Generally, we hear different designations about CS Engineers like Data Scientist, Data Analyst and Data Engineer. Let us discuss the differences between the above three roles.
- Data Analyst – The main focus of this person’s job would be on optimization of scenarios, say how an employee can improve the company’s product growth. Data Cleaning and organizing of raw data, analyzing and visualization of data to interpret the analysis and to present the technical analysis of data.
Skills needed for Data Analyst are R, Python, SQL, SAS, SAS Miner.
- Data Scientist – The predominant focus will be on the futuristic display of data. They provide both supervised and unsupervised learning of data, say classification and regression of data, Neural networks. The continuous regression analysis would be using machine learning techniques.
Skills needed for Data Scientist are R, Python, SQL, SAS, Pig, Apache Spark, Hadoop, Java, Perl.
- Data Engineer – Data Engineers concentrate more on optimization techniques and building of data in a proper manner. The main aim of a data engineer is continuously improving the data consumption. Mainly a data engineer works at the back end. Optimized machine learning algorithms were used for maintaining data and to make data to be available in most accurate manner.
Skills needed for Data Engineer are Pig, Hive, Hadoop, MapReduce techniques.
- Difference between a Data Analyst and a Data Scientist
- Difference between data type and data structure
- Difference between Data Warehouse and Data Mart
- Comparisons between Data Warehousing v/s Data Mining
- Data Mining | Sources of Data that can be mined
- Difference between Stack and Queue Data Structures
- Difference Between High-level Data Link Control (HDLC) and Point-to-Point Protocol (PPP)
- Difference between Structured, Semi-structured and Unstructured data
- 5 V's of Big Data
- Youtube Data API | Set-2
- What is Structured Data?
- HTML | <data> Tag
- Youtube Data API | Set-1
- Big Challenges with Big Data
- Benefits of Big Data
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