Business Analytics

Course Description

This comprehensive course on Business Analytics is designed to equip you with the essential skills and knowledge needed to analyze data and make data-driven decisions. The course begins with an overview of business analytics, followed by an introduction to Python programming for data analysis. You’ll explore key statistical concepts like probability distributions, regression analysis, and time series analysis. Additionally, the course covers advanced machine learning models, including decision trees, random forests, and support vector machines. Practical data analysis tasks and real-time projects provide hands-on experience, making this course ideal for aspiring business analysts.

What Will You Learn?

• Understand the key concepts and applications of business analytics
• Learn Python programming for data analysis
• Explore various probability distributions and their applications
• Master statistical tests, including T-tests and Z-tests
• Develop skills in regression analysis, including linear, logistic, polynomial, and exponential regression
• Gain insights into time series analysis and its applications
• Understand and apply advanced machine learning models like decision trees, random forests, and SVMs
• Get hands-on experience with practical data analysis tasks and real-time projects

Course Curriculum

Introduction to Business Analytics

• Overview of Business Analytics
• Key Concepts and Applications

Basic Concepts of Python

• Introduction to Python Programming
• Python for Data Analysis

Probability Distributions

• Binomial Distribution
• Bernoulli Distribution
• Poisson Distribution
• Normal Distribution

Statistical Tests

• Introduction to T-Test
• T-Test and Z-Test Comparisons
• Z-Test Numericals

Regression Analysis

• Linear Regression
  • Simple Linear Regression
  • Simple Linear Regression: Real Problems
• Multiple Linear Regression
• Logistic Regression
• Polynomial Regression
• Exponential Regression
• Lasso and Ridge Regression

Time Series Analysis

• Basics of Time Series Analysis
• Applications and Techniques

Machine Learning Models

• Decision Tree Introduction
• Random Forest Classification
• Support Vector Machines (SVM)

Natural Language Processing (NLP)

• Introduction to NLP
• NLP Practice: Advanced Techniques

Practical Data Analysis

• Multiple Regressions on Dataset
• Simple Linear Regression Analysis
• Polynomial and Exponential Regression

Real-Time Tasks

• Simple Linear Regression
• Multiple Variable Linear Regression
• Polynomial and Exponential Regression
• Logistic Regression
• IPL Matches Analysis