Data Preparation and Data Quality for Analytics
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.
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.
The Data Preparation for Analytics - Ecosystem