Yearly Archives: 2018

Tests of statistical hypotheses in Data science with Python 3
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Tests of statistical hypotheses in Data science with Python 3

Testing statistical hypotheses are an integral part of Data Science.  In my new course, I used real-world data sets to test parametric and nonparametric hypotheses using Python 3. The course has several strengths that should not be ignored. It is hands-on, uses real-world data and focuses on testing statistical hypotheses using Python 3. It is taught […]

Data Visualization and Descriptive Statistics with Python 3
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Data Visualization and Descriptive Statistics with Python 3

For those of you interested in learning how to leverage the power of Python Seaborn and Matplotlib libraries to build high-quality professional visualizations that answer many relevant Data Science questions, my new course entitled Data Visualization and Descriptive Statistics with Python 3  is the solution.  The course uses a set of real-world data sources from the […]

BUG report in computing the median, median_high and median_low within the statistics library in Python 3
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BUG report in computing the median, median_high and median_low within the statistics library in Python 3

In this blog I discuss a bug that I found in Python 3.6.4 statistics module version 3.4 concerning the computation of the median, median_high and median_low function when missing values (NaN, nan) are present in the data. I reported this  bug to the Python team and had investigated it with different data, with all results […]

Distribution of the World Cup soccer average attendance since 1970
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Distribution of the World Cup soccer average attendance since 1970

In this analysis, we are looking at the World Cup average game attendance distribution.  We decided to restrict the data from 1970 onward to give an overall distribution of relatively current attendance data. The analysis proceeds with basic data manipulation in pandas. After the basic data manipulation, we use Seaborn to create the distribution.  From […]

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