Final answer:
Data Understanding and Data Preparation are two important phases in the CRISP-DM process model for data analysis. Data Understanding involves familiarizing with the collected data, while Data Preparation involves transforming the data to make it suitable for analysis. Tools like statistical software and visualization tools can be used for Data Understanding, while data cleaning software and data integration tools can be used for Data Preparation.
Step-by-step explanation:
Data Understanding
Data Understanding is the phase in the CRISP-DM (Cross-Industry Standard Process for Data Mining) model where we get familiar with the data we have collected. This includes getting to know the data structure, quality, and the relationships between different variables. The goal is to gain insights into the data and understand its potential for analysis.
Data Preparation
Data Preparation is the phase where we transform the data into a format that is suitable for analysis. This includes tasks such as cleaning the data, handling missing values, dealing with outliers, and selecting relevant variables. The goal is to ensure that the data is of high quality and ready for processing.
There are several tools that can be used in these phases. For data understanding, tools like statistical software (such as R or Python) and visualization tools (such as Tableau or Power BI) can be used. These tools provide functionalities to explore and analyze the data, understand its patterns and relationships, and gain insights from it. For data preparation, tools like data cleaning software (such as OpenRefine or Trifacta) and data integration tools (such as Informatica or Talend) can be used. These tools automate and streamline the data preparation process, making it more efficient and reliable.