Scope of Data Science

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 Data science is the study of data to extract meaningful insights for business. It is a multidisciplinary approach that merges principles and practices from the fields of mathematics, artificial intelligence, statistics, and computer engineering to analyse huge amounts of data. This analysis helps data scientists to raise and answer questions such as what happened, why it happened, what will happen, and what can be done with the results.

The term Data science is not new to the world. First, it was introduced in the 1960s and it was standardised by computer professionals in the 90s. Afterward, Data Science became the process of data design, data collection, its analysis with visualization. It became popular and got a positive response in the market as it is very helpful in machine learning so helpful in prediction and analysis, specifically business analysis.

As businesses and other organizations undertake digital transformation, they’re faced with an increasing surge of data that is incredibly valuable and increasingly difficult to collect, process, and analyse.

Why is it decisive?

It’s very obvious nowadays that data is a business asset; data is increasing and being stored at an exponential rate. Almost every organization is collecting data, and of course, a flood of data is useless if we don’t manage it. It will be a waste of expensive assets. Data Science has emerged as an asset that will definitely bring a good change in present and future society.

In layman’s language, you can consider it as an analogy for the role of experience in decision making similarly, it is important for Machine learning, helps in prediction and contributes to decision-making. It will bring remarkable revolution in the following fields:

  •  Medical diagnosis.

  • Optimizing business

  • Automating business operations

  • Increasing revenue

  • Disaster management

  • Cyber management

  • Automation

  • Stock market

Challenges in Data Science

It all sounds prominent, of course, but there are some challenges faced by Data scientist are:

1. Data Preparation

2. Lack of Appropriate data

3. Incomplete dataset

4. Missing Values

5. Data Security

6. Data inaccuracy

7. Data inconsistence

8. Data cleaning

9. Efficient management of data

10.  Efficient Data handling.

Although there are many challenges, the future of data science is bright many institutes have started this course in fact, most of the Engineering and technology organization has started this subject in their course.

Futures in Data Science

Data Science is one of the fastest-growing technologies in India. Data science has captured all the field and it will remain an important asset in the coming future. As the scope of Data science has already been mentioned in many articles or blogs so I will not repeat the same…

Many students and people are confused about whether this technology will be demanding in the coming decades, and few of them hesitate to take this course. Of course, technology keeps on changing its form changes or we can say it gets extended and updated but the concept remains the same. As we can find previously there were core branches like electrical, civil, mechanical and then Computer science. It further got another form of Information Technology.

Nowadays, it got extended into IOT, Artificial Intelligence, Machine Learning, and Data Science, and many tools also came into the market, like Tableau, Power BI and many more.

So no need to worry there are many more decades where data science will affect our society in a more useful way.

Be ready to become Data Scientist!!!!

Sony Kumari

Assistant Professor

CSE Department


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