Data Analyst, Data Engineer, Data Scientist: Which Role Is Best For You?

 

Data science is a rewarding field that offers diverse job roles. Most professionals entering the field think that all these roles are interchangeable. But that's not the truth. Each position, data scientist, data analyst, data engineer, may share some similarities, but they are different. 

Let’s see the diffrences between the three job roles. By knowing that, you’ll be able to decide which field is suitable for you after graduating from the data science coding bootcamp. 

Profile Overview: Data Analyst, Data Scientist, Data Engineer

Data Analyst

Data analyst deciphers data and helps the business make intelligent business decisions. Their insights help businesses in many ways; investment planning, marketing, product development, operations, etc. You can say it's an entry-level job, which needs a bachelor's degree and good mathematical & statistical knowledge along with data modeling, data handling, visualization, and reporting skills. 

Data Scientist

You may find the role of a data scientist similar to a data analyst, but data scientists require advanced technical skills. Even the entry-level data scientist positions require a master's degree and a Ph.D. for senior positions. They have profound knowledge in mathematics, statistics, machine learning algorithms, and programming languages. They help understand complex data and make better decisions. If a data analyst emphasizes decoding data from the past and present perspectives, then a data scientist emphasizes making reliable future predictions.

Data Engineer

A data engineer comes from a STEM background and is fluent in Big Data Mathematics and Statistics. They usually start with the traditional solution architects, working with SQL databases, SAP installations, web servers, and other systems. They help both the data scientists and the analysts by building and optimizing systems suitable for their job roles. Their focus is on leveraging data tools, maintaining architecture, and creating and managing data pipelines. 

Skills: Data Analyst, Data Scientist, Data Engineer

Best data science bootcamp teaches the mentioned skills to prepare you for a data science career. Look at which skills fit into what data science role.

Data Analyst	Data Scientist	Data Engineer
Oracle, and SQL.	Statistical and Analytical skills	SQL and NoSQL technologies
Statistics	Database systems, SQL and NoSQL	Advanced programming knowledge (R, Python)
Programming languages	Machine Learning and Deep Learning principles	Big data technologies
Scripting and Statistical skills	SAS, R, Python coding	Machine learning algorithms and techniques
Reporting and data visualization tools	Big data tools.	Data architecture and pipelining
Data Warehouse Knowledge	Data warehousing	Scripting, reporting, and data visualization tools
Spread-sheet or excel knowledge	ETL solutions	Business Acumen
Adobe and Google Analytics	Decision making and soft skills	Soft skills (communication, leadership)

Responsibilities: Data Analyst, Data Scientist, Data Engineer

Now, you will see what the responsibilities are for each role.

Data Analyst	Data Scientist	Data Engineer
Data pre-processing, collection, and documentation	Collect and clean data for training ML models architecture	Develop, create and maintain data
Create reports based on previous or current data	Examine the data to find hidden patterns	Build communication pipelines between systems
Perform data analysis using statistical tools and interpret the meaning	Hypothesis development and testing for refining business metrics	Deploy ML algorithms and models
Identify hidden data trends or patterns in the data	Build accurate and performing predictive ML models	Create data warehousing solutions

Salary: Data Engineer, Data Analyst, Data Scientist (As per Indeed)

• A data engineer's average salary is $117,071 per year.
• A data analyst earns a salary of $65,156 per year.
• A data scientist makes an average of $74,865 in a year.

Now that you've explored these three careers in detail, the question is, which is the best option for you. 

Which Role Is The Best For You?

You have got an overview of the three data science roles and also understood the differences and similarities. To choose one, you will also need to understand what do they do with data. Data engineers remain at the backend working with the data pipelines and architecture to ensure the data accrued accurately fits the organization's needs.

With the help of different data tools, they ensure data is processed accurately. They save a lot of time for data analysts and data scientists. Using the custom APIs built by the data engineer, data analysts extract the dataset to identify the trends. Their analysis helps other non-technical people to understand what data means.

On the other hand, data scientists make predictions based on the data analysts' findings and dive deep into them. They train the machine learning models, run statistical analysis and offer completely new insight into data and not just what is present or has happened in the past. 

Considering everything covered in this article, you will be able to decide which role suits you the best. Do consider your qualifications, skills, and interests when choosing your career path. 

No matter what role you choose, make sure you select the best data science coding bootcamp to acquire the skills. As it is still an evolving field, you will see many more roles coming up in the future, and only a good bootcamp can help you prepare for them.

Also, Read This Blog: How to Select the Best Coding Bootcamp for Your Career?