Exploratory Data Analysis in Python
When we put our hands on a dataset for the first time, people can't wait to test several models and algorithms. This is wrong because if people don't know the information before feeding our model, the results will be unreliable and the model itself will surely fail. Moreover, if we don't select the best features in advance, the training phase becomes slow and the model won't learn anything useful. So, the first approach we must have is to take a look at the dataset and visualize the information it contains. In other words, people have to explore it. That's the purpose of the Exploratory Data Analysis.
Exploratory Data Analysis is an important step in data science and machine learning. It helps people explore the information hidden inside a dataset before applying any model or algorithm. It makes heavy use of data visualization, it's bias-free. Moreover, it lets people figure out whether our features have predictive power or not, determining if the machine learning project people are working on has the chance to be successful. Without EDA, people may give the wrong data to a model without reaching any success.
With this course, you will learn:
- How to visualize information that is hidden inside the dataset
- How to visualize the correlation and the importance of the columns of a dataset
- Some useful Python libraries
This course offers:
- Flexible deadlines: Reset deadlines based on your availability.
- Get a Certificate when you complete
- 100% online
- Beginner level
- Approximately 2 hours to complete
- Subtitles: English
Participants: 4,868
Enroll here: https://www.udemy.com/course/exploratory-data-analysis-in-python/
