Data Science and Design Thinking
Mosaic writes an in-depth view on the synergies between design thinking & data science. We also examine how these two disciplines can be used together.
Mosaic Data Science helps firms develop a data science strategy, which includes a comprehensive plan to leverage data analytics to inform decision-making across the business. Firms that work with us have a clear path forward on how to successfully execute data analytics and incorporate it with existing infrastructure. Mosaic draws upon its deep expertise, maintaining an objective view of the enterprise landscape, to deliver a powerful engagement with strong ROI.
Mosaic writes an in-depth view on the synergies between design thinking & data science. We also examine how these two disciplines can be used together.
We designed and delivered a custom, extensive data science assessment to help this energy firm modernize.
This whitepaper reviews an approach for applying machine learning and predictive analytics in a B2B sales & marketing environment.
Applying statistical inference is a great way for businesses to get more value out of there email marketing programs.
Mosaic built and delivered a custom data science training program for a leading oil & gas firm.
Indexes have two main purposes in relational databases. First, they can improve query performance. Second, they can implement data-integrity constraints.
Data scientists are a scarce commodity, and are likely to remain so for years to come. At the same time, data science can create a substantial competitive advantage for early adopters who make the best use of their scarce data-science resources.
In this post we explain why the assumption about industry experience is outdated—why often industry experience detracts from the best possible application of data science.
Variable selection is perhaps the most challenging activity in the data science lifecycle. Our blog highlights a repeatable approach to variable engineering.
Most data science algorithms do not tolerate nulls (missing values). So, one must do something to eliminate them, before or while analyzing a data set.