Sun. May 26th, 2024
Data Science

The overwhelming demand for trained data analysts, trainers, and scientists drives the trends related to jobs, compensation, and web development skills. For most data science enthusiasts, it is a fairly exciting journey when it begins. In less than 30 years, Python and other programming languages have coiled themselves around every relatable data science project you will come across. In the major courses on data science training in Noida, Python training is what every second professional seeks to master in. Reason; Python for data science unpacks troves of opportunities for trained professionals who are looking to take up exciting job roles in the industry. We have heard so much about the role of Python data science applications in Artificial Intelligence, Machine Learning, Business Analysis, and Big Data Development. But, there are other dimensions as well where Python is aptly involved. One of them is digital innovations or transformations built on the pillars of digital solutions.

If you are curious to know what role data science training in Noida plays in getting jobs in digital companies, here’s a quick overview. In this article, we have highlighted a few major applications of data science and Python as applied to “digital”. Let’s explore.

What do we really mean by ‘digital’?

Digital could mean all the electronic ‘avatars’ of the physical world. In the new era of industrial automation and digital transformations, digital is popularly associated with and understood as a ‘digital twin.’ So, it is more important to approach digital as part of a digital twin, and as such applied in every essence of data science and business intelligence.

What is a digital twin?

In the industrial context, a digital twin is defined as the “virtual”, mostly electronic version of the physical objects and concepts that can be simulated using automation, machine learning, and AI technologies with an ability to be retained and recorded for future use even when it means there is no distortion or limitation to access it is put forth. Some of the best examples of digital twins are:

  • Digital Signature Mobile applications
  • Video games 
  • Augmented Reality / virtual reality/ mixed reality
  • Holographic images 
  • Digital workspace
  • Marketing chatbots and virtual assistants
  • Voice assistants
  • Automated driverless vehicles
  • Internet of Things, and so on

All these digital twin items find their origin, evolution, and futuristic applications in the way data are collected, analyzed, stored, and refined for improving the effectiveness of their physical counterparts. 

Why data science for a digital twin?

There are different ways data science is applied to digital twin ecosystems. One of the best examples is cloud computing for IT management and security architecture. Without data, it is impossible to create an exact replica of the physical products in real-time, and still hope to measure the effectiveness of the digital twins as it appears in real-time using big data analysis, AI, and automation trackers. Digital twins technologies transcend the barriers commonly linked to the simulation of specific products and processes. For example, by using a digital twin for a physical human editor such as a WordPress or an e-commerce management platform, companies can develop cutting-edge content and publish content in real-time without necessarily involving a human. The ability to publish content, discover and share newer formats is what can be considered the foundation of a digital twin avatar, as it is applied to various industries. In the automotive industry, the content could be driver’s instructions to the software embedded at the heart of the driver experience management. In a marketing analytics platform, it would be the dashboards that send content in the form of intuitive graphs, bars, and pie charts, along with the predictive intelligence powered by AI.

In our interaction with the baking interfaces, the mobile app or Net banking/internet banking would be the digital twin, which runs on trillions of data points collected at the first-party level through KYC forms, digital surveys, and online chats, and email communication. In the modern banking world, this is called the gateway to the future of “smart banking” interfaces that would run irrespective of your level of interaction with a physical banking branch or customer care executive.  

Similarly, in healthcare, we are witnessing the rise of personalized mobile apps and digital diagnostics for patients who can barely visit the hospital or a healthcare center. Thanks to the hard work of talented data scientists and the data, we just no longer require to visit the physical stores to fulfill our needs, demands, and wishes.

By Manali