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Difference between Data Scientist, Data Engineer, Data Analyst
Data scientists, data engineers, and data analysts are all professionals who work with data in some way. However, they have different roles and responsibilities. Read this article to find out more the job profiles of data scientists, data engineers, and data analysts and how you can distinguish among them.
Who is a Data Scientist?
A Data Scientist is one who analyses and interprets complex data in digital form. Data scientists are responsible for extracting insights and knowledge from data. They use a variety of techniques, including machine learning, to analyze data and communicate their findings to stakeholders.
There are several ways of becoming a data scientist, the most common one is by acquiring enough experience and learning the various data scientist skills.
The most important data scientist skills include advanced statistical analysis of data, a depth understanding of machine learning, data conditioning, etc.
Who is a Data Engineer?
A Data Engineer focuses on the optimization of techniques, building data in the required format and so on. To become a data engineer, one must either have a master's degree in a data-related field or acquire enough amount of experience as a Data Analyst.
A Data Engineer is required to have a strong technical background with the ability to create and integrate APIs. Data engineers also require to understand data pipelining and performance optimization.
Data engineers are responsible for building the infrastructure to store, process, and analyze data. This includes tasks such as setting up data pipelines, building data lakes, and ensuring that data is properly formatted and normalized.
Who is a Data Analyst?
A Data Analyst focuses on data cleanup, organizing raw data, visualizing data and to provide technical analysis of data. Data analysts are responsible for a wide variety of tasks including data mining, data purification, the use of statistical methods, data maintenance, and the correction of errors through the development of databases and computer programs.
Data analysts must be able to work with other divisions of the organization like management and IT, to properly report on their results and create targets. Data analysts are responsible for using the data to answer business questions and provide help in the decision-making process. They may use tools such as SQL and Excel to extract and analyze data, and then present their findings in a clear and concise manner.
Difference between Data Scientist, Data Engineer, and Data Analyst
The following are some of the important differences between Data Scientist, Data Engineer, and Data Analyst-
Data Scientist focuses on a futuristic display of data.
Data Engineer focuses on improving data consumption techniques continuously.
Data Analyst focuses on the present technical analysis of data.
Data Scientist roles are to provide supervised/unsupervised learning of data, classify and regress data. Data Scientists heavily used neural networks, machine learning for continuous regression analysis.
Data Engineer roles are to build data in an appropriate format. A data engineer works at the back end. A data engineer uses optimized machine learning algorithms to maintain data and make data available in the most appropriate manner.
Data Analyst performs data cleaning, organizes raw data, analyze and visualize data to interpret the analysis.
Big Data − R, Python, SAS, Pig, Apache Spark, And Database − Hadoop, SQL, Programing: Java, Perl.
Big Data − R, Python, SAS, SAS Miner.
Big Data − Pig, Database: Hive, Hadoop, MapReduce.
All the three roles involve working with data in some way, but they have different focuses and responsibilities. A data scientist focuses on a futuristic display of data, a data engineer focuses on improving data consumption techniques continuously, while a data analyst focuses on the present technical analysis of data.
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