In today's world, the internet is saturated by the article of why data science is the sexiest job of the 21st century. But very few have spoken about the data science cons. undoubtedly, data science has rapid growth and this skill is in high demand and it also pays well. This technology is a good combination of programming, statistics and business analysis.
Here I will provide you the important insights of the data science field that will help you to choose the right course for you.
Pros of being a data scientist
Data Science in Demand
With year-over-year growth in this field, a data scientist has taken up the top position in LinkedIn analysis for the most promising job of the 21st century. A study we conducted estimated that even in a larger analytical ecosystem, 70% of vacancies are for data scientists with less than five years of work experience. In addition, potential job seekers with very few people who have the skills to succeed in this area have many options.
High Paying Job
According to Glassdoor, data scientists can earn an average salary of $113,309 per year. Data science is one of the top lucrative career options for the student. There may be one reason for being a high paying job that data science makes companies smarter. The company takes smart decisions and can make an important place in the top companies.
Diversify
Data science is industry-independent and has many applications in a variety of industries, including healthcare, banking, e-commerce, and marketing. Therefore, you are not tied to a specific company or role and can work in any area where data is used for decision making. For example, the advent of machine learning (ML) marked significant improvements in the healthcare sector. One of the most important applications was the early detection of tumors.
Challenging Work
Data science has multiple disciplines including mathematics, statistics, analysis, and programming, etc. since it is a growing skill day by day it demands new skills to learn. So it can be a challenging work for a data scientist. There is no single template by which you can use that template for multiple projects. For each project, you have to learn a new skill.
Cons of a data scientist
The ambiguous job role of a data scientist
Although it has become a buzzword over time, data science has no clear definition. This is essentially the study of data, and this can include extraction, analysis, visualization, etc. Create information to make business decisions. It would also depend on the area in which the company specializes. However, it is certain that all data scientists have to deal with a lot of raw data, which can take a long time. In addition, companies often provide arbitrary data that may not deliver the expected results.
Difficult To Master in Data Science
As mentioned above, data scientists need to work on large amounts of data to solve business problems. This includes expertise in a long list of skills, including computer programming and software applications, statistics, data analysis, and data visualization - and these are just technical skills. It is therefore far from possible to master every area and to be equally competent in each of them. Although many online courses have attempted to fill this skills gap, it remains difficult given the breadth of the subject. That brings us to the next point.
Simplifying Technical Concept
With all the skills you have acquired for your work, it is useless if you cannot pass your results on to stakeholders in a way that you understand. Explaining technical concepts to a non-technical audience is a major challenge for most data scientists who find it difficult to step back from something they have been in for a long time. This means that in addition to a long list of technical skills, you also need to acquire communication skills. And that's not all.
The technical concept should be acquired for your work, most data scientist who find it difficult to step back from something they have been in for a long time.
Multiple department Expertise
Data science must have multiple department expertise because, without industry knowledge, data scientists cannot make the right decision in order to assist the company. So he or should must be expertise in their industry where they work. So this can be a challenging task for them. It arises difficulties for data scientists when they migrate from one industry to another industry.
Problem with data privacy
Data is fuel for many industries. Data scientists help industries to make data-driven decisions. However, the data used can violate customers' privacy. The customer's personal data is visible to the parent company and can sometimes lead to data leaks due to a security error. Ethical issues related to maintaining the confidentiality of data and how it is used have been a problem for many industries.
Conclusion
After weighing the pros and cons of data science, we can imagine the full picture of this area. Although data science is an area with many lucrative advantages, it also suffers from its disadvantages. As a less saturated and well-paid field that has revolutionized multiple horizons, it also has its own background when one looks at the breadth of the field and its interdisciplinary nature. Data science is a constantly evolving field that will take years to acquire. Ultimately, it is up to you to decide whether the benefits of data science will motivate you for your future career or the disadvantages that will help you make a prudent decision!
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