Business Knowledge Series course
This two-day masterclass teaches you how to build powerful data marts for analytical modeling and data science in an efficient way. You learn about the ecosystem for analytic data preparation and the role of the data scientist in this environment. The most commonly used analytic data structures and their adequacy for certain analytic business questions are discussed. You receive guidelines for how to approach the creation of important derived variables to increase the predictive power of your models. The topic “Data Quality” is discussed from an analytical viewpoint. Relevant data quality criteria for analytics are discussed and methods are shown how the quality status of the data can be profiled and improved with analytical methods. As not all data quality problems can be corrected, results of simulations studies that quantify the consequences of poor data quality, are shown. This allows a better decision whether to proceed with inferior data or not.
Learn how to
describe in detail the business environment in which data preparation is included identify the relevant data sources based on the business questions better understand the importance of data preparation and data quality for data science structure your data in an analytic data mart define the relevant data quality criteria for your data and how to profile data quality create derived variables from different tables and table relationships consider characteristics of predictive modeling
Formats available | Standard duration | | |
Classroom: |
2 days | | |
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A basic understanding of statistical analysis, eventually also have experience in the context of data mining, statistics or forecasting. A basic understanding of data tables with rows and columns is expected. Programming skills in SAS or another statistical programming language may be helpful not mandatory.
