From the course: Cleaning Data for Effective Data Science: Data Ingestion, Anomaly Detection, Value Imputation, and Feature Engineering
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Visual rendering
Data is often presented with the Polars or the Pandas data frame libraries. Here we can see a little bit about the particular versions of Python, Polars, and Pandas that were used in development of this course, and we can proceed to visualize a particular dataset in this case using the Polars Library. Throughout this course, I am strongly opinionated about a number of technical questions. Take my opinion for what it's worth, but I believe there's reasons for each of the opinions I express. I do not believe it will be difficult to distinguish my opinions from the mere facts I also present. I have worked in this area for a number of years, and I hope to share with readers the conclusions I have reached. If you disagree with claims I make, I hope you'll benefit both from what you learn anew and what you were able to reformulate in strengthening your own opinions and conclusions. This course does not use mathematics and statistics heavily. There is some, but the references shown here are…
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