Given that data science has been named “the most promising” career by LinkedIn and the “best job in America” by Glassdoor, many in the industry are confused as to how something as promising as data science can possibly be deemed dead.
To find important data buried in an organization’s information systems, data science integrates arithmetic and statistics, specialized programming, sophisticated analytics, artificial intelligence (AI), & machine learning with unique subject matter knowledge. These insights may be used to guide decisions and strategic planning. The worldwide data science platform market was worth USD 3.93 billion in 2019 and is predicted to increase at a compound annual growth rate (CAGR) of 26.9% between 2020 and 2027. Technological advancements are developing at a rapid pace due to increased investment in research and development. As businesses expand, so does the desire for technology that may boost productivity and efficiency.
Data science platforms are becoming increasingly popular. The program gives significant flexibility to open-source tools as well as computer resource scalability. It is also simple to align with diverse data architectures. Aside from that, the platform supports version control, allowing the data science team to collaborate on projects without losing previously completed work. Such advantages contribute significantly to market expansion.
So, unless and until we discover a means to avoid using data itself, data science as a subject is unlikely to become outdated very soon. Many people feel that because a data scientist’s everyday responsibilities are quantitative or statistical in nature, they may be automated and that a data scientist will be obsolete in the future.
Specialization in your domain is essential
The idea came from the fact that various data scientist jobs, such as data purification, data visualization, and model creation, may be partially automated using autoML models. However, while the technologies may be capable of doing the work quickly, many fail to emphasize the component of “domain expertise” in the definition of a data scientist.
Domain expertise refers to data scientists’ substantial understanding of a certain topic that they use for their data science talents. So, even if a substantial portion of the data pipeline and workflow is automated, a data scientist is still required to convert the business problem being handled into the relevant format. Furthermore, determining which data science model to use depending on the sector is difficult. Especially when the sectors are so disparate; a recommendation system for the health business would be useless for a movie streaming site.
Is there a need for data scientists?
Most firms’ data science workflows are quite similar. Many businesses engage data scientists to help them overcome comparable business difficulties. The majority of the models created do not necessitate the development of unique solutions. The majority of the ways you will take to tackle data-driven challenges at these firms have most certainly previously been employed, and you may draw inspiration from the vast amount of materials available online.
To begin with, data science has never been about reinventing the wheel or developing extremely complicated algorithms. A data scientist’s role is to use data to create value for a company. And in most businesses, just a small percentage of this includes developing ML algorithms. The Second thing is that, there will always be difficulties that automated technologies cannot tackle. These tools offer a limited variety of algorithms from which to choose, and if you come into an issue that requires a mix of techniques, you will have to solve it manually. While it doesn’t happen very frequently, it does happen — and as an organization, you need to recruit individuals who are talented enough to accomplish it. Furthermore, systems like DataRobot cannot perform data pre-processing or any of the heavy work that precedes model creation.
Human Interference
As someone who has built data-driven solutions for both startups and major corporations, the environment is vastly different from working with Kaggle datasets. There is no such thing as a fixed problem. Typically, you are provided a dataset and a business challenge. It is your responsibility to determine what to do with consumer data in order to optimize sales for the organization. This means that a data scientist must have more than simply technical or modeling abilities. You must relate the facts to the situation at hand. You must choose external data sources that will help you optimize your solution.
Data pre-processing is time-consuming and tedious, not only because it necessitates good programming abilities, but also because you must experiment with various variables and their relevance to the task at hand. Model construction is not necessarily a component of this procedure. A basic computation may be enough to execute a task such as a customer rating. Only a few problems need you to make a forecast.
Is Data Science really going to die?
The concern first appeared in the accounting industry a few years ago, when it was suggested that AI might eventually replace accountants and auditors. However, even if an AI computer can perform almost everything an accountant can, you still need the accountant’s experience for tax exemptions, credits, and so on. Similarly, a data scientist may rely on autoML models to gather, visualize, and clean data, allowing them to focus more on business objectives. Furthermore, given data science is still in its infancy in many traditional fields such as banking, healthcare, defense, and government, the demand for data scientists will only grow in the future. The irony is that for AutoML data exploration to take place, data must first be collected, which is done by a data scientist.
Ultimately, the value of a data scientist to an organization is derived from their ability to apply data to real-world use cases. There is no meaningful advantage to a company unless the data are interpretable, whether it is constructing a segmentation model, a recommendation system, or analyzing consumer potential. The profession will continue to exist as long as a data scientist can solve issues using data and bridge the gap between technical and business abilities.
Author- Toshank Bhardwaj, AI Content Creator
