Difference Between Data Science vs Data Engineering
Data Science is an interdisciplinary subject that exploits the methods and tools from statistics, application domains, and computer science to process structured or unstructured data to gain meaningful insights and knowledge. Data Science is the process of extracting valuable business insights from the data. The process stack for collecting, storing, enriching, and processing data in real-time is designed and built by data engineers. Data engineering is responsible for making the pipeline or workflow for seamless data movement 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
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 tasks where this distinction is not marked specifically. To establish their unique identities, we are highlighting the significant differences between the two fields:
- Data Engineering is the discipline that develops the framework for processing, storing, and retrieving data from different data sources. On the other hand, Data Science is the discipline that creates a model to draw meaningful and valuable insights from the underlying data.
- Data engineering is responsible for discovering the best methods and identifying optimized solutions and toolsets for data acquisition. Developing models and processes for extracting practical business insights from data is the responsibility of a Data Scientist.
- 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.
- Data Engineering handles correct hardware utilization for data processing, storage, and distribution. At the same time, Data Science requires distributed computing knowledge but may not be much concerned with the hardware configuration.
- The underlying data must be turned into visual or graphical representations by data scientists; Data engineers are not required to do the same set of studies.
Data Science and Data Engineering Comparison Table
In this section, we will directly compare Data Science and Data Engineering; although both terms relate to data, they are distinct disciplines.
| 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 data from different sources. |
| Area of Expertise | This discipline requires 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 are 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
It 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 the 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 | An example of a data product can be a recommendation engine like YouTube recommended video list and email filters for identifying spam and non-spam emails. | One example of Data Engineering would be pulling daily Twitter tweets into the hive data warehouse spread across multiple clusters. |
Final Thoughts
Data Science and Data Engineering are two 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 models, they need to transform the data so that data scientists may develop machine learning models on top of it. Although data scientists may produce a core algorithm for analyzing and visualizing the data, they depend entirely on data engineers for their processed and enriched data requirements. 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 for data scientists and data engineers in almost every IT-based organization. For those interested in these areas, it’s not too late to start.
Frequently Asked Questions (FAQs)
Q1 Can a data engineer become a data scientist?
Answer: Yes, a data engineer can become a data scientist with the right skills and experience. Both roles have some similarities, but they also require different skill sets. To become a data scientist, a data engineer would need to develop the necessary skills in statistics and machine learning, as well as experience working with data to derive insights.
Q2 Which pays more, data scientists or data engineers?
Answer: Both data science and data engineering can be well-paying professions. In general, data scientists tend to earn slightly higher salaries than data engineers, but this can vary depending on factors such as location, industry, and years of experience.
Q3 Is a data engineer job stressful?
Answer: Like many professions, a data engineer job can be stressful at times, especially during critical periods with tight deadlines. However, many data engineers find their work rewarding and enjoy the challenge of solving complex problems.
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