November 9, 2021
Today's world is driven by data. Data has the potential to unlock the success of anyindustry, from unleashing ideas to improving decision-making processes. Data has profoundly changed the world as we know it, to the point where it's impossible to function without the insights gained from data in any domain.
With data's growing significance, a slew of data-related career roles and opportunities have popped up all over the world. Data Science will account for 28% of all digital occupations, according to an industry survey. Because of the tremendous rate of data generation and the growing need to make sense of it, they are extremely profitable. However, the same paper also emphasizes the enormous scarcityof talent in this field.
The main reason for the talent shortage in this field is the lack of clarity regarding the skills required for each role. Companies are looking to hire for niche, specialized skill sets as opposed to a jack-of-all-trades. If you want to avoid being labeled a generalist, you first need to understand the difference betweenthe three leading data roles — Data Scientist, Data Engineer, and Data Analyst. Simplilearn.com describes these three roles as follows:
1) A Data Scientist uses advanced data techniques to derive business insights, such as clustering,neural networks, decision trees, and so on. You will be the most senior member of a team in this position, and you should have extensive knowledge of machine learning, statistics, and data handling. After receiving feedback from Data Analysts and Data Engineers, you will be responsible for creating actionable business insights. You should be able to perform the functions of both a data analyst and a data engineer. In the case of a data scientist, however, theskill sets must be more in-depth and comprehensive.
2) In a data analytics team, a Data Analyst is an “entry-level” position. In this position, you must be skilled at converting numerical data into a format that everyone in the organization can understand. Furthermore, you must be proficient in a variety of areas, including programming languages such as Python, tools such as Excel, datamanagement foundations, reporting, and modeling. With enough experience underyour belt, you can go from being a data analyst to being a data engineer or a data scientist.
3) Data Engineers are the intermediary between data analysts and data scientists. As a data engineer, you will be responsible forthe pairing and preparation of data for operational or analytical purposes. Alot of experience in the construction, development, and maintenance of the data architecture will be demanded from you for this role. Usually, in this role,you will get to work on Big Data, compile reports on it, and send it to data cientists for analysis.
Regardless of which data science career path you choose, whether it's Data Scientist, Data Engineer, or Data Analyst, data-roles are highly lucrative and will only benefit in the future as a result of the effect of developing technologies suchas AI and Machine Learning. However, before pursuing a career in this field, keep in mind that these positions are not interchangeable and require different skill sets. You must learn to distinguish between them because the sector is already overburdened with generalists and is now experiencing a shortage of experts.
More info: https://www.simplilearn.com/tutorials/data-science-tutorial/data-scientist-vs-data-analyst-vs-data-engineer
