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.
A problem solver at heart, 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.
Course Overview [Autogenerated] Hi. My name is generally Ravi and welcome to the scores on interpreting data, using descriptive statistics with bite on a little about myself. I have a master's degree in electrical engineering from Stanford and have worked at companies such as Microsoft, Google and Flip Card at Google. I was one of the first engineers working on real time collaborative editing in Google Dogs, and I hold four patterns for its underlying technology's. I currently work on my own startup lunatic on a studio for high quality video content. The tools off machine learning algorithms, solution techniques and even neural network architectures are becoming commoditized. Everyone is using the same tools these days. So you are. EJ needs to come from. How value adapt those tools to your data today. More than ever, it's important that you really know your data. In this course, you will gain the ability to identify important statistical properties off your data set and understand their implications. First, you will learn how important measures off century tendency. The arithmetic mean the mold on the median. Each summarizes our leader in different ways. Next, you will discover how measures of dispersion such a standard deviation provide clues about variation in a single variable. You will learn how your data is distributed using SK, Eunice and CART horses, and you will then understand by various measures of dispersion and go movement such as correlation and COO of aliens. Finally, who drowned out your knowledge by implementing these measures? Using different libraries available in fight on, suggest bandas side by and starts models. When you finished with this course, you will have the skills and knowledge to summarize key statistically properties of your data set using fight on.