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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. Whether you were first a Pythonista or a Power BI power user, integrating Python into Power BI is a fantastic addition to the data toolbox. This course will demonstrate that, by using the two together, you can leverage the benefits of each, choosing the best one for the task at hand.
Getting Started with Python in Power BIFree
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!
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 Power BI, then Python. You will then work through addressing missing data by leveraging imputation techniques available in Python.
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 joint plot, pair plot, and ridge 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!
Correlation Coefficients and Heatmaps
In this chapter, you will continue evaluating the relationship between two variables. This time, by doing so quantitatively by calculating the correlation coefficient. You will learn how to do this in Power BI and Python, separately. 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!
Data Scientist at Microsoft
Jacob H. Marquez is an insatiable learner and lifelong builder. He is a data scientist by day, answering audacious questions to support customer experience and company goals. He is a serial hobbyist by day and night: being an educator, building a coffee recommendation app, drinking coffee, writing on Medium, and amateur cycling and muay thai. He has a bachelor's in psychology and a master's in computational analytics (2024).