This Guided Project, Data Analysis in Python: Using Numpy for Analysis, is designed for Intermediate Python learners looking to enhance their data analysis skills. Throughout this 1-hour course, you will delve into transforming 1 and 2-dimensional data in Python Lists and Dictionaries into Numpy Arrays, leveraging real-world data of the Lakers starting players to calculate their BMIs and player efficiency rates.
Key learnings include applying reshaping, joining, and splitting operations to Numpy arrays, as well as utilizing aggregate functions mean, mod, average, product, median, standard deviation, and variance to Numpy arrays. Additionally, you will work through importing necessary Python libraries and data, performing basic arithmetic operations on Numpy arrays, and conducting Numpy aggregation. This hands-on project culminates in a capstone project that applies the acquired skills to real-world data of the top 10 highest-paid NBA players to calculate their BMIs and player efficiencies.
Upon completion, you will have the ability to manipulate and analyze data effectively using Numpy, providing you with a valuable skill set in the field of data analysis.
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