some_text Business Intelligence for Biological Data Sets | CDS2011


Business Intelligence for Biological Data Sets

Marta Ewertowska

Business Intelligence for biological data sets was supposed give me experience of working with real life, huge amounts of data. I had to learn about proteomics field in order to understand business needs and data. To complete my project properly I have applied CRISP DM methodology. I had to work with data coming from different sources .To store my data I was using SQL Server 2008. I have used C# language to extract and transform data. Most data transformations were done with T-SQL scripts. Crisp DM methodology is different than other software engineering methodologies like Waterfall and Agile however they share some common characteristics.Most time consuming and crucial phase is data preparation. The quality of results will depend a lot on this stage. My project required constant coming back and forth between stages. There were quite a lot of small cycles taking place. Decisions about next tasks were made upon the results. There are many different data mining algorithms. It is important to add some ‘intelligence’. Different algorithms have different advantages and disadvantages. Business Intelligence and data mining by careful analysis make business smarter.

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