May 10, 2022
Financially, poor data quality costs organizations some ludicrous amounts of money. Worse, poor data quality is a strong inhibitor to the success of data science: No analytical method can create value from poor quality data. As a consequence, data science projects invest a majority of their resources on cleansing data. However, cleansing resists automation as […]
Read moreJune 26, 2020
Data visualization and analytics are nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can […]
Read moreMarch 20, 2020
Data visualization and analytics are nowadays one of the cornerstones of Data Science, turning the abundance of Big Data being produced through modern systems into actionable knowledge. Indeed, the Big Data era has realized the availability of voluminous datasets that are dynamic, noisy and heterogeneous in nature. Transforming a data-curious user into someone who can […]
Read moreApril 18, 2018
When dealing with real-world data, dirty data is the norm rather than the exception. We continuously need to predict correct values, impute missing ones, and find links between various data artefacts such as schemas and records. We need to stop treating data cleaning as a piecemeal exercise (resolving different types of errors in isolation), and […]
Read moreMarch 22, 2018
We are witnessing data of unprecedented volume, variety and velocity. Such data is collected from almost every aspect of human activity and stored in large repositories in order to be later analyzed and turned into useful insights. The storage model is not any more the one in which data is placed in predefined structures with […]
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