Data has always been vital to any kind of decision-making. Today’s world runs completely on data and none of today’s organizations would survive without data-driven decision-making and strategic plans. There are several roles in the industry today that deal with data because of its invaluable insights and trust. This article will discuss the key differences and similarities between a data analyst, data engineer and data scientist. You can even check out the Microsoft Azure Data Engineering Certification Course (DP-203)
You may also go through this recording of Data Analyst vs Data Engineer vs Data Scientist where you can understand the topics in a detailed manner.
This Edureka video on “Data Analyst vs Data Engineer vs Data Scientist” will help you understand the various similarities and differences between them.
Difference between Data Scientist ,Data Analyst and Data Engineer
| Data Analyst | Data Engineer | Data Scientist |
| Data Analyst analyzes numeric data and uses it to help companies make better decisions. | Data Engineer involved in preparing data. They develop, constructs, tests & maintain complete architecture. | A data scientist analyzes and interpret complex data. They are data wranglers who organize (big) data. |
Data Analyst
Most entry-level professionals interested in getting into a data-related job start off as Data Analysts. Qualifying for this role is as simple as it gets. All you need is a bachelor’s degree and good statistical knowledge. Strong technical skills would be a plus and can give you an edge over most other applicants. Other than this, companies expect you to understand data handling, modeling and reporting techniques along with a strong understanding of the business. Take your career to the next level with our comprehensive Data Analyst Certification Course.
Data Engineer
Data Engineer either acquires a master’s degree in a data-related field or gathers a good amount of experience as a Data Analyst. A Data Engineer needs to have a strong technical background with the ability to create and integrate APIs. They also need to understand data pipelining and performance optimization.
Data Scientist
Data Scientist is the one who analyses and interprets complex digital data. While there are several ways to get into a data scientist’s role, the most seamless one is by acquiring enough experience and learning the various data scientist skills. These skills include advanced statistical analyses, a complete understanding of machine learning, data conditioning etc. Transform raw data into actionable insights. Master Data Science with Python for analytics, machine learning, and more.
For a better understanding of these professionals, let’s dive deeper and understand their required skill-sets.
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Skill Sets
The table below illustrates the different skill sets required for Data Analyst, Data Engineer and Data Scientist:
| Data Analyst | Data Engineer | Data Scientist |
| Data Warehousing | Data Warehousing & ETL | Statistical & Analytical skills |
| Adobe & Google Analytics | Advanced programming knowledge | Data Mining |
| Programming knowledge | Hadoop-based Analytics | Machine Learning & Deep learning principles |
| Scripting & Statistical skills | In-depth knowledge of SQL/ database | In-depth programming knowledge (SAS/R/ Python coding) |
| Reporting & data visualization | Data architecture & pipelining | Hadoop-based analytics |
| SQL/ database knowledge | Machine learning concept knowledge | Data optimization |
| Spread-Sheet knowledge | Scripting, reporting & data visualization | Decision making and soft skills |
As mentioned above, a data analyst’s primary skill set revolves around data acquisition, handling, and processing. A data engineer, on the other hand, requires an intermediate level understanding of programming to build thorough algorithms along with a mastery of statistics and math! And finally, a data scientist needs to be a master of both worlds. Data, stats, and math along with in-depth programming knowledge for Machine Learning and Deep Learning.
Now that we have a complete understanding of what skill sets you need to become a data analyst, data engineer or data scientist, let’s look at what the typical roles and responsibilities of these professionals.
Next, let us compare the different roles and responsibilities of a data analyst, data engineer and data scientist in their day to day life.
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Roles and Responsibilities
The roles and responsibilities of a data analyst, data engineer and data scientist are quite similar as you can see from their skill-sets. Refer the below table for more understanding:
| Data Analyst | Data Engineer | Data Scientist |
| Pre-processing and data gathering | Develop, test & maintain architectures | Responsible for developing Operational Models |
| Emphasis on representing data via reporting and visualization | Understand programming and its complexity | Carry out data analytics and optimization using machine learning & deep learning |
| Responsible for statistical analysis & data interpretation | Deploy ML & statistical models | Involved in strategic planning for data analytics |
| Ensures data acquisition & maintenance | Building pipelines for various ETL operations | Integrate data & perform ad-hoc analysis |
| Optimize Statistical Efficiency & Quality | Ensures data accuracy and flexibility | Fill in the gap between the stakeholders and customer |
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Now data scientist and data engineers job roles are quite similar, but a data scientist is the one who has the upper hand on all the data related activities. When it comes to business-related decision making, data scientist have higher proficiency.
Explore the data analyst roles and responsibilities to understand what it takes to excel in this dynamic field in details.
After these two interesting topics, let’s now look at how much you can earn by getting into a career in data analytics, data engineering or data science. You can even check out the details of Big Data with the Data Engineering Training in Bangalore.
Salary
| Data Analyst | Data Engineer | Data Scientist |
| $59000 /year | $90,8390 /year | $91,470 /year |
The typical salary of a data analyst is just under $59000 /year. A data engineer can earn up to $90,8390 /year whereas a data scientist can earn $91,470 /year.
Looking at these figures of a data engineer and data scientist, you might not see much difference at first. But, delving deeper into the numbers, a data scientist can earn 20 to 30% more than an average data engineer. Job postings from companies like Facebook, IBM and many more quote salaries of up to $136,000 per year.
If you wish to know more about Data scientist salary, job openings, years of experience, geography, etc., here’s a full-fledged article on Data Scientist Salary for your reference.
If you are someone looking to get into an interesting career, now would be the right time to up-skill and take advantage of the Data Science career opportunities that come your way.
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