Business Statistics Part 2 - IIMBx

Course Description

Welcome to Part 2 of our comprehensive 4-part series on Business Statistics! This intermediate-level course, offered by IIMBx, is designed to equip you with advanced statistical knowledge and skills crucial for success in various business domains. Building upon the foundation laid in Part 1, this course delves deeper into the fascinating world of Descriptive Statistics, with a focus on probability and random variables.

What You'll Learn

• Advanced spreadsheet techniques for analyzing large datasets
• Formulation and answering of pertinent business questions using data
• Probabilistic description of random variables and derivation of key parameters
• Sampling techniques, including simple random sampling
• Modeling business phenomena using known distributions (Binomial, Poisson, Normal)
• Simulation of variables following prescribed distributions
• Introduction to R, an advanced statistical programming platform

Prerequisites

• Basic Algebra
• Some Calculus
• Familiarity with spreadsheet software
• Completion of Part 1 of the Business Statistics series (recommended)

Course Content

• Probability concepts and their application in business contexts
• Random variables and their distributions
• Binomial, Poisson, and Normal distributions
• Sampling techniques and their importance in statistical analysis
• Data simulation using known distributions
• Introduction to R programming for statistical analysis
• Analysis of large datasets (over a million rows)
• Real-world applications in economics, finance, and HR

Who This Course Is For

• Business students and professionals looking to enhance their statistical skills
• Aspiring data analysts and business intelligence professionals
• Managers and decision-makers who want to leverage data for better insights
• Anyone interested in applying statistical concepts to real-world business problems

Real-World Applications

• Financial analysis and risk assessment
• Market research and consumer behavior prediction
• Supply chain optimization
• Human resource management and performance analysis
• Economic forecasting and trend analysis
• Quality control and process improvement
• Data-driven decision making in various business contexts