Business Statistics - Sampling Methods With Python And Excel

Learn Sampling Techniques with Hands on Python and Excel – Random, Stratified, Cluster, and More!

What you'll learn

Introduction to Business Statistics – Key concepts and importance of sampling.

Convenience Sampling – Quick and easy data collection.

Simple Random Sampling – Equal chance selection for all data points.

Systematic Sampling – Selecting every ‘nth’ data point.

Stratified Sampling – Dividing data into subgroups for better representation.

Cluster Random Sampling – Random selection of entire groups.

Hands-on Sampling in Python – Implement techniques using pandas and NumPy.

Sampling in Excel – Generate samples using Excel’s built-in statistical tools.

Python vs. Excel for Sampling – Strengths, differences, and best use cases.

Real-World Applications – How businesses use sampling for decision-making.

Practical Exercises & Case Studies – Apply concepts with industry-relevant examples.

Requirements

Familiarity with Python and Excel (basic knowledge is sufficient).

Python installed on your computer (Anaconda or standalone Python).

Excel installed on your system (any version with data analysis tools).

Willingness to learn and apply sampling techniques in real-world scenarios.

Description

Unlock the power of Business Statistics by mastering sampling techniques with Python and Excel! This course provides a practical, hands-on approach to understanding statistics and its applications in real-world data analysis.What You’ll Learn:Introduction to Statistics – Understand key statistical concepts and why sampling is crucial for data-driven decision-making.Types of Sampling Methods:Convenience Sampling – Quick and easy data collection.Simple Random Sampling – Every data point has an equal chance of being selected.Systematic Sampling – Selecting every ‘nth’ data point from a dataset.Stratified Sampling – Dividing data into meaningful subgroups before sampling.Cluster Random Sampling – Random selection of entire groups instead of individuals.Key Highlights:Hands-on Implementation in Python – Apply each sampling method using pandas and NumPy for real-world datasets.Sampling in Excel – Learn how to generate and analyze samples using Excel’s built-in statistical tools.Comparing Python vs. Excel Approaches – Explore the strengths, differences, and best use cases for each tool.Real-World Business Applications – Learn how businesses use sampling to improve decision-making, reduce costs, and optimize strategies.Practical Exercises and Case Studies –Relevant examples to solidify your understanding.By the end of this course, you’ll be confident in using Python and Excel for statistical sampling, enabling you to make better, data-driven decisions. Who Should Enroll?Business Analysts, Data Enthusiasts, and Students.Professionals looking to enhance their data analytics skills.Anyone interested in statistical methods and data science.Join now and take your business statistics skills to the next level!

Overview

Section 1: Introduction

Lecture 1 Introduction

Section 2: Sampling Technique

Lecture 2 Download PPT

Lecture 3 What is Population and Sample

Lecture 4 What is Sample Technique

Lecture 5 Random, Systematic, Stratified, Cluster Random Sampling

Section 3: Sampling in Excel

Lecture 6 Download Excel

Lecture 7 Convenience Sampling

Lecture 8 Random Sampling

Lecture 9 Random Sampling with Data Analysis toolpack

Lecture 10 Systematic Random Sampling

Lecture 11 Stratified Random Sampling

Lecture 12 Cluster Random Sampling

Section 4: Sampling Technique with Python

Lecture 13 Download Python Code and Data

Lecture 14 Sampling Technique with Python - Load Dataset

Lecture 15 Random Sampling

Lecture 16 Systematic Random Sampling

Lecture 17 Stratified Random Sampling

Lecture 18 Cluster Random Sampling

Section 5: Bonus

Lecture 19 Bonus

Data enthusiasts and students interested in statistical sampling methods.,Researchers and academics who want to apply sampling techniques in their studies.,Beginners in data science seeking practical experience with Python and Excel.