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Blogs Page 5

  • Data Science for Manufacturing

    Data Science for Manufacturing

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

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  • Debating the Issues with NLP

    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.

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  • NLP: Geeking out with Words

    NLP: Geeking out with Words

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

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  • Word Frequency Models: A Simple Natural Language Processing Technique

    Word Frequency Models: A Simple Natural Language Processing Technique

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

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  • The Taylor Series and Beyond

    The Taylor Series and Beyond

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

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  • Data Architecture 101, Part 5: Indexes

    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.

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  • How to Make the Most of Your Data-Science Dollar

    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.

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  • Data Debt

    Data Debt

    Data debt occurs when data is improperly handled at the technical level with the intention of postponing certain costs, even though the postponed costs will be higher, or the postponed benefits will be lower. The remainder of this document describes some important types of data debt.

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  • Data Architecture 101, Part 4: Ontology-Driven Development is Lean

    Data Architecture 101, Part 4: Ontology-Driven Development is Lean

    In software-development & data architecture nirvana, the business analysts, database technologists, and application developers all speak the same language. Everyone agrees about what each user story means.

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  • Data Architecture 101, Part 3: Dimensions

    Data Architecture 101, Part 3: Dimensions

    Data marts, data warehouses, and some operational datastores use dimension tables. A dimension table categorizes a fact table that joins to the dimension. At query time one filters the facts by values in the dimension table, and uses those values to label the query results

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