Hello and Welcome from 360DigiTMG. This is a video playlist to explain the life cycle of a data science project. In the previous video we talked about Business Understanding. Now let us discuss about the next phase in the data science life cycle – Data Understanding.


Data Understanding: Now that we have a good understanding of the business problem that needs to be solved the data scientist attempts to find out the available data. In the above example, increase the sales of a particular product. The data scientist will identify the data sources – what data is available, where and how can we obtain it. It is recommended that the data scientist keep a catalog of all the data sources so that it can be easily reproduced when required. The data scientist will then proceed to acquire the datasets, interacting with various databases and file storage systems. Typically, this module requires that the data scientist have some experience with relational database systems such as Oracle, SQL Server, MySQL and non-relational databases such as Hadoop, Cassandra, MongodB and finally cloud storages such as AWS S3 (simplified storage service)

Some of the best practices during data collection:

Detail the various sources and steps needed to extract data
Confirm the availability of the data, both quantity and quality
Comprehensively understand your data before preparing it for downstream consumption
Define data governance: who owns the data, who has access, the appropriate usage of the data, and the ability to access and delete specific pieces of data on demand
Track data lineage, so that the location and data source is tracked and known during further processing

That concludes Data Understanding video. In the next video we will talk about the next phase in the Data Science Life Cycle – Data Preparation.



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