Summary

You began our introduction to data analysis with NumPy, Python's incredibly fast library for handling massive matrix computations. Next, you learned about the fundamentals of pandas, Python's library for handling DataFrames. Taken together, you used NumPy and pandas to analyze the Boston Housing dataset, which included descriptive statistical methods and Matplotlib and Seaborn's graphical libraries. Along the way, you learned about fundamental statistical concepts, including the mean, standard deviation, median, quartiles, correlation, skewed data, and outliers. You also learned about advanced methods for creating clean, clearly labeled, publishable graphs.

In Chapter 11, Machine Learning, you will come across interesting machine learning concepts such as regression, different types of classifications, decision trees. You will use Python to build efficient machine learning models and predict new results.