Data Scientist vs Data Engineer: What’s the Difference?

In today’s digital world, data is a prized fuel. Speaking of data, the expertise of data scientists and data engineers is in high demand in the tech world. Once you understand the facets of both these job roles, you will know which job is suitable for you.

With that in mind, in today’s blog, I’m going to elaborate on the roles and responsibilities of data scientists and data engineers and the differences between the two roles.

What is data science?

It involves studying data structures and processes with the aim of preserving data sets and generating value from them. The goal of data science is to make sense of random data clusters using a variety of approaches, applications, concepts, and algorithms. With almost all types of organizations generating exponential quantities of data today, tracking and storing that data can be tricky. To manage the growing collection of data, modelling and data warehousing are critical aspects of the data science field. Organizations use data science applications to accomplish goals and direct business processes.

A significant part of data science is the application of approximations, the analysis of data results, and the understanding of outcomes. Like software engineers, data scientists aim to optimize algorithms and balance speed with accuracy.

The process of gaining valuable insights from data usually involves the use of several technologies, such as artificial intelligence, machine learning, and data mining.

What is data engineering?

Data engineering is a technical self-discipline that involves the constructing and designing of structures for managing, collecting, storing, and examining large statistics at scale.

Data engineering emphasizes on the functions and harvesting of massive data. It focuses on sensible purposes of data series and analysis. In this information is modified into a beneficial layout for analysis. Data engineering is like software program engineering in many ways. Beginning with a concrete goal, data engineers are tasked with placing collectively purposeful structures to recognize that goal.

Now that you are familiar with both the disciplines, let us take a look at the pre-requisites for each.

Pre-requisites for learning data engineering

Take a look at the skills that are required in order to become a data engineer:

• Excellent understanding of database fundamentals
• Expertise in writing complex queries
• Exposure to big data tools
• Knowledge of data wrangling, data cleaning, mining, visualisation, and reporting tools

Pre-requisites for learning data science

• Good understanding of statistics, mathematics, database fundamentals, and machine learning
• Skilled in computer programming languages like R, Python, Java, and C++
• Efficiency in advanced probability and statistics
• Expertise in database management systems
• Experienced in using cloud services providing platforms like AWS/GCP/Azure
• Knowledge of multiple machine learning and deep learning algorithms
• Knowledge of tools like Apache Spark and Apache Hadoop
• Good communication skills

Let us now turn to the tools used in both the disciplines.

Tools used in data science

Data science includes data visualization tools, data analytics tools, and database tools. Software engineering includes programming tools, database tools, format tools, CMS tools, integration tools, and so on.

Tools used in data engineering

Even the most knowledgeable engineers require specialised tools. Often, these are software programs or programming languages that permit data engineers to organize, manipulate, and analyse giant datasets. But there isn’t a one-size-fits-all tool; it’s a good idea to make use of a device that aligns with your goals.

I must add that a crucial finding is that the software engineer builds their decisions on the needs established by the data scientist or data engineer. So, data science and software engineering frequently work together. Finding information and trends concerning functions or products is also helpful in data science. It’s also important to keep in mind that effective communication with clients or end-users helps to create more powerful business solutions because requirement gathering is the most important step in the SDLC.

Reach out to us at Nitor Infotech with your thoughts about this blog or if you’d like to learn more about our big data and analytics offerings.