Statistics & Probability For Business Analytics

Learn descriptive statistics, inferential statistics, probability, correlation analysis, and computational statistics

What you'll learn

Learn the basic fundamentals of business analytics, its workflow, statistics and probability applications in this field

Learn about computational statistics using Python, Pandas, Numpy, Matplotlib, Scipy, Seaborn, and Scikit Learn

Learn about descriptive statistics and inferential statistics

Learn how to calculate mean, median, mode, sum, max, and min

Learn how to calculate standard deviation, variance, and range

Learn how to split data into quartiles and visualise data using histogram

Learn how to conduct hypothesis testing & t-test

Learn how to calculate confidence interval

Learn how to predict house prices using linear regression

Learn how to analyze price differences using ANOVA

Learn how to calculate joint probability and conditional probability

Learn how to calculate probability using Bayes Theorem

Learn how to calculate expected value

Learn about discrete distribution and continuous distribution

Learn how to calculate binomial distribution and poisson distribution

Learn how to calculate normal distribution, uniform distribution, and exponential distribution

Learn how to perform correlation analysis and calculate correlation coefficient

Learn how to predict customer churn using logistic regression

Requirements

No previous experience in business analytics is required

Basic knowledge in statistics

Description

Welcome to Statistics & Probability for Business Analytics course. This is a comprehensive business math tutorial designed for data analysts and business analysts. This course will cover basic to intermediate statistics and probability concepts, computational techniques using Python and their practical applications in the field of business analytics. This course is a perfect combination between statistics and Python, making it an ideal opportunity to practice your business analytics skills while improving your statistical knowledge. In the introduction session, you will learn the basic fundamentals of business analytics, statistics and probability applications in this field, and also business analytics workflow. Then, in the next section, we will start the first lesson where you will learn about descriptive statistics. This section will cover mean, median, mode, standard deviation, range, variance, and quartile. These concepts will provide you with the foundational tools to summarize data, identify patterns, and gain meaningful business insights. Afterward, in the second lesson, you will learn about inferential statistics. This section will cover hypothesis testing, confidence interval, regression analysis, and analysis of variance. These methods will help you to make data driven predictions and draw conclusions from sample data. Then, in the third lesson, we will cover the fundamentals of probability. This section will introduce key concepts such as basic probability calculation, joint probability, conditional probability, Bayes’ theorem, and expected value. These probability concepts will assist you to assess the likelihood of various business scenarios and outcomes. Meanwhile, in the fourth lesson, we will learn about probability distribution. This section will cover discrete distributions such as Binomial and Poisson, as well as continuous distributions including Normal, Uniform, and Exponential. Additionally, we will explore their practical applications in business analytics. By learning these concepts, you will be able to model uncertainty, analyze patterns, and apply probability distributions to solve real-world business problems effectively. Then, in the fifth lesson, we will learn about correlation analysis, specifically, we will measure the strength and direction of relationships between variables. At the end of the course, we will apply all statistics and probability concepts that we have learnt to real-world business case studies, where we will use Python to perform data analysis, build statistical models, and calculate probability to predict customer churn.First of all, before getting into the course, we need to ask ourselves these questions, why should we learn about statistics and probability? Why are they crucial for business analytics? Well, here is my answer, statistics and probability enable businesses to analyze data effectively, identify patterns, and understand trends with greater accuracy. They help in optimizing processes, forecasting future outcomes, and evaluating risks, ensuring that every decision is backed by evidence. By applying these concepts, businesses can enhance efficiency, improve strategies, and make better data driven decisions.Below are things that you can expect to learn from this course:Learn the basic fundamentals of business analytics, its workflow, statistics and probability applications in this fieldLearn about computational statistics using Python, Pandas, Numpy, Matplotlib, Scipy, Seaborn, and Scikit LearnLearn about descriptive statistics and inferential statisticsLearn how to calculate mean, median, mode, sum, max, and minLearn how to calculate standard deviation, variance, and rangeLearn how to split data into quartiles and visualise data using histogramLearn how to conduct hypothesis testing & t-testLearn how to calculate confidence intervalLearn how to predict house prices using linear regressionLearn how to analyze price differences using ANOVALearn how to calculate joint probability and conditional probabilityLearn how to calculate probability using Bayes TheoremLearn how to calculate expected valueLearn about discrete distribution and continuous distributionLearn how to calculate binomial distribution and poisson distributionLearn how to calculate normal distribution, uniform distribution, and exponential distributionLearn how to perform correlation analysis and calculate correlation coefficientLearn how to predict customer churn using logistic regression

Overview

Section 1: Introduction to the Course

Lecture 1 Introduction

Lecture 2 Table of Contents

Lecture 3 Whom This Course is Intended for?

Section 2: Tools, IDE, and Datasets

Lecture 4 Tools, IDE, and Datasets

Section 3: Statistics & Probability Applications in Business Analytics

Lecture 5 Statistics & Probability Applications in Business Analytics

Section 4: Finding & Downloading Datasets From Kaggle

Lecture 6 Finding & Downloading Datasets From Kaggle

Section 5: Calculating Mean, Median, Mode, Sum, Max, and Min

Lecture 7 Calculating Mean, Median, Mode, Sum, Max, and Min

Section 6: Calculating Standard Deviation, Variance, and Range

Lecture 8 Calculating Standard Deviation, Variance, and Range

Section 7: Splitting Data Into Quartiles & Visualising Data with Histogram

Lecture 9 Splitting Data Into Quartiles & Visualising Data with Histogram

Section 8: Conducting Hypothesis Testing & T-Test

Lecture 10 Conducting Hypothesis Testing & T-Test

Section 9: Calculating Confidence Interval

Lecture 11 Calculating Confidence Interval

Section 10: Predicting House Prices with Linear Regression

Lecture 12 Predicting House Prices with Linear Regression

Section 11: Analyzing Price Difference with ANOVA

Lecture 13 Analyzing Price Difference with ANOVA

Section 12: Calculating Joint Probability & Conditional Probability

Lecture 14 Calculating Joint Probability & Conditional Probability

Section 13: Calculating Probability with Bayes Theorem

Lecture 15 Calculating Probability with Bayes Theorem

Section 14: Calculating Expected Value

Lecture 16 Calculating Expected Value

Section 15: Calculating Binomial Distribution & Poisson Distribution

Lecture 17 Calculating Binomial Distribution & Poisson Distribution

Section 16: Calculating Normal Distribution, Uniform Distribution, Exponential Distribution

Lecture 18 Calculating Normal Distribution, Uniform Distribution, Exponential Distribution

Section 17: Performing Correlation Analysis & Calculating Correlation Coefficient

Lecture 19 Performing Correlation Analysis & Calculating Correlation Coefficient

Section 18: Predicting Customer Churn with Logistic Regression

Lecture 20 Predicting Customer Churn with Logistic Regression

Section 19: Conclusion & Summary

Lecture 21 Conclusion & Summary

Data analysts who are interested in learning computational statistics and probability,Business analysts who are interested in leveraging big data to solve complex business problems