Data Science with Python


Overview of Data Science
Data science is all about finding and exploring the data that later help the companies or business to make decisions for their better development. Let’s discuss some of its examples.

The first example is ‘Customer Prediction’ where systems are skilled as per the behavioral pattern of the customer in order to make predictions about the possibilities of customers buying particular products. Let’s take one more example ‘Service Planning’; restaurants are able to make predictions of those customers who’ll probably visit on weekends and then they will plan their catalog accordingly in order to handle the demand.

Why Python is required?
Not particularly in data science but Python is one of those popular programming languages in IOT& AI. Day by day Python is getting famous and it is used in data science because of the availability of some of the well-off tools from the perspective of statistics and mathematics.

One of the major reasons for using Python for data science is that it is an open-source programming language & it is a language that can be understood with ease and in addition to this it has the number of useful libraries.

Factors of Data Science
There are three main factors of data science which are followed as; Speed, Packages and Design goal.

The design goal is the primary syntax rule in Python which is inborn and is easy to understand which results in building the applications with those codes that are easy to read whereas there is the number of packages build by people that can be reused and lastly Python is the fastest programming language.

Python libraries for Data Analysis
Since we already know that Python is an easy language to learn that can perform the number of tasks such as adding, printing, statement and so on. In the case of performing a data analysis Python libraries are needed to be imported and there is a number of python libraries such as Pandas, NumPy, SciPy, Matplotlib and SciKit learn. Some of the additional libraries of Python are TensorFlow, BeautifulSoup, OS, and Networks & I Graph.

• PANDAS
It is one of the most useful libraries in Python which is used for structured data operations and manipulations. Pandas is widely used for the preparation and mugging of the data.

• SCIPY
This library is built on top of the Numpy with full-fledged featured versions of the modules of linear algebra and presently it supports integration, special functions, gradient optimizations and so on.

Python is a great programming language used for data science.

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