CompTIA Data+ (DA0-002) flashcards

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.

Memory trick: Choosing the right data environment is like picking the right vault for your treasures: some are for raw goods, others for organized valuables, and some for high-security transactions.

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.

Memory trick: Bars Grouped, Comparisons Bloomed.

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.

Memory trick: Outliers are odd, so either cut them, cap them, or change their view.

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.

Memory trick: Think of data environments as different rooms in a house: some are for daily tasks, some for long-term storage, and some for deep study.

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)

Memory trick: Dynamic masking = a one-way mirror 🪞 showing different faces depending on who's looking

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.

Memory trick: Real-time's rhythm, quick and bright, guides action, day and night.

A statistical method used to model the linear relationship between a dependent variable and one or more independent variables.

• Predictive analysis.
• Identifies strength and direction of linear association.
• Assumes linearity, independence, homoscedasticity, and normality of residuals.

Memory trick: Regression predicts the 'reg'ular trend.

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.

Memory trick: Conditions trigger formatting, like traffic lights changing color.

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.

Memory trick: Missing? Impute it! Outlier? Fix it! Duplicate? Delete it!

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.

Memory trick: Acquiring data for ETL: CDC is like a smart tracker, only picking up new footprints.

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).

Memory trick: Think of file formats as different ways to package information, like a letter (CSV), a gift box (JSON), or a scroll (XML).

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.

Memory trick: When the data landscape is bumpy with outliers, the Median is the smooth path through the middle.

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.

Memory trick: Joining tables is like connecting puzzle pieces, but sometimes you need all of one side.

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.

Memory trick: Cues Visual, Alerts Crucial.

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.

Memory trick: Transforming data is like a puzzle, parsing pieces into place.

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.

Memory trick: Consistent Design, Clearer Insights.

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.
• Scales horizontally and handles complex, nested data.

Memory trick: Think of 'Doc'ument databases as a digital filing cabinet for flexible, varied 'documents'.

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

Memory trick: Survivorship = the value that WINS the battle to enter the Golden Record 🏆

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.
• Can display counts, sums, averages, or other aggregations.

Memory trick: Pivot Tables PIVOT to show Status and Assignments FAST!

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

Memory trick: Owner holds the KEY 🔑 to decisions; Custodian holds the TOOLBOX 🛠️ to implement them

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.

Memory trick: Secular Trend is like looking at the 'century-long' direction of your data, ignoring the daily ups and downs.

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.

Memory trick: Time's rhythmic dance: Trend, Season, Cycle, Residual Chance.

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.

Memory trick: Progressive Disclosure: Layers of Lore.

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.

Memory trick: Think of data types as different drawers in a filing cabinet, each holding a specific kind of information.

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).

Memory trick: Incoming data: check the map, then check the contents.

A formal agreement or specification that details the expected format, schema, quality, and behavior of data exchanged between systems or teams, especially with external data providers.

• Ensures data consistency and quality at the source.
• Defines data governance and compliance requirements.
• Acts as a foundational document for reliable data pipelines.

Memory trick: Acquiring data: secure the source, define the rules, then flow.

A map visualization where geographical locations are represented by bubbles, and the size and/or color of the bubbles encode quantitative data related to those locations.

• Combines geographical context with quantitative data.
• Bubble size can represent one metric (e.g., volume).
• Bubble color can represent another metric (e.g., intensity or average).

Memory trick: Bubbles on Maps, Metrics Overlaps.

An SQL join type that returns all rows from both the left and right tables, including unmatched rows from either side, with NULL values for the columns of the table that has no match.

• Combines the results of both LEFT JOIN and RIGHT JOIN.
• Used when you need to see all records from both datasets, regardless of a match.
• Can result in a large dataset with many NULL values.

Memory trick: Joins connect, but some are more inclusive than others.

The degree to which each real-world entity is represented only once in a dataset, with no duplicate records.

