Data Scientist Vs Data Engineer | Which is better?
Both data scientist and data engineers are the part of team who analyze the business and convert its raw data into useful information for decision making and betterment, growth of business.
Both play an important role in business analysis and making strategic decision for improvement of business.
Who is data scientist?
Data Scientists are responsible for solving business problem by doing statistical analysis on the data, build a model and generate an insight for the business to solve the problem. The problems can be more complex than that of data engineers.
Data scientist are mainly concerned with performing these tasks. However these tasks can vary depending upon the requirement of the business or post.
- Carrying out deep analysis on a large volume of data prepared by the data engineers. The analysis can be from basic to advance level.
- Data integration and optimization with the help of machine learning and in some cases deep learning. He should be well aware of machine learning and deep learning principles.
- Database/SQL knowledge is the key in optimization.
- Reporting and visualization of data. For this, data scientist may use R/Pythong or Hadoop skills.
- Building of models for the business. The knowledge of business is also necessary.
Who is data engineer?
These are the persons who are responsible for generation of data. They do the task by building a platform/framework/infrastructure and architecture.
Data engineering revolves around creation of data. Data engineer works on specific areas of data and answer the different types of questions which are helpful to understand the data.
Some duties (job description) performed by Data Engineers are briefly described here. The duties may vary from company to company.
- Gather the required data.
- The record of metadata about data.
- How the data is stored and technologies associated with optimization of data like NoSQL, Hadoop or any other technology.
- Processing of data with the help of tools to transform and summarize it for specific purpose.
- Who can access the data
- Ensuring the data security, data encryption and access of data.
Data Scientist Vs Data Engineer
Definition | Data Engineer collects and prepare data (a large volume of data) for data scientist for analytical purposes. The prepared data can easily be analyzed. | Data Scientist analyze, interpret and optimize the large volume of data and build the operational model for the business to improve the operations of business. |
Focus | The focus of data engineers is to build framework/platform for generation of data. | The main focus of data scientists is on statistical and mathematical methods for the purpose of analysis of data that is generated by data engineers. |
Skill set | Skill set for data engineer includes - Data warehousing - ETL - Advanced programming - Hadoop (for analysis purpose) - Data architecture & pipelining - Knowledge of SQL | They require skills of - Mathematical concepts - Statistical analysis, - Advanced programming knowledge - Machine learning concepts - Deep learning concepts - Analytical skills using tools like RapidMiner, Hadoop etc. - Decision Making skills |
Tools | The tool set of data engineer includes ETL tools, Databases (MySQL, PostgreSQL, MongoDB, Cassandra), Programming languages like Python, Java, C#, C++ and analysis tools like Spark and Hadoop | Data scientist uses programming languages such as Python, R, Java, C#, analysis tools like RapidMiner, Matlab, SPSS (for advanced statistical analysis), Microsoft Excel, Tableau |
Educational Background | Computer science, computer engineering | Computer science, mathematics, statistics |
Responsibilities | The responsibilities of data engineer are: - Acquiring data - Storage of data - Clean the data and remove errors - Remove data redundancy. - Convert the data into required format | The responsibilities of data scientist are: - Analyze and optimize data using machine learning or deep learning - Data integration and analysis - Advance analytics - To develop operational model for a business - Involvement in strategic planning |
Salaries | According to glassgoor.com, average salary of data engineer in United States is $114,887/year | While average salary of data scientist in United States is $120,495/year. |
Job opportunities | There is lot of opportunity in this post. According to glassdoor.com, there are more than 85000 job openings in United States. | More and more job openings as compared to previous years. According to glassdoor.com, there is more than 23000 job openings in United States. |
Bottom Line
Besides some differences mentioned in the above table, there are some overlapping skills of the data scientist and data engineers. These include knowledge of programming languages (R/Python), big data and working with data sets.
Further reading
The work of data scientist and data engineer are very closely related to each other. For a business to be successful, the specific role according to their posts is necessary. A business while creating the posts of data scientist and data engineer must be careful in defining their duties, which ultimately play role business success.
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