Understanding the Use of Python for Data Science

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Data Science and engineering have become one of the fastest ways to drive modernization in business enterprises. Get to know how Python converts data into an asset!

Data Science has become a popular subject opening a fresh array of possibilities for upcoming data engineers and scientists. Business Intelligence is a derivative of data modernization => turning raw data into an asset, thus increasing its utility in 2020 and beyond. It is the fastest way to achieve Artificial Intelligence-driven modernization to source all the data that matters to the business and deliver enterprise-wide intelligence. It transforms data into an asset.

 

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Although programming packages that make it easier to make machine learning models are available, it’s still pretty important to understand a lot of computer science that underlines this scenario. It might as well require quite a bit of training, especially for folks who don’t have any experience in computational thinking.

Concepts that go in favor of the current context: Data Modernization => Modernization Method and Modernization Platform:

Data Modernization allows leaders to leverage data as an asset to the enterprise and helps drive revenue, growth, contain costs and strengthens corporate valuations by:

  • Complementing businesses with all data types (by removing data silos, enforcing data quality and integrating various data types)
  • Creating and instrumenting actionable insights (empowering teams to leverage data at all times)
  • Driving performance by embracing agility, driving competitiveness (resulting in reduced time to market, supporting the creation of new products and services, streamlining operations and improving customer service)

From applications in smartphones (voice assistants: Apple’s Siri, Google Assistant, Amazon’s Alexa, Google Duplex, Microsoft’s Cortana and Samsung’s Bixby; app store and play store recommendations; face unlock), transportation optimization (dynamic pricing), popular web services (e-mail filtering, Google Search, Google Translate, LinkedIn and Facebook Recommendations) Sales and marketing (Recommendation Engines, personalized marketing, chat support queries/chatbots), security (video surveillance, cybersecurity captchas, speech-to-text model)), financial domain and other popular use cases – machines can be used to automate just about any of the everyday tasks.

  • Setting an alarm
  • Booking a cab
  • Estimating the ride fare
  • Asking Google Maps to tell the estimated time it might take to reach, machines can power just about any activity in the coming years.

How is Python Used for Data Science?

Data Science is primarily used to convert meaningful data into marketing and business strategies that help a company grow. It has spanned across various business sectors including e-commerce, health-care, finance, education, shipping, logistics, etc. Tools that make it even better: Hadoop, R programming, SAS, SQL, etc.

Besides these, one of the most popular tools in Big Data Real-Time Analytics is Python. It supports structured programming, object-oriented programming, and functional programming approaches.

Why is Python the best fit for Data Science?

  • It is powerful and easy to use.
  • It is an industry leader and is being widely used in various fields like oil and gas, signal processing, finance, insurance, banking, etc.
  • It can be efficiently used both for quantitative and analytical computing.
  • Python has been used to strengthen Google’s internal architecture; apps like Youtube have been created by the help of Python.
  • It is flexible and open-source.
  • It has massive libraries (Scikit Learn, TensorFlow, Seaborn, Pytorch, Matplotlib) which can be used for data manipulation, are easy to learn for naïve developers.
  • It is capable of integrating existing infrastructures that can resolve the most complex problems.
  • It minimizes the time spend on debugging codes and on minimizing various software engineering constraints.
  • Less time is required to implement code as compared to other programming languages like C, Java, C#, etc.
  • Python is scalable and faster compared to other programming languages like Java or Kotlin etc.
  • Python comes with various visualization options; including libraries like Matplotlib, ggplot, pandas plotting, PyTorch, NumPy, SciPy, Pandas, IPython; all these packages and libraries help create charts, web-ready plots, graphical layouts with very less effort.

Python Development Utility in Every Stage of Data Science and Analysis

  • Understanding the types of form to be taken by data – this involves deriving insights by performing functions.
  • Fetching the necessary data with the help of Python libraries like Scrapy and BeautifulSoup etc.
  • Get the graphical representation and visualization of the data with the help of Python libraries like Seaborn and Matplotlib etc.
  • The next stage is to learn the actual usage of highly complex computational techniques by making use of tools like probability, calculus and matrix functions of over lakhs columns and rows. This can be eased out by the help of the Scikit Python library.

All these stages are applicable only to text data. If such data comes in the form of images, then some different best python frameworks and libraries can be used for image processing. Konstant has been engaged in delivering apps with faster delivery times, at a reduced total cost of data. We can deploy at earliest (even within a month’s timeline) realizing the value within six months. We empower real-time, enterprise-wide analytics and intelligence web application development services to re-invent, re-engineer and augment data platforms of your choice.  Get a word from our developers to network, learn and improve!

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About Author
Neeti Kotia

Neeti Kotia

Neeti got her master's degree in software engineering in 2009 and has been working since for software companies of all sizes as a technical writer. What started as a high school passion has now been converted into a serious profession. She has a special knack of learning from all verticals and imbibing the extracts into her writing. She enjoys learning technical aspects of writing from her tasks where her experience and understanding are most impactful.

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