical analysis.
The business analytics life cycle and how to deploy analytics models.
The fundamentals of statistics, probability, and distributions, including hypothesis testing.
Advanced forecasting techniques like ARIMA and time-series analysis.
How to create compelling data visualizations to communicate insights.

Requirements
Basic knowledge of statistics and business concepts is helpful. No prior programming experience is required, though familiarity with basic programming concepts will be beneficial. A willingness to learn R and apply it to real-world business analytics problems.

Description
Course IntroductionThis course is designed to teach students how to harness the power of R programming for business analytics. Whether you're an aspiring data scientist or a business professional, this course will guide you through every step—from understanding basic data concepts to implementing complex statistical models and machine learning techniques. You'll work with practical examples, data manipulation, visualization, and forecasting, giving you a solid foundation to analyze business data and drive decisions using R.Section-Wise WriteupSection 1: Introduction to Business Analytics and RThe course begins by introducing the concept of business analytics and its evolution in modern business. We start with a discussion on discriminant analysis and move into an introduction to R and its application in business analytics. This section also covers fundamental business examples, such as hotel data, to illustrate how analytics can be applied in real-world scenarios. You will learn about different types of data used in analytics, including ordinal data, and explore decision models used to solve business problems.Section 2: Business Analytics Life CycleThis section dives into the Business Analytics Life Cycle, providing insights into how analytics processes are structured. You'll learn about model deployment, which is critical for turning your models into actionable business strategies. We also explore the steps in the problem-solving process, introduce software commonly used in business analytics, and guide you through setting up R and R Studio for effective use in your analytics projects.Section 3: Understanding R ProgrammingR is the core tool used in this course, and here you'll get a comprehensive introduction to it. The section covers basic R functions, data types, and key concepts such as recycling rules, special numerical values, and logical conjunctions. You will also learn about arrays, matrices, and factors in R, along with how to work with repositories and install packages. The practical aspects of working with data, importing, and aggregating data will be demonstrated.Section 4: Data Manipulation & Statistics BasicsIn this section, you'll focus on data manipulation techniques like merging and data creation, followed by an introduction to basic statistics. You will learn how to compute variance, covariance, and cumulative frequency, while also getting hands-on experience with functions in R like head() and scatterplot(). The section also explores control flow, which helps in making decisions based on data.Section 5: Statistics, Probability & DistributionThis section covers core concepts of statistics and probability necessary for business analytics. You'll learn about random variables, discrete and continuous distributions, and how to calculate expected values. The section also explores binomial distributions and uniform random variables, alongside examples such as gambling and decision-making games like "Deal or No Deal."Section 6: Business Analytics Using RFocusing on advanced business analytics, this section delves into statistical concepts like Normal and t-distributions, along with tools for hypothesis testing. You'll work with real-world examples, such as SAT scores and birth weights, to understand estimation, confidence intervals, and central limit theorem. The section culminates in building confidence intervals and learning about kurtosis, all while gaining practical experience using R.Section 7: Examples, Testing & ForecastingThis section emphasizes hypothesis generation and testing using R. You will work with sample differences, calculate Z values, and perform one-sided P-value tests. Additionally, you will learn about forecasting, time-series analysis, and methods such as ARIMA and double exponential smoothing. These tools are essential for predicting future trends and making informed decisions in business.Section 8: Understanding VisualizationsData visualization is a powerful tool for business analytics, and in this section, you will master how to create effective visual representations of data in R. You'll learn why and how to visualize data, overlay plots, and use advanced graphs such as bubble charts. The section also covers the concept of ANOVA (Analysis of Variance) and regression modeling, providing you with the skills to build and interpret statistical models.ConclusionBy the end of this course, you will have a strong understanding of business analytics concepts and the practical skills to implement them using R. From basic data manipulation and statistical analysis to advanced forecasting and visualizations, this course will prepare you to tackle complex business problems with confidence. You'll be equipped to use R for data-driven decision-making and analysis, giving you the tools to succeed in any business analytics role.