Data wrangling is the unification and data cleaning process of complex datasets to ensure a high data quality analysis. The goals of data preparation and data wrangling...
Data warehousing is a vital tool for when data from multiple sources has been collected, to structure, compare and process data for improved business intelligence. Data...
Once data collection is complete, the next step is data discovery. Data discovery is the process of discerning datasets that will be used for data analysis and/or data i...
Data preparation is essential to the data science process. It ensures that quality data is sourced and only the most valuable and defined insights are retrieved. It is a...
The use of data science is beneficial to making informed business decisions and projections using predictive analytics, prescriptive analytics as well as machine learnin...
Once data has been collected, it must be processed to ensure that only the most relevant and highest quality data is available for analysis. If poor or irrelevant data i...
Data preparation is the process of transforming raw data to extract value and ensure data quality. This mandatory step of preprocessing ensures that data is reformatted,...
Data cleansing (otherwise known as data cleaning) is the process of deleting duplicates, unifying data formats and removing irrelevant, inaccurate or corrupt Data. The a...
