The tools of machine learning – algorithms, solution techniques, and even neural network architectures, are becoming commoditized. Everyone is using the same tools these days, so your edge needs to come from how well you adapt those tools to your data. In this course, Interpreting Data using Descriptive Statistics with Python, you will gain the ability to identify the important statistical properties of your dataset and understand their implications. First, you will explore how important measures of central tendency, the arithmetic mean, the mode, and the median, each summarize our data in different ways. Next, you will discover how measures of dispersion such as standard deviation provide clues about variation in a single variable. Later, you will learn how your data is distributed using skewness and kurtosis and understand bivariate measures of dispersion and co-movement like correlation and covariance. Finally, you will round out your knowledge by implementing these measures using different libraries available in Python, like Pandas, SciPy, and StatsModels. When you are finished with this course, you will have the skills and knowledge to summarize key statistical properties of your dataset using Python.
Author Name: Janani Ravi
Author Description:
Janani has a Masters degree from Stanford and worked for 7+ years at Google. She was one of the original engineers on Google Docs and holds 4 patents for its real-time collaborative editing framework. After spending years working in tech in the Bay Area, New York, and Singapore at companies such as Microsoft, Google, and Flipkart, Janani finally decided to combine her love for technology with her passion for teaching. She is now the co-founder of Loonycorn, a content studio focused on providing … more