Data science has emerged as one of the most sought-after fields in the 21st-century job market. It is the multidisciplinary study of data that combines knowledge of statistics, mathematics, and computer science. The scientific tools extract and uncover useful insights from structured and unstructured data.
So, this revolutionary technology is transforming the landscape of work and delivering immense business value. Following the industry trends, it is no secret that a career in data science can prove to be extremely beneficial. Want more proof? Checkout data science salary in India.
Moreover, the applications of data science reach far and wide. Specialists in the field can follow diverse career paths, which is why data science courses have picked up the pace in recent times. From those transitioning to advanced roles to those simply looking to hone their skills for entering the workforce, the discipline offers something for everyone.
Besides the lucrative pay and the multitude of job positions, there can be various reasons why data science as a career makes sense for you. But, before you commit your time and money to it, consider all the pros and cons. Below are some factors on which you can base your decision!
Pros of Data Science
1. Highly in-demand field
Data Science is one of the most in-demand jobs for 2020. Data science and analytics would create approximately 11.5 million jobs by the year 2026. And India is the second most prominent hub of such positions after the United States. So, data science is a highly employable and appealing sector as per the current industry trends.
2. Availability of highly-paid and diverse roles
Not only is the demand for data scientists booming, but the kinds of job positions are also abundant. As analytics take centre stage in decision-making, more and more businesses are hiring data scientists. Since it is a relatively less saturated area with a moderate supply of talent, opportunities requiring diverse skill-sets and competencies are available today. According to Glassdoor, a data scientist can earn $116,100 per year on average.
3. Evolving workplace environments
Data science is shaping the workplace of the future. With the advent of artificial intelligence and robotics, more and more routine and manual tasks are getting automated. Data science technologies have made it possible to train machines in performing repetitive tasks as humans take on more critical thinking and problem-solving roles. These are highly-paid and prestigious positions that capitalize on technological disruptions to simplify arduous work.
4. Improving product standards
Usage of machine learning has enabled companies to customize their offerings and enhance customer experiences. E-commerce sites serve as the best example of this development. The websites use Recommendation Systems to refer products and give personalized advice to users based on their past purchases. By understanding human behaviour and backing decisions with data, businesses can match their products and services to customer needs and make the necessary improvements.
5. Invigorating businesses
Businesses require skilled data scientists to assist the senior staff members in taking important corporate actions. These specialists extract hidden information from huge chunks of data to provide additional insights for decision-making. The large datasets also have to be cleaned and enriched. So, there are various reasons why data science is valuable for businesses nowadays. Some of the industry sectors that are benefitting include healthcare, finance, banking, management, consultancy, and e-commerce.
6. Helping the world
Predictive analytics and machine learning have revolutionized the healthcare industry. Data science is saving lives by enabling early detection of tumors, organ anomalies, and more. In a similar vein, it is helping the world’s farmers by introducing new ways of scientifically dealing with agricultural pests and harmful insects.
Cons of Data Science
‘Data scientist’ is a broad term. When someone introduces themselves as a data scientist, it can seem difficult to pinpoint what they actually do. This is because the actual role depends on the area of specialization. Depending on one’s skills and qualifications, one can be a data science researcher, developer, business analyst, or even a product engineer. Therefore, data science is often dubbed as an ambiguous field by many experts. At the same time, others regard it as the fourth paradigm of science!
Data science is a complex field of study that borrows concepts from other academic, scientific, and mathematical disciplines. Recently, many online courses have cropped up to fill the skill-gap in the data science sector. But, it is challenging to prepare a workforce that is equally proficient in all three subjects that constitute it – Math, Computers, and Statistics. Someone with a background in Statistics may find it difficult to master computer science. So, data scientists have to keep learning and upgrading their skills to make full use of the opportunities.
Data science roles require a firm hold on domain knowledge. For example, a research study on analyzing genomic sequences would prefer someone with a background in genetics and molecular biology. Similarly, business analytics roles may expect prior knowledge of economics and finance. It is due to this reason that data scientists sometimes find it tricky to transition from one industry to another.
Data-driven predictions minimize business risks to a great extent. But in some cases when arbitrary data is provided, the expected results may not be attained. Such instances can bring down the confidence in data science systems. So, it is equally important to have relevant datasets and data points to get meaningful and actionable insights for decision-making. It is also a good practice for the management and data scientists to set goals collaboratively before they devote time and resources to the process.
5. Privacy Issues
Consumer data fuels major business strategies in modern organizations. Companies hold large volumes of identifiable data with them, which has raised ethical concerns around data privacy. A single security lapse can compromise personal data and thus, pose a threat to the individuals. As a result, it has become pertinent to integrate cybersecurity and privacy measures within data science techniques.
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When you are trying to build a career in data science, choosing the next right step can be difficult. There are several data science courses out there, which can complicate your decision-making process. So, evaluate your options by considering all the advantages and limitations before you dive in!
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Which is more beneficial: artificial intelligence or data science?
The two most significant technologies in the world now are data science and artificial intelligence. While Data Science employs AI in its processes, it does not fully reflect AI. Pre-processing, analysis, visualization, and prediction are all part of the Data Science process. Artificial intelligence, on the other hand, is the use of a predictive model to anticipate future occurrences. Data Science employs a variety of statistical approaches, whereas AI employs computer algorithms. Finding hidden patterns in data is the goal of data science while the goal of AI is to give the data model autonomy.
What aspect of data science is the most difficult?
Data scientists must be capable of resolving difficult issues. These issues are centered on building models that address some of the most difficult business issues. This necessitates a good sense of problem-solving and a strong grasp of mathematics. This makes data science an even more challenging task for many companies. Data scientists also confront significant problems in day-to-day operations, which need a great deal of critical thinking, decision-making, and analytical abilities. One of the most important tasks in evaluating an issue and creating a solution is to first identify the problem and its many aspects.
What role does data science play in assisting firms in making better decisions?
While classical statistics and data analysis have always emphasized the use of data to explain and forecast, data science expands on this particular compulsion. It learns from data by creating algorithms and programs that take data from a variety of sources and use blends of mathematics and computer science approaches to extract more practical insights. Data science, unlike traditional analysis, dares to ask more questions by examining unstructured 'big data' gathered from millions of sources and nontraditional mediums including text, video, and pictures. This enables businesses to make better decisions based on consumer information.