Data Cleaning and Preparation Flashcards
6 cards from real DA practice questions. Tap to flip, then mark Knew It or Still Learning — missed cards come back until you master them.
Read the first 6 Data Cleaning and Preparation flashcards as text
What is data parsing in the context of data preparation?
Answer: Extracting structured information from raw or unstructured text
Parsing breaks down raw data (like strings or JSON) into structured, usable components for analysis.
Which technique rescales data so the mean is 0 and standard deviation is 1?
Answer: Standardization (Z-score scaling)
Standardization subtracts the mean and divides by the standard deviation, centering the data at zero with unit variance.
What is the purpose of data profiling?
Answer: Assessing data quality, structure, and content before analysis
Data profiling examines datasets to understand their structure, completeness, distributions, and anomalies before analysis begins.
What is binning (bucketing) in data preparation?
Answer: Grouping continuous values into discrete categories or intervals
Binning converts continuous numeric values into categorical bins, which can reduce noise and simplify analysis.
What is a regex (regular expression) commonly used for in data cleaning?
Answer: Pattern-based text extraction, validation, and replacement
Regular expressions define patterns that match character sequences, enabling extraction, validation, or replacement of text in data fields.
What does 'tidy data' mean as defined by Hadley Wickham?
Answer: Each variable is a column, each observation is a row, and each type of observational unit is a table
Tidy data is structured so that each column is a variable, each row is an observation, and each table holds one observational unit, making it easy to work with.