Is Data Science A Multidisciplinary Subject ?

 

As of today, data science has become prevalent in nearly every industry. Today, there isn't a single industry on earth in which data isn't important. Data science has thus become one of the most critical sources of energy for every sector.

 

Data Science is primarily concerned with finding patterns in data using various statistical techniques. It analyses and draws insights from data using various statistical techniques. Data Science is responsible for making predictions based on data. By analysing the data, Data Science aims to reach conclusions.

 

We had statisticians before Data Science. Companies hired these statisticians with competence in qualitative data analysis to analyse their overall performance and sales.

The fields of computer science and statistics fused with the introduction of computing processes, cloud storage, and analytical tools. Data Science was born as a result of this.

 

You might be wondering why the term "Big Data" has gained so much traction. "Big Data" is a word that describes a significant amount of raw data that your company collects, stores, and analyses. Big data may assist you in making those decisions with confidence, based on a thorough examination of your marketplace, business, and customers.

Big Data is essentially a special application of data science, which has been explored and applied in various domains such as.

• ·       Fraud and Risk Detection
• ·       Healthcare
• ·       Internet Search
• ·       Targeted Advertising
• ·       Website Recommendations
• ·       Advanced Image Recognition
• ·       Speech Recognition
• ·       Airline Route Planning
• ·       Gaming
• ·       Augmented Reality

 

Data science has exploded in popularity due to advancements in numerous tools and technologies. The ACID principles, which stand for Atomicity, Consistency, Is
Isolation, and Durability, are adhered to by many designed databases.

APPLYING DATA SCIENCE IN VARIOUS FIELDS

 

Data science is one of the era's most popular technologies, and it has deep roots in the corporate world. Data science has proven to be useful in addressing a wide range of real-world issues, and it is increasingly being used across industries to enable more intelligent and well-informed decision-making. There is a desire for intelligent devices that can learn human behaviour and work habits as the usage of computers for day-to-day business and personal activities grows. This pushes big data analytics and data science to the foreground.

 

MECHANICS

If a gadget can collect a large amount of data, use machine learning, and detect patterns and solve problems that would otherwise only be discovered after extensive testing. Sensors, transducers, and actuators with key properties that require mechanical engineering to analyse, design, implement and test thermal, motion, location, pressure, optical, chemical, and sound states are sensed and adjusted. Mechanical engineering is one of the most fascinating areas that allows AI algorithms to interface with the real environment. People who are skilled in both mechanical engineering and machine learning will be in high demand in the future.

AUTOMATION AND MANUFACTURING:

Manufacturing is being transformed by robots. Robots are increasingly being used to handle everyday activities as well as those that are difficult or risky for humans.

Every year, manufacturers invest more and more money in the roboticization of their businesses. The AI-powered robot models assist in meeting the rising demand. Furthermore, industrial robots play a significant role in improving product quality. Every year, improved models arrive on the factory floor, causing the lines to be revolutionised. They are uncomplicated. Furthermore, manufacturing robots are more inexpensive than ever before for businesses.

 

SUPPLY CHAIN MANAGEMENT:

Complex and unpredictably complex supply chains have always existed. Manufacturing operations and product delivery have always included risk. For manufacturers, using big data analytics to manage supply chain risk might be quite helpful. Companies can use analytics to identify future delays and quantify the likelihood of serious issues. Analytics is used by businesses to discover backup sources and establish contingency plans.

 

INTERNET OF THINGS:

The Internet of Things is primarily concerned with computers and devices using networks to "speak" to one another, a process that is largely based on data exchange. As a result, if data is the fuel that powers the Internet of Things, data science algorithms transform it into something useful.

 

Data science, according to Daniel Christie, Head of Engineering at James Cook University, serves a critical value generation function for IoT devices. "Data science takes data acquired through IoT devices and technology and converts it into something that can provide value to an organisation or business through analysis and visualisation." The data science component enables value to be derived and understood from the use and deployment of IoT technology."

 

Virtual assistants, such as Amazon's Alexa, are one example.

(JCU) 

 

MEDICAL:

Data science is accelerating the development of new insights on how to treat sickness and illness by improving the drug discovery process. Data scientists can aggregate data from existing and future studies to use a "big data" approach to research by building algorithms capable of absorbing massive volumes of medical data. For example, Data2Discovery, a data science firm, is collecting and analysing data from hundreds of thousands of pages of medical literature using a natural language processing algorithm.

Precision medicine, often known as personalised medicine, envisions a future in which each patient receives treatment tailored to their specific biological traits. In this still-emerging discipline, data scientists are developing methods to better understand how a patient's genetics affects their reaction to specific medicines, intending to allow physicians to provide patients with individualised treatment plans that are matched to their biology.

 

This all that gives the answer to the question “Is Data Science a Multidisciplinary Subject ?”.

So, Yes! Data Science is definitely a multidisciplinary subject as it is used in most of the fields that are trending today. And that’s exactly why companies are investing heavily into data and why so many people have chosen data as their professional career path.

There are unbelievably 1,200 petabytes of information that Google, Microsoft, Amazon store.

Facebook generated about 4 petabytes of data per day.

463 ZB of data will be created every day by 2025. (Raconteur, 2020)

Big Data is going to be worth $229.4 billion by 2025. (Strategic Tech Investor, 2021)

We've never had to deal with as much different data as we do now, and we've never had to do so rapidly. The variety and volume of data we collect through the number of techniques are increasing at an exponential rate. This expansion necessitates novel data collecting, storage, processing, analysis, and visualisation tactics and methodologies.

 

Thus, Data science is an umbrella term that encompasses all of the techniques and tools used during the life cycle stages of useful data.

 

REFERENCES:

Multidisciplinary data Science

https://www.livemint.com/Opinion/mkP9m9lVJ638smN0tT5F6J/Multidisciplinary-data-science.html

 Data Science and Big Data 

https://www.kdnuggets.com/2016/11/big-data-data-science-explained.html

How much data is created every day?

https://seedscientific.com/how-much-data-is-created-every-day

What Is Data Science Process, Steps Involved, and Their Significance?https://www.analytixlabs.co.in/blog/data-science-process/

Multidisciplinary statisticians will further healthcare big data

https://healthitanalytics.com/news/multidisciplinary-statisticians-will-further-healthcare-big-data

 

 

BLOG BY:

 

Prathamesh Deshpande

Sakshi Deshpande

Sakshi Hiremath

Gajanan Jadhav

Sahil Jadhav