Data acquisition and preparation is the first crucial step in the data science lifecycle. It involves collecting and organizing the data that will be used for analysis and modeling. This stage sets the foundation for the rest of the data science process.

Data Sources: There are various sources from which data can be acquired - public datasets, company databases, APIs, web scraping, or even manual data entry. It's important to ensure that the data is relevant and of high quality for accurate and reliable analysis.

Data Cleaning: Raw data often contains errors, inconsistencies, missing values, or outliers. Data cleaning involves identifying and handling these issues to ensure the data is accurate and suitable for analysis. This may require techniques such as removing duplicates, imputing missing values, and normalizing data.

Data Quality: Data quality is crucial as it directly impacts the outcomes of the data science process. It's essential to assess the quality of the data by checking for completeness, consistency, validity, and reliability. Poor data quality can lead to biased or incorrect results.

Tags: ['data acquisition', 'data preparation', 'data quality']