What is Data Science? Why is it in Demand?

This is an exciting time to work as a data scientist. According to a LinkedIn report from August 2018, the requirement for Data science is “off the charts.” According to the U.S. Bureau of Labor Statistics (BLS), the demand for data scientists will increase at a rate that is more than three times faster than the national average. Data scientist jobs were ranked as best in the US in 2019 by the employment website Glassdoor, making it the profession’s fourth straight year at the top of the list. Data scientists are in demand across all sectors of the economy, and this trend isn’t slowing down any time soon.

It takes time to become a data scientist. Data scientists are experts in computer science, including operating systems and programming languages. Additionally, they are skilled at working with huge amounts of data using software and analysis. Discover what is data science, who are data scientists, why data science is in demand around the world, and how to reach this potentially well-paying, intellectually demanding field from research proposal writing service writer by reading on.

What Is a Data Science?

The development of technology, particularly programming languages, and methods for gathering, analyzing, and interpreting data have helped make data science the well-known field it is today. The blog of dissertation writing service London based writer mentioned that, In the article “The Future of Data Analysis” from 1962–1963, American mathematician John W. Tukey foresaw the emergence of a brand-new field. The first definition of data science was provided by another pioneer, computer engineer Peter Naur, in his book “Concise Survey of Computer Methods.”

In two decades

technology advanced, the amount of data collected exploded, and IBM introduced personal computers in 1981. In 1983, Apple followed suit. Computing advanced exponentially throughout the 1980s, enabling businesses to easily transform their operations digitally and gather data.

The internet became practically ubiquitous in the 1990s, enabling communication, data collection, and (of course) connectivity. In the middle of the 2000s, data started to take on more significance, and businesses started to care more about discovering patterns and improving their business decisions. In many regions of the world, there has been a sharp rise in the need for data scientists, and the field is still one of the thriving ones right now.

Who Are Data Scientists?

Data scientists are experts in data. Every second of every day, people all over the world generate enormous amounts of data, which businesses and governments collect. The tracking and storage of clicks from apps, websites, smart devices, and even clicks are all done in massive server vaults for data scientists to organize through and analyze. Since data can be used for so many different things, data scientists are all in high demand.

For example,

fashion brands can use consumer data to better manage product production and distribution, government investigators can use data to advise politicians on policy, and financial firms can use data to assess potential investment opportunities.

On the other hand,

data scientists go beyond being analysts. They are not only capable of reading and interpreting data, but also of managing, storing, and safeguarding it. They create data management software, as well as hardware and network systems, using programming languages. After all, programming is an art (Gupta, 2004). They are aware of how to identify threats by reviewing data security gaps and vulnerabilities. In order to fix problems and enhance network functionality, data scientists test systems and collaborate with software engineers. This makes it simpler for analysts and clients to use information.

Why Are Data Science Professionals in High Demand Around the World?

Here are the primary reasons:

1. Data abundance

The huge amounts of data that are currently available to organizations present a significant challenge and managing future datasets which will grow exponentially more large presents an even greater challenge.

2. Alack of talent

Finding a skilled data scientist is difficult. It’s difficult to find individuals who are skilled at comprehending and utilizing data to promote business benefits. Data scientists and analysts are in great demand, but there is only a trickle of them available to meet demand. According to a McKinsey report from 2021, the United States alone has a lack of over 190,000 data scientists professionals. Since then, demand has skyrocketed.

3. A diverse and extensive skill set is required

More than just having a basic understanding of programming or coding is necessary to succeed as a data science professional. You must be skilled in the use of tools such as Hadoop, Spark, and NoSQL. Additionally, you need to have solid training in programming, statistical modeling, and machine learning. It’s extremely difficult to find all of these skills in a single person.

4. Professionals without any background in related fields are not eligible to participate

Professionals and students with no link to engineering, computer science, mathematics/statistics, or general science are almost completely barred from entry. The multidisciplinary field of data science necessitates knowledge in both of the aforementioned areas.

5. Handsome pay

It is not only one of the highest-paying sectors in the world, but it is also the field with the highest rate of growth (Miller, 2021). That is without a doubt. The pay is simply fantastic. However, the work that goes into being a data science professional in your organization is also important.

They are aware of how to identify threats by reviewing data security gaps and vulnerabilities. In order to fix problems and enhance network functionality, data scientists test systems and collaborate with software engineers. This makes it simpler for analysts and clients to use information.

How Can You Set Yourself Up for a Successful Data Science Career?

Here’s everything you can do:

• Understand matrix factorizations.
• Develop your distributed computing concepts.
• Consider receiving training or certification in statistical analysis
• Acquire knowledge of signal processing and machine learning techniques.
• Take a machine learning professional diploma/certification course to learn advanced concepts.
• Get to know the methodologies and techniques used in information retrieval.
• Knowledge of algorithm design and data structure decoding would undoubtedly be beneficial.
• A computer science engineering course with a focus on machine learning.

Conclusion

There isn’t a better career opportunity for millennials or members of Generation Z than beginning a data science profession in the current job market. To enter this booming industry niche, do your homework, put in the time to learn new material, polish up your abilities, enroll in online courses, and earn professional certifications. Professionals in data science are like unicorns in the business world.

Reference list

Gupta, D. (2004). What is a good first programming language? XRDS: Crossroads, The ACM Magazine for Students, 10(4), 7–7. https://doi.org/10.1145/1027313.1027320

Miller, J. CM. 2021.  List of Best Data Science Research Topics (2021-2022). Online Available at <https://www.dissertationproposal.co.uk/dissertation-topics/data-science-research-topics/> [Accessed on 20th July 2022]