Exploratory Data Analysis with Python
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Discover the power of Python for data analysis in this guided project. Learn how to import, visualize, and analyze data using Python’s libraries. Gain insights into feature patterns, distribution, and relationships. Explore grouping, correlation, and causation concepts. Unlock the potential of ANOVA for statistical comparisons.
At a Glance
Python is a popular programming language for data analysis due to its numerous libraries. In this guided project, you will use Python to perform data analysis and determine which variables are important to feed prediction models.
Data analysis involves a number of steps. With Python, you can import data, visualize data, and perform descriptive statistical analysis. You can also group data and perform Analysis of Variance (ANOVA) tests.
You’ll begin this guided project by importing data from multiple sources using Python. You’ll then analyze individual feature patterns using visualization techniques to get a better understanding of the data. Next, you’ll perform descriptive statistical analysis to summarize the data and gain insights into its distribution and relationships. You’ll learn the basics of grouping data to compare and analyze subsets of data. You will also study the concepts of correlation and causation to understand the relationship between different features of the data. Finally, you will learn about ANOVA, a statistical method to determine the difference between two or more means.
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