This course covers measures of central tendency and dispersion needed to identify key insights in data. It also covers: correlation, covariance, skewness, kurtosis, and implementations in Python libraries such as Pandas, SciPy, and StatsModels.
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.
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.
