Introduction to Python in Power BI
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Learn how to use Python scripts in Power BI for data prep, visualizations, and calculating correlation coefficients.
Course Description
In this introduction to Python in Power BI course, you’ll use data from an overfishing study and an online retailer to learn how to use Python scripts in Power BI for data prep, visualizations, and calculating correlation coefficients. Specifically for building custom Python-based visuals, you will be utilizing the Seaborn package. By the end, you should feel a little more comfortable using Python in (and outside of) Power BI.
What You’ll Learn
Getting Started with Python in Power BI
In this first chapter, you will learn the advantages and limitations of Python in Power BI as well as how to enable this capability within a workbook. You will also perform the same task using both technologies separately to build familiarity with the strengths and weaknesses of both. Power BI is a powerful tool. Python can be leveraged to make it even more powerful!
Visualizations with Seaborn in Power BI
In this chapter, you will construct several Python-based visualizations, using the Seaborn package, in Power BI. Specifically, a line plot, pair plot, and joint plot. You will also learn how to interpret these visualizations to extract insights about the data. By this point, you will know some of the key differences between Python and Power BI in basic data processing steps. The next step is to visualize this data!
Missing Data and Imputation
Now that you’re up and running with Python in Power BI, let’s move on to another important data processing step – identifying missing data and imputation. In this chapter, you will identify missing data in a dataset using Python, then Power BI. You will then work through addressing missing data by leveraging imputation techniques.
Heatmaps and Correlation Coefficients
In this chapter, you will continue evaluating the relationship between variables. This time, you will be doing so quantitatively by calculating the correlation coefficient. You will learn how to do this in Power BI then Python. Finally, you will leverage the power of Seaborn visualizations to create a correlation heatmap! By the time you finish the course, you’ll be skilled in Power BI, Python, and data visualization techniques. Nice work!
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