Basic Statistics in Python (Correlations and T-tests)

Overview

Class Central Tips

By the end of this project, you will learn how to use Python for basic statistics (including t-tests and correlations). We will learn all the important steps of analysis, including loading, sorting and cleaning data. In this course, we will use exploratory data analysis to understand our data and plot boxplots to visualize the data. Boxplots also allow us to investigate any outliers in our datasets. We will then learn how to examine relationships between the different data using correlations and scatter plots. Finally, we will compare data using t-tests. Throughout this course we will analyse a dataset on Science and Technology from World Bank. The measures in this dataset are numeric, therefore you will learn how to handle and compare numeric data. This guided project is for anyone with an interest in performing statistical analysis using Python. This could be someone from a social science background with statistics knowledge who wants to advance their analysis, or anyone interested in analysing data.

Syllabus

Project Overview

By the end of this project, you will learn how to use Python for basic statistics (including correlations and t-tests). You will learn about the important steps of data sourcing and cleaning. As well as exploratory data analysis and visulization (including boxplots and scatter plots). This guided project is for anyone with an interest in performing statistical analysis using Python. This could be someone from a social science background with statistics knowledge. No prior Python or programming experience is needed, but an understanding of the statistical methods we will cover and a Google Account are. We will examine a dataset from World Bank full of Science and Technology indicators from multiple countries in 2009 and 2018. The indicators in this data are all numeric, eg number of patents and trademarks registered. This course will teach you how to deal with missing values, explore outliers in the data, produce descriptive statistics, examine relationships using correlations, perform t-tests and produce charts to visulize your results.