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Difference between Data Scientist, Data Engineer, Data Analyst.
Data scientists, data engineers, and data analysts are various kinds of job profiles in Information Technology companies.
Data Scientist is the highly privileged job who oversees the overall functionalities, provides supervision, the focus on futuristic display of information, data.
Data Engineer focuses on the optimization of techniques, building data in the required format and so on.
Data analyst focuses on data cleanup, organizing raw data, visualizing data and to provide technical analysis of data.
The following are some of the important differences between Data Scientist, Data Engineer, and Data Analyst.
|Sr. No.||Key||Data Scientist||Data Engineer||Data Analyst|
|1||Focus||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.|
|2||Roles||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.|
|3||Skills needed||Big Data: R, Python, SAS, Pig, Apache Spark, Database: Hadoop, SQL, Programing: Java, Perl.||Big Data: R, Python, SAS, SAS Miner.||Big Data: Pig, Database: Hive, Hadoop, MapReduce.|
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