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Data scientists and data engineers work collaboratively to design and implement organizations’ data estates, facilitating the methods and means of data consumption and ensuring data security, reliability, and accessibility.
- Data Science: This interdisciplinary practice uses statistical approaches and machine learning algorithms to extract useful information from complicated datasets to provide insights for strategic decision-making.
- Data Engineering: Involves the design, build-out, and upkeep of databases, data pipelines, and other data structures, and is an important part of an organization’s data management program.
Understanding how these roles differ and where they overlap can provide a clearer view of how organizations use data, as well as a good idea of whether either of these careers might be right for you.
Table of Contents
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Data Science vs. Data Engineering
The chart below provides a high-level look at the difference between data scientists and data engineers.
| Data Scientists | Data Engineers | |
|---|---|---|
| Primary Focus | Analyzing and interpreting data to extract insights | Designing, building, and maintaining data infrastructure |
| Skills |
|
|
| Educational Background | Statistics, mathematics, or related field | Computer science, software engineering, or related technical discipline |
| Tasks and Responsibilities | Statistical analysis, predictive modeling, and influencing business strategies based on data | Database design and optimization, building data pipelines, and ensuring data reliability |
| Work Environment |
|
|
| Long-Term Goals |
|
|
| Industry Trends | Demand for expertise in machine learning, AI, and predictive analytics | Growing demand for scalable data architectures, cloud technologies, and data integration |
| Overlap and Collaboration | Collaboration with data engineers for data access and infrastructure | Collaboration with data scientists to ensure data availability and quality |
| Continuous Learning | Staying up to date on new algorithms and methodologies | Continuous adaptation to evolving database technologies and data management tools |
| Common Tools | Python, R, TensorFlow, and PyTorch | SQL, Hadoop, Spark, Apache Kafka |
What Is Data Science?
Data science is a multidisciplinary field that involves extracting important insights and information from data using a variety of scientific approaches, procedures, algorithms, and systems. By using a mix of tools and methodologies from statistics, mathematics, computer science, and domain-specific expertise to analyze and understand large datasets, data science provides a better understanding of patterns, trends, and correlations in data, eventually improving decision-making, forecasting future events, and solving complicated issues.
What Does a Data Scientist Do?
Data scientists extract insights and information from data using scientific approaches, procedures, algorithms, and systems. They lead data-gathering initiatives by finding relevant sources and collaborating with data engineers to provide high-quality data. They also clean and process raw data, do exploratory data analysis, and build features to improve predictive modeling using their statistical and programming abilities.
Data scientists use machine learning techniques to develop models, fine-tune parameters, and rigorously analyze results. Data scientists also create data visualizations to help non-technical stakeholders understand complex data representations.
Median Salary of a Data Scientist
In the United States, the typical salary for a data scientist ranges from $96,000 to $140,000 per year. However, these salaries may be higher or lower depending on the state, experience, skills, and level of education. According to Glassdoor, the projected wage ranges for data scientists vary depending on their degree of expertise. Median salaries based on years of experience are as follows:
- 0-1: $96,986
- 1-3: $108,197
- 4-6: $118,101
- 7-9: $124,037
- 10-14: $131,327
- 15+: $140,079
What Is Data Engineering?
Data engineering concentrates on the actual implementation of procedures required to manage and optimize data flow inside an organization. By creating and managing systems that receive data from various sources, analyze and convert it to suit analytical requirements, and provide safe and scalable storage solutions, data engineering helps ensure a smooth flow of information through an organization.
What Does a Data Engineer Do?
Data engineers design efficient and reliable infrastructure for data-driven decision-making and orchestrate complex data processes that begin with gathering data from many, sometimes divergent sources. They also create complex pipelines that use techniques like cleansing, transformation, and aggregation to assure the quality and relevance of acquired data.
Their responsibilities include creating and managing databases and data warehouses and making strategic decisions about storage solutions. Data engineers must also deal with key elements of data security, implementing access restrictions and encryption to protect sensitive information. By seamlessly connecting diverse data sources, data engineers produce a coherent and unified data ecosystem, enabling an environment in which information flows fluidly, ready to fuel data scientists’ analytics and machine learning efforts.
Median Salary of a Data Engineer
According to Glassdoor, the typical salary range for a data engineer in the United States is between $87,000 and $185,000. Salaries vary depending on educational attainment, professional experience, skillset, and geographic region.
Salary increases for data engineers are directly related to their cumulative level of experience and the development of advanced abilities over the course of their careers. The need for specialist skills, such as fluency in certain programming languages, database administration, or data analytics tools, can have a substantial influence on income trends. Median salaries based on years of experience are as follows:
- 0-1: $87,000
- 1-3: $97,000
- 4-6: $106,000
- 7-9: $109,000
- 10+: $185,000
Educational Paths: How to Get Started
Data science and data engineering require a combination of underlying knowledge and practical skills. Even experienced professionals must constantly relearn or upskill their abilities to keep up with new technologies and approaches.
Starting a career in data science or data engineering requires a deliberate approach, beginning with a well-chosen college curriculum and progressing to specialized education courses. Degrees in computer science, information technology, or statistics provide a solid basis on which to grow with focused courses.
Exploring topics like data mining provides important insights into feature selection, data aggregation, and transformation, while understanding operating systems and programming languages serves as a foundation. In addition, learning about data visualization, database management systems, SQL, ETL, and warehousing technologies broadens your skillset.
Courses in feature engineering, Hadoop, Spark Frameworks, and machine learning provide advanced expertise and are required in this area. Pursuing certifications in big data, open-source languages such as R, and specialized tools is highly advisable to stay competitive and well-equipped for the demands of the industry.
Bottom Line: Data Science vs Data Engineering
Professionals with data science and data engineering skills are in high demand, helping companies bridge the gap between comprehending the complexities of data and creating the infrastructure to support data-driven decision-making. As organizations continue to collect massive volumes of data, the demand for talented workers in these disciplines is projected to expand, and pursuing a career in either of these fields offers a range of opportunities.
Data pipelines are a key focus area for data scientists and data engineers. Read about data pipeline architecture and design, or learn more about the difference between ETL and data pipelines.
