Business Statistics with Python

Course details

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 Students 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. Its designed for all business statistics knowledge levels and a basic understanding of Python programming language is useful but not required.

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

Next, youll define probability distributions. Then, youll define theoretical and empirical probability distributions. After that, youll define continuous random variable and continuous probability distribution. Later, youll define normal probability distribution, standard normal probability distribution and Students t probability distribution for several degrees of freedom alternatives. Then, youll define probability distribution goodness of fit testing. For probability distribution goodness of fit testing, youll do Kolmogorov-Smirnov and Anderson-Darling evaluations.

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

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