Statistics and linear algebra lay the foundational ground for exploratory data analysis. Both of the main statistical methodologies, descriptive and inferential, are useful in gaining insights and making inferences from raw data. For instance, we can compute that the data for a variable has a certain arithmetic mean and standard deviation. From these numbers, we can then infer a range and the expected value for this variable. Then, we can run statistical tests to check how likely it is that we reached the right conclusion.
Linear algebra concerns itself with systems of linear equations. These are easy to solve with NumPy and SciPy using the linalg package. Linear algebra is useful, for instance, to fit data to a model. We shall introduce other NumPy and SciPy packages in this chapter for random number generation and masked arrays.
In this chapter, we will cover the following topics:
Basic descriptive statistics with NumPy
Linera algebra with NumPy
Finding...