Mining Data to Extract and Visualize Insights in Python
Learn how to clean and extract useful information from your dataset in Python
Learn how to create several different types of visualizations to identify patterns, outliers, and correlations in your dataset
Learn how to visualize a highly dimensional dataset using principal component analysis (PCA)
By the end of this project, you will learn how to load and extract useful information from your dataset using Python, a free, open-source program that you can download. You will then learn how to clean your data set by removing unwanted whitespaces, columns containing several empty values, rows containing empty column values and duplicated row entries. Next, you will create various visualizations to identify patterns and outliers in your dataset, and visualize correlations between different columns. Lastly, you will learn how to visualize a highly dimensional dataset using principal component analysis (PCA). These steps are part of exploratory data analysis that you will need to carry out for any data science project to help you understand your dataset. Note: This course works best for learners who are based in the North America region. We’re currently working on providing the same experience in other regions.
Skills you will develop
Learn step-by-step
In a video that plays in a split-screen with your work area, your instructor will walk you through these steps:
Load a dataset and extract basic information using Python
Learn various ways to clean your dataset
Visualize patterns and outliers that may be present in your dataset
Calculate and visualize the correlation between different numeric columns
Cluster your dataset to identify similar groups
Visualize your dataset using principal component analysis (PCA)
How Guided Projects work
Your workspace is a cloud desktop right in your browser, no download required
In a split-screen video, your instructor guides you step-by-step
Reviews
TOP REVIEWS FROM MINING DATA TO EXTRACT AND VISUALIZE INSIGHTS IN PYTHON
it was a nice experience to learn again the some of the python analysis
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