Another common mistake that data scientists should avoid is skimming data. In this video, learn how to avoid this mistake by exploring your data first before writing algorithms and building models.
- [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 histogram. … As you can see, the first bin, …
Skill Level Intermediate
1. Avoid Mistakes in Coding Practices
2. Avoid Mistakes in Structuring Code
3. Avoid Mistakes in Handling Data
4. Avoid Mistakes in Machine Learning
Using redundant features1m 45s
Get started with Python1m 7s
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