One of the most Googled phrases related to data analytics is if it is the same as data science. The terms are often used interchangeably, which can be confusing if you’re referring to a specific job role instead of the larger industry. For example, not everyone who works in the field of data science can be called a data scientist.
So, what’s the difference between a data analyst, data engineer, and data scientist? Here’s the complete rundown of the career progression you can expect when you join the data science industry:
Starting out in Data Science
There are a variety of different career paths within the field of data science. Sometimes, the distinctions between the various roles are blurred and you may find that certain roles and responsibilities overlap. Unsure about which path suits you best? In the following section, we’ll explain each role in depth to help you make an informed decision.
- An entry-level role
- Responsible for collecting and cleaning data, developing data models, and communicating the results of their analysis to stakeholders
- Minimum requirements usually include a Bachelor’s degree in quantitative fields such as statistics, mathematics, and computer science
- Data engineers design and build data pipelines to collect, store, and process data. They also develop and maintain data warehouses and data lakes
- While data analysts and scientists deal with statistics, machine learning and programming, data engineers usually deal with software engineering, cloud computing, data warehousing
- Data engineers also ensure the security of company data and regulation compliance
- Minimum requirements usually include a Bachelor’s degree in software development as well as a few years of experience as a junior data engineer
- Requires a few years of work experience with some roles requiring a master’s degree
- Responsible for data architecture, building machine learning or deep learning models that improve predictive analysis
- This is often a client-facing role where the data scientist communicates with stakeholders to understand business needs and pain points
- Data is presented through data visualization which is the responsibility of the data scientist
It is important to note that there are various data science roles that do not fit within company hierarchies. There are also roles in this field that may vary at different organizations. For example, some companies specifically recruit for roles such as Data Managers and Data Architects. A data manager is responsible for managing data systems, from data organization to data storage and from compliance to confidentiality. A data architect, on the other hand, works with stakeholders to understand the business needs and then design a data architecture that meets those needs.
Since this is an evolving field and the rate of technological advancement is quite high, various other roles have also surfaced in the past decade.
Some of these roles include:
- Business Intelligence Analyst: Uses data to help businesses make better decisions.
- Operations Analyst: Analyzes data to improve the efficiency and productivity of a business.
- Risk Analyst: Identifies and assesses risks to an organization.
- Research Analyst: Gathers and analyzes data to support decision-making.
- Marketing Analyst: Uses data to understand customers and improve marketing campaigns.
These examples display the versatility of a data science career with many paths to choose from and various skills to specialize in. While the hierarchies in the domain may not always be the same for different organizations, there are crucial differences between data analysts and data scientists, such as seniority, years of experience, and client-facing responsibilities.
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