Python
  • Introduction-Why is Python a language of choice for Data Scientists
  • top 10 programming languages
  • Python Usability
  • Python Scalability 
  • Python Libraries 
  • Python Frameworks
  • How Python Helps Data Scientists?
  • Python & its Community
  • Takeaway

 

Why is Python a language of choice for every Data Scientist?

Python has emerged as the most popular language for data analysis and is fast becoming a preferred choice among Data Scientists. 

With many frameworks to choose from, Python gives you the flexibility to perform complex tasks in simple ways. It is an easy-to-learn and versatile programming language that facilitates writing code without much fuss. 

Python offers high readability, code reusability, and a shorter learning curve than its other programming counterparts.

IEEE Spectrum has announced the top programming languages of 2020 and Python tops the list. According to the data-mining site KDnuggets, Python is currently used by 35% of all data scientists in the world.

Python has been a game-changer for Data Science. It has simplified the handling of low-level tasks and given engineers an ability to focus on their algorithms instead of spending time on infrastructure issues.

Python is very user-friendly for both beginners and experts alike. It offers simple modules that are easy to comprehend at first sight. Unlike many popular computing languages like C or Java, Python has no strict rules for the declaration of variables, classes, etc.

Here’s what programmers mean when they talk about “top ten programming languages“:

  • Python
  • Java
  • C
  • C++
  • JavaScript
  • R
  • Arduino
  • Go
  • Swift
  • Matlab

Python leads the game! Python is typically the programming language of choice in Machine Learning and Artificial Intelligence for its popularity. It has a wide variety of useful libraries that are used widely by data scientists. Python is on the rise, according to recent trends in AI and ML. 

Let’s take a look at what makes Python such a favourite among data scientists.

 

Python Usability 

One of the more common languages that you will encounter in data science is Python. One advantage to using python is that it is easy to pick up and is extremely versatile, which means you can apply your skills in many different scenarios. It’s simple, yet effective. Flexible and replete with opportunities. The versatility of Python has been demonstrated by many factors.

  • Is free and open-source language
  • Is high-level
  • Is interpreted
  • Is community-backed

 

Python Scalability 

Python ranks highest for scalability. Python is flexible and can be used for virtually any development issue, big or small.

Python Libraries 

Looking for a new application for your software development process? Python has plenty of libraries to choose from. With the ever-increasing prominence of Big Data and the development of machine learning and artificial intelligence, it is no surprise that there are many libraries focusing on analytics. Libraries can save an enormous amount of manual labour and will often replace the need for most solutions.

 

Few popular Python libraries

  • SciPy

SciPy is a Python library for numbers, vectors, matrices, and other mathematical data types. SciPy supports optimisation of linear algebra, integration, interpolation, special functions with signal and image processing.

  • Pandas

Pandas is a free, Python-based tool used for computer programming tasks like data analysis and machine learning.

  • NumPy

NumPy stands for “Numerical Python” and is a collection of modules designed to work with large, multi-dimensional arrays. NumPy also supports large, multi-dimensional arrays and matrices alongside high-level mathematical functions to operate high collections of arrays. 

  • SciKit

SciKit stands for “SciPy Toolkit” and is used in machine learning and statistical modelling. That includes classification, regression, clustering, and dimensionality reduction via a consistent interface in Python. SciKit is a simple and efficient tool for predictive data analysis. 

  • Data Visualization

Python supports a diverse set of data visualisation libraries, including Matplotlib, Seaborn, ggplot, and Bokeh.

 

Python Frameworks

Even though Python is not typically used in web development, it can still be a valuable asset. Python libraries, such as Django and Flask, make coding easier and faster. 

Let’s take a look at popular Python Full-Stack Frameworks. 

  • Django
  • Pyramid
  • TurboGears
  • Web2py
  • Bottle
  • Flask
  • Cherrypy

 

How does Python help Data Scientists?

By Collecting and Cleansing Data

Python comes with libraries like PyMySQL and BeautifulSoup that make importing data from SQL tables and scraping web pages a breeze. As an open-source library, PyMySQL allows Python programs to connect and communicate with MySQL databases. BeautifulSoup is a free HTML parser for Python and comes in handy when working with web scraped data.

By Exploring Data

Once your data is collected and cleaned, it is time to extract insights out of it. After the data is separated into designated categories, Python libraries like NumPy or Pandas help manipulate data quickly and efficiently. 

By Modelling Data

When creating a data model, one of the first decisions you’ll need to make is choosing between several Python libraries. If you want scientific computing and calculations, SciPy or SciKit are two possibilities. NumPy can be used for numerical modelling analysis. 

By Visualising and Interpreting Data

Python has many helpful packages for users to use to visualise data in a way that drives your business questions. The most popular Python data visualisation library is Matplotlib, which has a variety of configurable basic graphs and charts. Plotly is the best option to create more complicated graphics.

Python and its community

Python is well known for its large and influential community. Early support from the community can make all the difference for a learner. You can learn and watch the works of specialists in-depth; take necessary notes for comprehensive Python learning. These communities are creating a buzz for acquiring a Python background in Data Science programs.

 

Takeaway:

Python is a great language. There are many tasks that aspiring Data Scientists can complete if they know the language. This blog post only touches the surface of the vast territory of Python applications in Data Science, also known as python for data science. Python is often the brainchild of data scientists, due to its versatility, flexibility, and resourcefulness.

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