SciPy is a free and open-source Python library. It is specifically used for scientific and technical computation. It has different modules for optimization, linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers, and other actions that are common in science and engineering.

SciPy is a group of conferences for users and developers of tools like SciPy (which is used in the United States), EuroSciPy (in Europe), and SciPy.in (in India). Enthought introduced the SciPy conference in the United States and sponsors many international conferences, and also hosts the SciPy website. It is now sponsored by an open community of developers. SciPy is supported by NumFOCUS, a community foundation that provides support to reproducible and accessible science.

The SciPy package makes the core of Python’s scientific computing capabilities. The core components include:

• Cluster: Hierarchical clustering, vector quantization, K-means.

• Constants: Physical constants, conversion factors. 

• Fft: Discrete Fourier Transform algorithm. 

• Fftpack: Interface for Discrete Fourier Transform.

• Integrate: Numerical integration routines.

• IO: data input and output.

• Linalg: Linear algebra routine. 

• Misc: Miscellaneous utilities like sample images. 

• Ndimage: Different functions for multi-dimensional image processing. 

• ODR: Orthogonal distance regression classes and algorithms. 

• Optimize: Algorithms including linear programming. 

• Signal: Signal processing tools

• Sparse: Sparse matrices and related algorithms.

• Spacial: Algorithms like spatial structures like K-D trees, nearest neighbors, convex hulls, etc.

• Special: Special functions

• Stats: Statistical functions

• Weave: Tools to program in C/C++ as Python multiline strings; it is now deprecated for Cython. 

Data Structures:

A multi-dimensional array is the fundamental data structure used in SciPy. It is provided by the NumPy module. It offers a few functions for linear algebra, Fourier transform, and random number generation. However, it is not the same with the generality of equivalent functions in SciPy. NumPy is additionally used as an efficient multi-dimensional container of the data with arbitrary data types. This way, it allows NumPy to boundlessly and speedily integrate with a wide variety of databases. Older versions of SciPy used Numeric as an array type. This is now deprecated in favor of the newer NumPy array code. 

The SciPy course offered by Great Learning will take you through a specific Python library that helps the developers to work with scientific and technical problems or applications. You will also be able to analyze the different modules offered by SciPy and know the difference between NumPy and the subject. At the end of the SciPy tutorial, you will be able to work in fledge with the SciPy library. The course is designed to help both working professionals and students work with Python and its projects. You will secure a certificate after the successful completion of the program. Happy Learning!