Applied Statistics with Python

Applied Statistics with Python

This course helps prepare you for positions that require the analysis of large data sets, providing the statistics foundation you’ll need for data analysis.

You’ll learn how to model real-world applications using statistical methods. You’ll also gain skills to set up those applications in statistical frameworks, solve the statistical problems using technology and apply those results to answer real business questions. By leveraging the pandas and matplotlib python libraries, you’ll gain exposure to tools used in data analysis, visualization, and data science.

This course specifically explores inferential statistics - the science of applying statistical techniques to quantify and answer real-world data analysis questions.

The content covers three components – descriptive statistics and probability, hypothesis tests and confidence intervals and linear regression analysis.

Upon completing this course, you’ll be able to:

• Formulate real-world data analysis questions as problems in statistical frameworks
• Explain how statistical concepts are applied in authentic situations
• Represent data numerically and visually
• Calculate and apply general and normal distribution probabilities

Analyze real-world problems using hypothesis tests and confidence interval techniques

• Construct hypothesis test and confidence interval models to solve problems
• Calculate hypothesis test and confidence interval results using technology
• Interpret the results of hypothesis test and confidence interval calculations