Data Science Vs Data Engineering – Which One Is More Useful

Difference Between Data Science and Data Engineering

Data Science is an interdisciplinary subject that exploits the methods and tools from statistics, application domain, and computer science to process data, structured or unstructured, in order to gain meaningful insights and knowledge. Data Science is the process of extracting useful business insights from the data. Data Engineering designs and creates the process stack for collecting or generating, storing, enriching and processing data in real-time. Data engineering is responsible for building the pipeline or workflow for the seamless movement of data from one instance to another. The engineers involved take care of hardware and software requirements alongside the IT and Data security and protection aspect.

Head to Head Comparison Between Data Science Vs Data Engineering (Infographics)

Below is the Top 6 Comparision Between Data Science Vs Data Engineering

Key Differences Between Data Science Vs Data Engineering

following is the difference between Data Science and Data Engineering

Data Science and Data Engineering are two distinct disciplines yet there are some views where people use them interchangeably. This also depends on the organization or project team undertaking such tasks where this distinction is not marked specifically. To establish their unique identities, we are highlighting the major differences between the two fields:

1. Data Engineering is the discipline that takes care of developing the framework for processing, storage, and retrieval of data from different data sources. On the other hand, Data Science is the discipline that develops a model to draw meaningful and useful insights from the underlying data.
2. Data engineering is responsible for discovering the best methods and identification of optimized solutions and toolset for data acquisition. Data Science is responsible for developing models and procedures for extracting useful business insights from the data.
3. Data Engineer lays the foundation or prepares the data on which a Data Scientist will develop the machine learning and statistical models.
4. Data engineering usually employs tools and programming languages to build API for large-scale data processing and query optimization. On the contrary, Data Science uses the knowledge of statistics, mathematics, computer science and business knowledge for developing industry-specific analysis and intelligence models.
5. While Data Engineering also takes care of correct hardware utilization for data processing, storage, and distribution, Data science may not be much concerned with the hardware configuration but distributed computing knowledge is required.
6. Data Scientists need to prepare visual or graphical representation from the underlying data, Data engineer is not required to do the same set studies.

Data Science Vs Data Engineering Comparision Table

While both terms are related with data yet they are totally distinct disciplines, in this section, we will do a head-to-head comparison of both Data Science Vs Data Engineering.

Conclusion – Data Science Vs Data Engineering

Data Science and Data Engineering are two totally different disciplines. Both Data Science and Data Engineering address distinct problem areas and require specialized skill sets and approaches for dealing with day to day problems. While Data Engineering may not involve Machine learning and statistical model, they need to transform the data so that data scientists may develop machine learning models on top of it. Although data scientists may develop a core algorithm for analyzing and visualizing the data, yet they are completely dependent on data engineers for their requirement for processed and enriched data. Both fields have plenty of opportunities and scope of work, with increasing data and advent of IoT and Big data technologies there will be a massive requirement of data scientists and data engineers in almost every IT based organization. For those interested in these areas, it’s not too late to start.

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