From the course: Python Data Science Mistakes to Avoid
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Skimming data - Python Tutorial
From the course: Python Data Science Mistakes to Avoid
Skimming data
- [Instructor] Another common mistake the data scientist should avoid is skimming data. When you skim over your data and quickly move on, you may overlook inconsistencies in your data and fail to notice important aspects of your data as well. For example, let's say that I have information about students' grades on a particular exam and the data is stored in a Pandas DataFrame. I've displayed the first few rows here. Now say I want to build a model that predicts students' grades on future exams and this data set will be one of the data sets that will be used to build the model. If I proceed to write machine learning algorithm right away I will miss out on making key observations about this data and the algorithms and models I implement may likely be off. So I'll start by taking a closer look at my data. One part of that process can include visualizing the distribution of the grade values. To do that, I'll create a…
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