gegevensverkenning

Gegevensverkenning, also known as exploratory data analysis (EDA), is a critical phase in the data science workflow. It involves an initial investigation of data to discover patterns, spot anomalies, test hypotheses, and check assumptions. The primary goal of gegevensverkenning is to understand the data's characteristics before applying more formal statistical modeling or machine learning techniques.

During this process, analysts use a combination of statistical summaries and graphical representations. Common statistical summaries include measures of central tendency (mean, median), dispersion (variance, standard deviation), and frequency distributions. Visualizations such as histograms, box plots, scatter plots, and bar charts play a vital role in revealing underlying structures, relationships between variables, and potential outliers.

Gegevensverkenning helps in identifying data quality issues like missing values, inconsistencies, or errors, which can then be addressed. It also aids in feature selection by highlighting relevant variables and understanding their predictive power. By gaining a deep understanding of the data's nuances, data scientists can make more informed decisions about subsequent analysis and modeling steps, ultimately leading to more accurate and reliable results. It is an iterative process, often leading back to data cleaning or collection if new insights warrant it.