Blogs Archives | Page 5 of 7 | Mosaic Data Science

Human Decision Making in Machine Learning Deployment for Air Traffic Flow Management

Being able to accept machine learning outputs in the decision making process is critically important, especially in Air Traffic decisions.

By Drew Clancy, 8 yearsDecember 7, 2016 ago

Blogs

Filling Predictive Modeling Gaps with Anomaly Detection

Anomaly detection is a great technique to use in supervised machine learning and AI applications.

By Drew Clancy, 8 yearsOctober 31, 2016 ago

Blogs

Finding External Data Sources: Data is Everywhere!

Sometimes in ML & AI development, a data scientist will need to go and find external data sources to complement the modeling efforts.

By Drew Clancy, 8 yearsSeptember 28, 2016 ago

Blogs

Data Science for Manufacturing

Manufacturing companies have a large opportunity to apply data science techniques like AI & ML to optimize their business processes.

By Drew Clancy, 8 yearsJuly 18, 2016 ago

Blogs

Debating the Issues with NLP

We examine a practical use of NLP, analyzing the 2016 presidential debate speech transcripts to uncover what issues the candidates discuss most.

By Drew Clancy, 9 yearsMay 9, 2016 ago

Blogs

NLP: Geeking out with Words

NLP is a great AI technique businesses can utilize to take advantage of text data.

By Drew Clancy, 9 yearsFebruary 27, 2016 ago

Blogs

Word Frequency Models: A Simple Natural Language Processing Technique

Word frequency models are a great text mining technique in NLP development.

By Drew Clancy, 9 yearsOctober 12, 2015 ago

Blogs

The Taylor Series and Beyond

We examine an old but popular algorithm technique, the Taylor Series.

By Drew Clancy, 10 yearsApril 2, 2015 ago

Blogs

Data Architecture 101, Part 5: Indexes

Indexes have two main purposes in relational databases. First, they can improve query performance. Second, they can implement data-integrity constraints.

By Drew Clancy, 10 yearsSeptember 5, 2014 ago

Blogs

How to Make the Most of Your Data-Science Dollar

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.

By Drew Clancy, 10 yearsJune 26, 2014 ago

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