What is a Data Scientist career path?
Data Scientist is a fresh profession, and it has created many other new career paths for youngsters. People end up in data science from various backgrounds like economics, computer science, mathematics etc. Some of which had previous experiences in corresponding professions.
How to Get Into Data Science?
Many reports found that around 37 percent of data scientists have received their bachelor’s degree, generally in a field such as mathematics, statistics, computer science, information technologies, or data science. In addition, 35 percent of data scientists carry a master’s degree, and 14 percent have achieved a doctoral degree.
Some assume that a degree is not as important to career success as achieving early mastery in programming languages such as Python, Java, and R, which can deliver substantial advantages in the long run. A data analytics boot camp will instruct the students about the aptitudes to follow an entry-level data science role and join this exciting career. Such boot camps are short-term or intensive courses lasting three to six months, offering adjustable scheduling, online coursework, and practical training.
What Are the Different Career Paths for Data Scientists?
Data science is a wide field that beholds a mixture of various paths and career opportunities within it. Therefore, it is quite natural if a person is confused or unsure what each position is about or which career path is more suitable for them.
Some of the best career options for data scientists are-
Data Analyst
This role is generally deemed as the “Entry level” in the data science realm. A Data Analyst’s function is to accumulate information from different sources, analyze its ways & present it to stakeholders instinctively.
Data Scientist
The function of a data scientist is an extraordinary mixture of skills that can both unlock the understanding of data and tell a fantastic story via the data.
Data Manager
Data Managers are the ones who are accountable for building & managing techniques around data as per the specifications from Data Architects.
Data Architect
Data architects develop a blueprint for all the data management strategies. The corporation’s every system & infrastructure associated with data needs to be created and maintained by determining all possible structural and installation explanations.
Data Engineer
This is yet another very prominent career path for a Data Scientist. A Data Engineer is accountable for building, nurturing & managing data pipelines that assist in making information obtainable to data scientists at all times.
Business Analyst
Business analysts are closely connected to Data analysts, with basic differences in the way they employ and function.
Machine Learning Engineer
Machine learning engineers are frequently one level down the line than Data scientists. Therefore, the immediate obligation of an ML engineer is to write code and develop data funnels & pipelines for Machine learning applications.
The Future of Data Science
Data science job growth is ensuing across a mixture of enterprises every year. According to the U.S. Bureau of Labor Statistics, the top three states utilising the most data scientists are California, Texas, and New York respectively, with New York City being the prime metropolitan area for data scientist employment in the U.S. While the need for data scientists is incredibly high in these regions, these professionals are in high demand across the nation and the globe.
Data Science Salary
Another appealing element of a data science career is the compensation. According to the Bureau of Labor Statistics, the standard annual salary for a data scientist in the U.S. is $103,930.
And according to the BLS, the states with the highest standard annual salary were California ($129,060), New York ($124,240), and Washington ($118,320). The business sectors noting the highest annual salary for data scientists comprise computer/peripheral equipment ($144,090), finance ($143,490), and dealer wholesalers ($142,300).
A career as a data scientist can provide substantial chances and rewards. The necessity for these professionals is just evolving on a national and global scale, with unprecedented expansion in both the quantity and granularity of data, as well as the growing usage of that data to navigate decision-making and steer AI and ML.
