Top Python and R Libraries to Data Science
Data science is a fascinating, promising field that is constantly evolving. The world is moving into the new world of big-data, and the need for better data storage has become a major concern.
If you are looking to begin a career in data science, this is the time. You might be wondering where to start with Python or R. These languages are rapidly changing the technology sector and could even replace existing programming languages. It is a great combination to know Python and R. Even if you don’t have any technical bike experience, you can still learn Python or R in many simple ways.
Why choose Python?
Python is a powerful and simple programming language. Basic knowledge is sufficient to allow researchers and aspirants to use Python and get started on any platform.
Python is a good choice for libraries; it has a large collection of Machin Learning libraries and Artificial Intelligence libraries.
It is highly scalable and can be used faster than any other language.
It offers amazing visualization and graphical tools that will allow you to analyze your data effectively.
Top Python Libraries for Data Science
The single most important reason Python is so popular in the field Data Science, Machine Learning and Artificial Intelligence, is that it has thousands of built-in libraries that provide functions and methods to efficiently perform data analysis, processing, wrangling and modeling. Here are the top Python libraries that support Data Science.
1. NumPyNumPy can also be known as Numerical Python. It is one the most basic Python libraries to perform statistics. Here are the NumPy features:
Multi-dimensional arrays are a key feature of NumPy. They can be used for any type of logical operation, as well as mathematical operations.
NumPy functions can be used to index, sort and pre-shape sound waves and images in a multi-dimensional array.
It allows you to perform simple to complicated mathematical and scientific computations.
It supports multi-dimensional array objects as well as a variety of functions and methods for processing these array elements.
2. SciPyNumPy provides the foundation for SciPy. This library contains sub-packages that can be used to solve the most basic problems in statistical analysis. This library is used for processing the NumPy-defined array of elements. It is also used to solve mathematical equations that NumPy cannot do.
It works with the NumPy arrays.
It offers a platform that allows for numerous methods of numerical integration and optimization.
It also includes sub-packages to perform Fourier Transformations and Vector Quantization.
It also includes a full-fledged set of linear algebra functions that can be used to solve the most difficult competitions, such as clustering and Kimi algorithms.
3. PandasThis is the most important statistical library used in many fields including finance, statistics, and data analysis. Pandas, like SciPy depends on the NumPy array to process Pandas and other data objects.
Pandas is a useful library for handling large amounts of data.
It creates data frame objects quickly and effectively with pre-defined syntaxes.
It can be used for sub-sitting and data slicing.
It also has built-in features that allow you to create excel charts and perform any type of complex data analysis task like statistical analysis, data wrangling and transformation, manipulation, visualization and so forth.
4. Matplotlib Matplotlib is one of the most popular data visualization libraries. It can be used to create a variety of crafts such as plots, histograms and bar charts. It is a 2D graphics library.