Descriptive business analytics equips students with essential skills to analyze and summarize business data. It covers statistical techniques, data management, exploratory analysis, and visualization methods. The course emphasizes practical applications in marketing, finance, HR, and operations, reinforcing its role in decision-making. Designed for business students and professionals, it requires basic statistics knowledge and familiarity with spreadsheet tools.
Predictive Business Analytics equips students with the skills to analyze historical data and forecast trends using statistical and machine learning techniques. Key topics include regression models, classification methods, time series forecasting, and business applications of machine learning. Through case studies and hands-on projects, students will learn how predictive analytics enhances decision-making in marketing, finance, HR, and operations. The course requires a basic understanding of statistics and familiarity with tools.
R Programming is a powerful statistical computing language widely used for data analysis, visualization, and machine learning. This course introduces fundamental concepts such as data structures (vectors, matrices, lists, and data frames), control structures (loops and conditional statements), and functions. Students will learn how to manipulate data, perform exploratory data analysis, and create compelling visualizations using packages like ggplot2 and dplyr. The course also covers statistical modeling, including regression analysis and hypothesis testing, along with best practices for writing efficient R code. Designed for beginners and intermediate learners, it provides hands-on experience through coding exercises and real-world datasets, preparing students for careers in data science, analytics, and research.