Accelerate Complicated Statistical Test for Data Scientist with Pingouin. Quick and easy important statistical test in one package
As a Data Scientist, our work consists of creating a Machine Learning model and building** our assumption regarding the data**.
Knowing how our data related and any differences between the target statistic could make a big difference in our data analysis and model creation.
I really encourage you to learn basic statistics and hypothesis testing if you want to edge in the data science field.
Regardless of your knowledge in statistic testing, I want to introduce an interesting open-source statistical package that I know would be useful in your daily data science work (because it helps me)—this package is called Pingoiun.
Let’s see what this package offer is and how it could contribute to our work.
According to the Pingouin homepage, this package is designed for users who want simple yet exhaustive stats functions.
It was designed like that because some function, just like the t-test from the SciPy, returns only the T-value and the p-value when sometimes we want more explanation regarding the data.
In the Pingouin package, the calculation is taken a few steps above. For example, instead of returning only the T-value and p-value, the t-test from Pingouin also return the degrees of freedom, the effect size (Cohen’s d), the 95% confidence intervals of the difference in means, the statistical power, and the Bayes Factor (BF10) of the test.
Let’s try the package with a real dataset. For starter, let’s install the Pingouin package.
#Installing via pip pip install pingouin #or using conda conda install -c conda-forge pingouin
Now, let’s say I have the car mpg dataset from various places (available free from the Seaborn package).
Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments. Our latest survey report suggests that as the overall Data Science and Analytics market evolves to adapt to the constantly changing economic and business environments, data scientists and AI practitioners should be aware of the skills and tools that the broader community is working on. A good grip in these skills will further help data science enthusiasts to get the best jobs that various industries in their data science functions are offering.
Statistics for Data Science and Machine Learning Engineer. I’ll try to teach you just enough to be dangerous, and pique your interest just enough that you’ll go off and learn more.
The agenda of the talk included an introduction to 3D data, its applications and case studies, 3D data alignment and more.
Become a data analysis expert using the R programming language in this [data science](https://360digitmg.com/usa/data-science-using-python-and-r-programming-in-dallas "data science") certification training in Dallas, TX. You will master data...
Learn the most important pillar of data science. Everybody and their mother wants to learn data science. The field is quite interesting — I have to admit .