Despite the fact that data science is a young subject, data scientists are unearthing the deepest secrets and cautioning students by outlining the reasons they should avoid becoming data scientists in the future. Specialists known as data scientists build code and combine it with statistical methods to glean insight from data.
In order to extract knowledge and facts from unstructured, raw data, it utilises techniques from numerous disciplines. Additionally, data scientists work closely with numerous departments and organisations inside an organisation, including sales, marketing, and design. By collaborating with various divisions, data scientists can better understand the needs of each part of the business and develop solutions that address those problems.
Principal duties of a data scientist
data analysis to identify patterns and trends
data analysis to uncover solutions and opportunities
to determine which data analytics problems provide the company the greatest opportunities
selecting suitable variables and data sets
putting together enormous data collections from various sources, both organised and unstructured cleans and validates the data to make sure it is correct, complete, and consistent.
mining massive data sets by building models and using strategies
presenting findings to stakeholders using different techniques and a graphic format
Data Science, however, exposes the reasons why one shouldn’t become a data scientist in the year 2023 by having flaws in these duties. Give us 10 reasons why you should avoid becoming a data scientist in 2023.
The lack of a suitable infrastructure for data scientists Most companies have hired data scientists on the spur of the moment without the appropriate support infrastructure. As a result, instead of designing machine learning algorithms in their new position, they spend their time setting up data or preparing analytics reports.
Assigning improper work – As organisations continue to assign data scientists tasks that are not suitable for them and ultimately result in organising and cleaning up data, which is a critical talent, the situation gets worse because there is no end in sight. But as a data scientist, you also want to use models and make a difference in the world of business and society.
Overresponsibilities – There is nothing wrong with accepting responsibility, but a data scientist will find that they grow over time and require a lot of erratic effort.
Salary is subpar, and data scientists feel that their pay is unreasonable given the business, the market, and the location. Consequently, many of them are not motivated by their employment.
Lack of opportunity for professional and personal growth – They also think that because they are committed to a certain task inside the organisation, their roles do not have room for advancement.
Workplace stress – They frequently work in stressful environments, which can lead to burnout and decreased productivity.
Disconnected from aims and vision – The corporation does not share the scientist’s objectives, vision, or mission. They become disconnected as a result of being unable to communicate.
Organizational rigidity – Some businesses are resistant to change and are rigid. Data scientists are constantly looking for new challenges to tackle because their sector is so dynamic, thus this does not apply to them.
Unmet expectations – Businesses will hire data science specialists and give them tasks unrelated to their data science roles. Professionals frequently quit their employment due to dissatisfaction.
Loss of Interest – Even if one of the aforementioned factors is present in a company, it will cause a Data Scientist to lose interest in their work.
In summary, these are a few reasons why you should avoid becoming a data scientist in the future. The benefits of being a data scientist, however, cannot be ignored.
