Difference Between Data Science vs 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 aspects. In this article, we will look at the difference between Data Science vs Data Engineering in detail.
Head to Head Comparison Between Data Science and Data Engineering (Infographics)
Below is the top 6 comparison between Data Science and Data Engineering:
Key Differences Between Data Science and 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:
- 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.
- Data engineering is responsible for discovering the best methods and identification of optimized solutions and toolsets for data acquisition. Data Science is responsible for developing models and procedures for extracting useful business insights from the data.
- Data Engineer lays the foundation or prepares the data on which a Data Scientist will develop the machine learning and statistical models.
- 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.
- 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.
- 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 and Data Engineering Comparision Table
While both terms are related to data yet they are totally distinct disciplines, in this section, we will do a head-to-head comparison of both Data Science and Data Engineering.
| Basis for Comparison | Data Science | Data Engineering |
| Definition | Data Science draws insights from the raw data for bringing insights and value from the data using statistical models | Data Engineering creates API’s and frameworks for consuming the data from different sources |
| Area of Expertise | This discipline requires an expert-level knowledge of mathematics, statistics, computer science, and domain. Hardware knowledge is not required | Data Engineering requires programming, middleware, and hardware-related knowledge. Machine learning and Statistic knowledge is not mandatory |
| Work Profile | Establishes the statistical and machine learning model for analysis and keeps improving them
Builds visualizations and charts for analysis of data |
Helps the Data Science team by applying feature transformations for machine learning models on the datasets
Does not require working on data visualization |
| Responsibilities | Is responsible for the optimized performance of the ML/Statistical model | Is responsible for optimizing and performance of whole data pipeline |
| Output | The output of Data Science is a data product | The output of data engineering is a Data flow, storage, and retrieval system |
| Examples | Ann example of a data product can be a recommendation engine like YouTube recommended video list, email filters for identifying spam and non-spam emails. | One example of Data Engineering would be to pull daily tweets from Twitter into the hive data warehouse spread across multiple clusters. |
Conclusion
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 the 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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