Master Comptia-Data+ Data Data acquisition and preparation with our interactive study cards designed for effective learning. These flashcards use proven spaced repetition techniques to help you memorize key concepts, definitions, and facts. Perfect for students, professionals, and lifelong learners seeking to improve knowledge retention and ace exams through active recall practice.
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Process of collecting and importing data from various sources for analysis
Databases APIs web scraping surveys files and IoT sensors
Structured repository storing organized data accessible through queries
Application Programming Interface allowing systems to communicate and exchange data
Extracting data from websites using automated tools
Simple data files like CSV or text files with data in rows and columns
Continuous flow of data generated in real-time from sources
Social media feeds sensor data and transaction logs
Collecting and processing data in large groups at scheduled intervals
Processing data immediately as it is generated or received
Extract Transform Load - process of moving data from sources to destination
Pulling data from various source systems
Converting data into desired format and structure
Inserting transformed data into target system or database
Extract Load Transform - loading data first then transforming in target system
ETL transforms before loading ELT loads then transforms in destination
Combining data from multiple sources into unified view
Merging data from different sources into single location
Process of importing data into system for storage or processing
Structure defining how data is organized in database
Defining structure before loading data (traditional databases)
Defining structure when reading data (data lakes)
Process of cleaning transforming and preparing data for analysis
Another term for data wrangling - manipulating raw data into usable format
Identifying and correcting errors inconsistencies and inaccuracies in data
Missing values duplicates inconsistent formats and outliers
Strategies for dealing with incomplete data records
Deletion imputation or using algorithms that handle missing data
Filling in missing values using statistical methods or estimates
Records that appear multiple times in dataset
Identify and remove duplicates keeping only unique records
Converting data to common format or scale
Scaling numeric data to specific range typically 0 to 1
Converting data from one format structure or type to another
Aggregation filtering sorting merging and pivoting
Combining multiple data points into summary statistics
Analyzing and breaking down data into components for processing
Checking data meets quality standards and business rules
Constraints ensuring data accuracy and consistency
Changing data from one type to another (string to integer date to text)
Combining multiple data fields into single field
Dividing single field into multiple separate fields
Enhancing data by adding information from external sources
Selecting subset of data from larger dataset for analysis
Random sampling stratified sampling and systematic sampling
Creating new variables from existing data to improve analysis
Grouping continuous data into discrete categories or ranges
Converting categorical variables into numeric format for analysis
Creating binary columns for each category in categorical variable
Process of identifying and removing duplicate records from dataset
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