Data Scientists vs. Data Analysts vs. Data Engineers
Posted: Aug 16, 2019
As mentioned, often the data scientist role is confused with other similar roles. The two main ones are data analysts and data engineers, both quite different from each other, and from data science as well.
Let’s explore both of these roles in more detail.
Data Analyst huge numbers of indistinguishable abilities and duties from a data researcher and here and there has a comparable instructive foundation too. A portion of these mutual aptitudes incorporates the capacity to:
- Access and query (e.g., SQL) different data sources
- Process and clean data
- Summarize data
- Understand and use some statistics and mathematical techniques
- Prepare data visualizations and reports
A portion of the key contrasts, nonetheless, is that data examiners ordinarily are not PC software engineers, nor in charge of measurable displaying, AI, and a considerable lot of different advances sketched out in the data science process above.
The tools used are usually different as well. Data analysts often use tools for analysis and business intelligence like Microsoft Excel (visualization, pivot tables, …), Tableau, SAS, SAP, and Qlik.
Analysts sometimes perform data mining and modeling tasks, but tend to use visual platforms such as IBM SPSS Modeler, Rapid Miner, SAS, and KNIME. Data scientists, on the other hand, perform these same tasks usually with tools such as R and Python, combined with relevant libraries for the language(s) being used.
Lastly, data analysts tend to differ significantly in their interactions with top business managers and executives. Data analysts are often given questions and goals from the top down, perform the analysis, and then report their findings.
Data scientists however, tend to generate the questions themselves, driven by knowing which business goals are most important and how the data can be used to achieve certain goals. In addition, data scientists typically leverage programming with specialized software packages and employ much more advanced statistics, analytics, and modeling techniques.
Data engineers are becoming more important in the age of big data, and can be thought of as a type of data architect. They are less concerned with statistics, analytics, and modeling as their data scientist/analyst counterparts, and are much more concerned with data architecture, computing and data storage infrastructure, data flow, and so on.
The data used by data scientists and big data applications often come from multiple sources, and must be extracted, moved, transformed, integrated, and stored (e.g., ETL/ELT) in a way that’s optimized for analytics, business intelligence, and modeling.
Data engineers are therefore responsible for data architecture, and for setting up the required infrastructure. As such, they need to be competent programmers with skills very similar to someone in a DevOps role, and with strong data query writing skills as well.
Another key aspect of this role is database design (RDBMS, NoSQL, and NewSQL), data warehousing, and setting up a data lake. This means that they must be very familiar with many of the available database technologies and management systems, including those associated with big data (e.g., Hadoop and HBase).
Lastly, data engineers also typically address non-functional infrastructure requirements such as scalability, reliability, durability, availability, backups, and so on. Join Universal Academy which gives best Data Science Training In Indore.
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