# Business Statistics with Python

## What you'll learn

- Read S&P 500® Index ETF prices data and perform business statistics operations by installing related packages and running code on Python PyCharm IDE.
- Chart absolute frequency, relative frequency, cumulative absolute frequency and cumulative relative frequency histograms.
- Approximate sample mean, sample median central tendency measures and sample standard deviation, sample variance, sample mean absolute deviation dispersion measures.
- Estimate sample skewness, sample kurtosis frequency distribution shape measures and samples correlation, samples covariance association measures.
- Define normal probability distribution, standard normal probability distribution and Student’s t probability distribution for several degrees of freedom alternatives.
- Evaluate probability distribution goodness of fit through Kolmogorov-Smirnov and Anderson Darling tests.
- Approximate population mean, population proportion and bootstrap population mean point estimations.
- Estimate population mean, population proportion and bootstrap population mean confidence intervals assuming known or unknown population variance.
- Calculate population mean sample size assuming known or unknown population variance for specific margin of error.
- Approximate population mean two tails, right tail and population proportion left tail statistical inference tests probability values.
- Estimate paired populations means two tails statistical inference test probability value.
- Assess population mean two tails statistical inference test power for several levels of statistical significance or confidence alternatives.

## Requirements

- Python programming language is required. Downloading instructions included.
- Python Distribution (PD) and Integrated Development Environment (IDE) are recommended. Downloading instructions included.
- Practical example data and Python code files provided with the course.
- Prior basic Python programming language knowledge is useful but not required.

## Description

Learn business statistics through a practical course with Python programming language using S&P 500® Index ETF prices historical data. It explores main concepts from basic to expert level which can help you achieve better grades, develop your academic career, apply your knowledge at work or do your business statistics research. All of this while exploring the wisdom of best academics and practitioners in the field.

**Become a Business Statistics Expert in this Practical Course with Python**

Read S&P 500® Index ETF prices data and perform business statistics operations by installing related packages and running code on Python PyCharm IDE.

Chart absolute frequency, relative frequency, cumulative absolute frequency and cumulative relative frequency histograms.

Approximate sample mean, sample median central tendency measures and sample standard deviation, sample variance, sample mean absolute deviation dispersion measures.

Estimate sample skewness, sample kurtosis frequency distribution shape measures and samples correlation, samples covariance association measures.

Define normal probability distribution, standard normal probability distribution and Student’s t probability distribution for several degrees of freedom alternatives.

Evaluate probability distribution goodness of fit through Kolmogorov-Smirnov and Anderson Darling tests.

Approximate population mean, population proportion and bootstrap population mean point estimations.

Estimate population mean, population proportion and bootstrap population mean confidence intervals assuming known or unknown population variance.

Calculate population mean sample size assuming known or unknown population variance for specific margin of error.

Approximate population mean two tails, right tail and population proportion left tail statistical inference tests probability values.

Estimate paired populations means two tails statistical inference test probability value.

Assess population mean two tails statistical inference test power for several levels of statistical significance or confidence alternatives.

**Become a Business Statistics Expert and Put Your Knowledge in Practice**

Learning business statistics is indispensable for data science applications in areas such as consumer analytics, finance, banking, health care, e-commerce or social media. It is also essential for academic careers in applied statistics or quantitative finance. And it is necessary for business statistics research.

But as learning curve can become steep as complexity grows, this course helps by leading you step by step using S&P 500® Index ETF prices historical data for business statistics analysis to achieve greater effectiveness.

**Content and Overview**

This practical course contains 38 lectures and 5 hours of content. It’s designed for all business statistics knowledge levels and a basic understanding of Python programming language is useful but not required.

At first, you’ll learn how to read S&P 500® Index ETF prices historical data to perform business statistics operations by installing related packages and running code on Python PyCharm IDE.

Then, you’ll define descriptive statistics. Next, you’ll define quantitative data, data population and data sample. After that, you’ll define absolute frequency distribution and relative frequency distribution or empirical probability. For frequency distributions, you’ll do frequency, density, cumulative frequency and cumulative density histograms. Later, you’ll define central tendency measures. For central tendency measures, you’ll estimate sample mean and sample median. Then, you’ll define dispersion measures. For dispersion measures, you’ll estimate sample standard deviation, sample variance and sample mean absolute deviation or sample average deviation. Next, you’ll define frequency distribution shape measures. For frequency distribution shape measures, you’ll estimate sample skewness and sample kurtosis. Then, you’ll define association measures. For association measures, you’ll estimate samples correlation and samples covariance.

Next, you’ll define probability distributions. Then, you’ll define theoretical and empirical probability distributions. After that, you’ll define continuous random variable and continuous probability distribution. Later, you’ll define normal probability distribution, standard normal probability distribution and Student’s t probability distribution for several degrees of freedom alternatives. Then, you’ll define probability distribution goodness of fit testing. For probability distribution goodness of fit testing, you’ll do Kolmogorov-Smirnov and Anderson-Darling evaluations.

After that, you’ll define parameters estimation statistical inference. Next, you’ll define theoretical and bootstrap mean probability distributions. Then, you’ll define point estimation. For point estimation, you’ll do population mean, population proportion and bootstrap population mean point estimations. After that, you’ll define confidence interval estimation. For confidence interval estimation, you’ll do population mean, population proportion and bootstrap population mean confidence intervals estimation assuming known and unknown population variance. Later, you’ll define sample size estimation. For sample size estimation, you’ll do population mean sample size estimation assuming known and unknown population variance for specific margin of error.

Later, you’ll define parameters hypothesis testing statistical inference. Next, you’ll define probability value. For probability value, you’ll do population mean two tails and right tail tests. Also, for probability value, you’ll do population proportion left tail test. Additionally, for probability value, you’ll do paired populations means two tails test. Finally, you’ll define statistical power, type I error, type II error, type I error probability and type II error probability. For statistical power, you’ll do population mean two tails tests for several statistical significance or confidence levels.

## Who this course is for:

- Undergraduates or postgraduates who want to learn about business statistics using Python programming language.
- Academic researchers who wish to deepen their knowledge in applied statistics or quantitative finance.
- Business data scientists who desire to apply this knowledge in areas such as consumer analytics, finance, banking, health care, e-commerce or social media.

## Instructor

**Diego Fernandez is author of high-quality online courses and ebooks at Exfinsis for anyone who wants to become an expert in financial data analysis.**

His main areas of expertise are financial analysis and data science. Within financial analysis he has focused on computational finance, quantitative finance and trading strategies analysis. Within data science he has concentrated on machine learning, applied statistics and econometrics. For all of this he has become proficient in Microsoft Excel®, R statistical software® and Python programming language® analysis tools.

He has important online business development experience at fast-growing startups and blue-chip companies in several European countries. He has always exceeded expected professional objectives by starting with a comprehensive analysis of business environment and then efficiently executing formulated strategy.

He also achieved outstanding performance in his undergraduate and postgraduate degrees at world-class academic institutions. This outperformance allowed him to become teacher assistant for specialized subjects and constant student leader within study groups.

His motivation is a lifelong passion for financial data analysis which he intends to transmit in all of the courses.