Python for Data Sciences

What is Python?

Python is a general-purpose high-level language.  It was launched in late 1980 by Guido van Rossum. The second version of the language was released in 2016 with many advanced features. Python is one of the most popular programming languages and is used in Machine Learning, Artificial Intelligence, Web Development, and Android Development.

With the time being certain other fields like the construction industry, business industry, and data sciences have also adopted the use of Artificial Intelligence and python. One of the main reasons for python to be popular is its higher productivity as compared to other programming languages like Java and C++. The other important reason is its English-like commands which make the programming a lot easier and efficient.

Introduction to Data Sciences

The field of data science applies advanced analytical techniques to obtain valuable information from data. The obtained information is used for business decision-making. The benefits of data sciences include improvement of marketing and sales programs, enhance operational efficiency and identification of new business opportunities. For a business to thrive, data sciences play a key role.

Data sciences integrate various disciplines like machine learning, data mining, software programming, mathematics, statistics, data preparation, and data engineering. Data sciences play an important role in all aspects of a business. Data sciences optimize the management of the supply chain, identify frauds in transactions, increases operational efficiency, and manage financial risks. It allows a business organization to stay strategically ahead of its rivals. So, the importance of data sciences for a business can’t be compromised.

Python for Data Sciences

Python is an interpreted and high-level language which has an outstanding approach for object-oriented programming. It is the most popular programming language amongst data scientists for many projects and applications of data sciences. The remarkable functionality of python helps its users to deal with multiple subjects such as statistics and mathematics.

You may ask yourself that why python is widely used in the field of data sciences? The main reason is the simplicity of its syntax and ease of its use. It makes it convenient for people without an engineering background to use it and they can adapt quickly. It is well suited for fast prototyping.

The modern era is considered to be the era of Artificial Intelligence and deep learning. The introduction of deep learning frameworks with python APIs has greatly enhanced the versatility and productivity of the language. Deep learning frameworks have gone through a lot of evolution and they are continuously upgrading so they are always compatible with the modern requirements.

Key features of python

Here is the list of all the features of python which make it an attractive package for the users.

• Due to its brief syntax, programs are easy to read
• The simplicity of its syntax makes programming easy
• The interactive mode of python makes code testing easy
• It has a large standard library
• You can extend the codes in pythons quite easily by appending the new modules
• The program developers can run the code anywhere like Windows, MAC, Linux, etc.
• It has a user-friendly interface
• Its compiler software is free in a couple of categories

Common libraries of python used for Data Sciences

1. Numpy

Numpy stands for Numerical python. It is a python library that provides mathematical functions. The prime purpose is to handle large dimension arrays. It provides various functions of arrays, matrices, and linear algebra. It has many useful operations for arrays and matrices. This library also provides the vectorization of numerical operations which speeds up the execution. Numpy makes it easy to work with large multi-dimensional arrays and matrices.

2. Matplotlib

Visualization of data is crucial for data organization. Matplotlib provides various ways of visualization of data. It can visualize data more virtually. You can draw graphs, histograms, pie charts, and other statistical figures using this library. The features of Matplotlib include zooming and planning of graphs. You can also save the graphs in graphics format.

3. Pandas

It is the most common python library manipulation and analysis of data. For manipulation of a large amount of structured data, Panda provides very useful functions. The library also provides the easiest functions for the analysis of data. Panda is the perfect platform for data wrangling. Panda is designed for brisk manipulation, analysis, and visualization of data. It has the following two data structures.

Series: for one dimensional data

Data frame: for two-dimensional data

4. Sicikit-learn

It is also known as Sklearn This python library is dedicated to machine learning. It provides numerous algorithms and functions of machine learning. It provides simple and easy to use tools for data mining and data analysis. It helps you to use machine learning algorithms to cater to real-world problems.

5. Scipy

It is another popular Python library for data sciences. It provides functionality for scientific mathematics. It contains sub-modules of integration, interpolation, ODE solvers, optimization, linear algebra, and image processing.