CompTIA Data+ (DA0-002)

A bar chart that displays multiple sets of bars, grouped together for each category, allowing for direct comparison of sub-categories.

• Compares multiple discrete categories.
• Effective for showing performance across different groups.
• Each group has its own set of bars representing sub-categories.

Techniques used to manage the impact of extreme data points (outliers) on statistical analysis, either by transforming their values (winsorization) or by changing the data's scale (normalization).

• Winsorization caps outliers at a specified percentile.
• Normalization (e.g., log transform) can reduce skewness caused by outliers.
• Aims to preserve data while reducing the distorting effect of extremes.

A large, centralized repository of integrated data from various disparate sources, optimized for analytical querying and reporting.

• Stores historical and aggregated data, not real-time operational data.
• Data is cleaned, transformed, and loaded (ETL) into a predefined schema (schema-on-write).
• Used for business intelligence, trend analysis, and strategic decision-making.

A security technique that obscures sensitive data in real time as it is queried, based on the user's role or privilege level, without modifying the underlying stored data.

• Applied at query/view time, not to the stored data itself
• Different users can see different masked/unmasked views of the same record
• Contrasts with static masking, which permanently alters a data copy (e.g., for test/dev)

Graphical elements like color, size, or icons used to highlight data points that meet or exceed predefined thresholds, indicating immediate status or action.

• Enables rapid scanning and comprehension.
• Draws attention to critical data points.
• Supports quick decision-making in dynamic environments.

A visual design technique that applies specific formatting (e.g., color, icons, font styles) to data points in a visualization when certain conditions or rules are met.

• Draws immediate attention to critical data points or outliers.
• Helps users quickly identify trends, patterns, or anomalies.
• Commonly used to highlight values above/below thresholds, or within specific ranges.

Data imputation is the process of replacing missing data with substituted values. The goal is to fill in gaps in a dataset to maintain data integrity and enable complete analysis.

• Common methods include mean, median, mode, or predictive imputation.
• Helps maintain the dataset size and statistical power.
• Choice of method depends on the nature of missing data and variable distribution.

A set of software design patterns used to determine and track the data that has changed within a database since the last time data was extracted.

• Enables incremental loading, significantly reducing data transfer volume and processing time.
• Typically works by monitoring database transaction logs, using timestamps, or trigger-based mechanisms.
• Essential for real-time or near real-time data synchronization in ETL/ELT pipelines.

A plain-text file format that stores tabular data in rows and columns, where each column value is separated by a comma.

• Human-readable and simple to parse.
• Often used for exchanging data between different applications.
• Each line typically represents a data record, and values within a record are separated by a delimiter (usually a comma).

The median is a measure of central tendency that is resistant to the influence of outliers and extreme values, making it a preferred statistic for skewed distributions.

• Represents the middle value in an ordered dataset.
• Not affected by the magnitude of extreme values, only their count.
• Provides a more accurate 'typical' value for skewed data compared to the mean.

A type of SQL join that returns all records from the left table (first table in the FROM clause) and the matching records from the right table. If there is no match, the right side will contain NULL values.

• Used to find records in one table that do or do not have corresponding records in another.
• Preserves all rows from the 'left' table.
• Often combined with `WHERE right_table.column IS NULL` to find unmatched records.

The use of graphical elements like distinct colors, shapes, or icons to convey information, highlight important data, or draw attention to specific states or thresholds.

• Provides immediate, non-textual communication.
• Effectively highlights critical information or exceptions.
• Should be used consistently and sparingly to avoid visual clutter.

The process of breaking down a complex string or text field into multiple, structured components based on defined patterns, delimiters, or rules.

• Used to extract meaningful information from unstructured or semi-structured data.
• Commonly applied to address fields, log files, or free-form text.
• Often involves regular expressions or specific parsing functions.

A design principle ensuring that similar visual elements (colors, fonts, labels, spacing) are treated in the same way across a visualization or dashboard, and that elements are neatly arranged.

• Reduces cognitive load for users.
• Improves readability and ease of understanding.
• Applies to colors, fonts, terminology, layout, and spacing.

A type of NoSQL database that stores data in flexible, semi-structured document formats (typically JSON, BSON, or XML), allowing for dynamic schemas and easy handling of hierarchical data.

• Stores data as 'documents' (e.g., JSON objects).
• Flexible schema (schema-on-read).
• Ideal for semi-structured and unstructured data.

A predefined rule in master data management that determines which value 'survives' into the golden record when multiple source systems have conflicting data for the same attribute.

• Common rules: most recent update, most trusted source, most complete value
• Applied during the merge/match process when building golden records
• Different from lineage, which just tracks data's origin/history

A data summarization tool used to rearrange and aggregate data by one or more keys or dimensions, allowing for interactive exploration and analysis of large datasets.

• Summarizes data from a larger table.
• Allows for dynamic rearrangement (pivoting) of rows and columns.
• Excellent for cross-tabulation and identifying patterns in categorical data.

The Data Owner is the accountable business role deciding classification, access, and retention; the Data Custodian implements the technical controls (storage, backup, encryption) that enforce those decisions.

• Owner = business accountability and decision-making authority
• Custodian = technical implementation of security/storage controls
• Steward = day-to-day data quality rule definition and enforcement

A type of database that stores and provides access to data points that are related to one another. Data is organized into tables (relations) with predefined schemas.

• Uses SQL for data manipulation and querying.
• Enforces ACID properties (Atomicity, Consistency, Isolation, Durability).
• Ideal for structured data and transactional applications.

The long-term, underlying movement or general direction (upward, downward, or stable) of a time series over an extended period, often spanning several years.

• Represents the dominant long-term pattern.
• Not influenced by short-term fluctuations like seasonality or random noise.
• Can be linear or non-linear.

A predictable and recurrent pattern in time series data that repeats over a fixed period, such as within a year, month, or week, often influenced by calendar events.

• Repeats at fixed intervals (e.g., yearly, monthly).
• Predictable and consistent.
• Often driven by calendar events like holidays or weather.

A design principle where essential information is presented first, and more detailed or advanced information is revealed only when explicitly requested by the user.

• Reduces information overload for new or general users.
• Allows experienced users to access depth when needed.
• Commonly implemented with drill-down features, expand/collapse sections, or tooltips.

A data type used to store sequences of characters, often representing human language or descriptive information.

• Can include letters, numbers, symbols, and spaces.
• Often used for names, addresses, descriptions, and comments.
• Typically requires more storage than numerical or boolean types.

The process of verifying that a dataset's structure, data types, and constraints conform to a predefined schema or data model.

• Crucial for maintaining data integrity at the point of ingestion.
• Prevents malformed data from entering downstream systems.
• Often uses tools like JSON Schema or XML Schema Definition (XSD).