10 Reasons not to Become a Data Scientist in 2023

Things that every Data Science student should know while choosing it as a career

Data Science is an emerging field in recent times, yet Data Scientists are revealing the greatest secrets and warning Data Science students by uncovering the reasons not to become Data Scientists in the future. Data scientists are specialists who write code and use it along with statistical techniques to extract knowledge from data.

It incorporates methods from several disciplines to draw knowledge and facts from unstructured, raw data. Additionally, data scientists collaborate with various groups and divisions inside a business such as sales, marketing, and design. Working with these divisions enables data scientists to comprehend the requirements of each area of the company and create solutions that effectively handle those issues.

Major tasks for a Data Scientist –

• Analysis of data to spot patterns and trends
• Analyzing the data to find answers and opportunities
• To identify the data-analytics issues that present the firm with the most opportunities
• Choosing the appropriate variables and data sets
• Assembling huge data collections, both structured and unstructured, from many sources ensures the data is accurate, full, and consistent by cleaning and validating it
• Creating and utilizing models and techniques to mine large data sets
• Presenting findings in a visual manner and other methods to stakeholders

However, Data Science has loopholes in these tasks and expose the reasons to not become a Data Scientist in the future i.e., 2023. Let us know 10 Reasons not to become a data scientist in 2023.

1. No proper infrastructure for Data Scientists – The majority of businesses have made impulsive hires of data scientists without the necessary support systems in place. As a result, they spend their time in their new role creating analytics reports or setting up data rather than writing machine learning algorithms.
2. Assigning inappropriate work – It gets worse because there is no end in sight to businesses, beginning to assign data scientists duties that are not appropriate for data scientists and ends up in organizing and cleaning up data; in fact, it is a crucial skill. However, as a data scientist, you want to also deploy models and have an impact on the economy and society.
3. Over responsibilities – There is nothing wrong with taking responsibility; however for a Data scientist it will raise over time and needs to work restlessly
4. Unsatisfactory pay – Data Scientists believe they are being paid unfairly in light of the market, location, and industry. Thus, many of them are not enthusiastic about their work
5. Lack of professional and personal growth opportunities – They also believe that there is no growth in their role since they are dedicated to the particular work in the organization.
6. Stressful work environment – They usually experience stressful work which can result in burnout and reduced productivity.
7. Disconnected from goals and vision – The objectives, vision, and mission of the scientist do not resonate with the company. Thus, they are unable to interact and get disconnected.
8. Rigidity in organizations – Some companies are rigid and do not adapt to change. Because their industry is so dynamic, data scientists are always looking for fresh problems to solve and this does not work with them.
9. Gaps between reality and expectations – Companies will employ data science specialists and assign them tasks unrelated to data science roles. Professionals often leave their jobs because they are dissatisfied with them.
10. Loss of Interest – Even if one of the above-mentioned is present in an organization then it will lead to the loss of interest as a Data Scientist.  

Conclusion – These are a few of the reasons not to become a Data Scientist in the future. However, the advantages of being a Data Scientist cannot be overruled.