Data catalog case study

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The data science case study is often the most difficult part of the hiring process. Often, at this point in the process, prospects have already demonstrated sufficient technical understanding and skills for the position, so this is no longer a question of whether or not they can perform job duties. Rather, due to all the ambiguities, candidates will need to demonstrate decisiveness in their investigations, as well as a capacity to consider impacts and topics from a variety of angles. Perhaps even more importantly, the ability to effectively communicate conclusions will be heavily highlighted in data science case study problems. There are three main types of data science case studies: product questions, modeling and machine learning questions, and business case questions. This type of case study tackles a specific product or feature, often tied to the interviewing company.

Which data catalog example use case is most relevant for you?

Data Catalog Use Cases & Examples |

Enterprises must be aware of the data sources in-house to use the data for analytics and reporting purposes. It is very important that the data within an enterprise adds value, is of good quality, is traceable and is accessible based on the need. All this boils down to the necessity of data governance for organizations - a key component of which is Data Catalog. I have worked for a couple of years with data catalogs from Informatica, Alteryx and Microsoft and I have learned that development team is faced with some important challenges during implementation of data catalogs.

A Catalog of Civic Data Use Cases

Global transport and logistics leader Maersk delivers organization-wide Azure cost optimization. Indonesian industrial equipment retailer, Kawan Lama, modernizes its workplace using Microsoft Microsoft's business intelligence upgrades boost order growth for Extend Forming Industrial Corp. AIA Thailand seeks to become the best digital provider insurer with the help of Microsoft technologies. Major Russian university ushers in era of hybrid learning, driving up attendance.
Improve the accuracy of your machine learning models with publicly available datasets. Save time on data discovery and preparation by using curated datasets that are ready to use in machine learning workflows and easy to access from Azure services. Account for real-world factors that can impact business outcomes. By incorporating features from curated datasets into your machine learning models, improve the accuracy of predictions and reduce data preparation time. Nominate datasets to help solve real-world challenges, promote collaboration and machine learning research, and advance global causes.
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