Role of data Scientist

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 Data scientists are responsible for extracting data from various unstructured sources such as log data, SNMP (Simple Network Management Protocol), Syslog, telemetry, and other system outputs. This involves understanding the sources of data, their relevance, and the technical mechanisms for data extraction. 

 Given the unstructured nature of the data, significant effort is required to clean and transform this data into a usable format. Data scientists develop algorithms to parse, clean, and normalize data, handling inconsistencies, missing values, and anomalies. This step is crucial for ensuring the quality of data fed into ML models.

 Data scientists create and select appropriate features from the raw data. This involves understanding the domain-specific needs of predictive analytics, such as network performance and fault diagnosis, and transforming raw data into features that can effectively predict outcomes. 

After processing, data needs to be loaded into a data warehouse or analytics platform for further analysis. Data scientists ensure that the data is loaded efficiently and is accessible for complex queries and ML models.

 They also refine ETL processes over time, adapting to changes in data structures, evolving network technologies, and business needs.