• Uniqueness rate = (Total records − Duplicates) / Total records
• Duplicate rate = Duplicates / Total records
• Poor uniqueness inflates counts and skews aggregate reporting

Memory trick: 'CACTUS' - Completeness, Accuracy, Consistency, Timeliness, Uniqueness, Validity Standards

The degree to which data conforms to the syntax, format, type, or range defined by business or system rules.

• Checks format, type, and allowable range
• Different from accuracy (validity can be correct format but wrong fact)
• Commonly enforced via validation rules or constraints

Memory trick: 'A CUTV' car: Accuracy, Completeness, Uniqueness, Timeliness, Validity

A design principle that arranges elements to show their order of importance, guiding the viewer's eye through the information in a logical and intuitive way.

• Uses size, color, contrast, and placement to indicate importance.
• Ensures the most critical information is seen first.
• Helps users quickly understand the main message.
• Crucial for dashboards with limited space and multiple metrics.

Memory trick: Hierarchy Guides Your Eye, So Key Info You Won't Miss By!

A powerful SQL feature that allows for advanced pattern matching in string data, enabling complex validation, searching, and extraction based on specific character sequences and structures.

• Uses a defined syntax to specify patterns (e.g., `^`, `$`, `[0-9]`, `[A-Za-z]`, `{n}`).
• Highly effective for validating data formats, parsing text, and identifying specific string structures.
• Supported by many SQL dialects (e.g., `REGEXP_LIKE` in Oracle, `~` operator in PostgreSQL, `REGEXP` in MySQL).

Memory trick: Validating data with SQL is like a detective's work; REGEX is your best magnifying glass for patterns.

A large repository that stores raw data in its native format until it is needed, without an upfront schema or processing.

• Stores raw, unrefined data
• Supports various data formats (structured, semi-structured, unstructured)
• Schema-on-read approach
• Cost-effective storage for large volumes

Memory trick: Lake holds everything, Warehouse organizes, Mart specializes.

A database optimized for storing and retrieving data points that are indexed by time, making them ideal for handling metrics, events, and sensor data over periods.

• Optimized for time-stamped data.
• Efficient for time-range queries and aggregations.
• High write and read throughput for sequential data.
• Commonly used for IoT, monitoring, and financial data.

Memory trick: Remember 'Time-Series' for 'Tick-Tock' data that changes over time.

A bar chart that displays multiple data series on top of each other, where each segment of the bar represents a part of the total, allowing for comparison of composition across different categories.

• Shows part-to-whole relationships.
• Compares the composition of different groups.
• Each bar represents a total, divided into segments by sub-categories.

Memory trick: Stacked Bars Show Parts, Comparing Across Groups with Hearts!

Winsorization is a statistical method of transforming data by limiting extreme values in the dataset to reduce the effect of possibly spurious outliers. Instead of deleting outliers, they are replaced with the nearest value that is not an outlier.

• Reduces the influence of outliers without discarding data.
• Useful when outliers are due to measurement errors rather than true extreme events.
• Typically involves setting values above/below a certain percentile to that percentile's value.

Memory trick: Winsorize to cap, not chop, the extremes!

A chart that displays the contribution of multiple data series to a total over time, with each series stacked on top of the previous one.

• Shows changes in composition over time.
• Emphasizes the total and the proportion of each component.
• Useful for visualizing market share, population demographics, etc.

Memory trick: Areas Stacked, Shares Tracked.

A bar chart that uses two independent Y-axes to display two different quantitative variables, often with different units, against a common X-axis representing categories.

• Enables direct comparison of two distinct metrics within the same visualization.
• Useful when metrics have different scales or units.
• Can sometimes be misleading if not used carefully, due to independent scales.

Memory trick: Dual axes balance two different scales.

A storytelling structure that organizes a narrative around defining a business problem, proposing a data-driven solution, and demonstrating the measurable impact or benefits of that solution.

• Highly effective for communicating complex data to non-technical audiences.
• Focuses on business value and practical outcomes.
• Helps justify investments and drive decision-making.

Memory trick: Executives care about problems, solutions, and impact.

A governance role responsible for the day-to-day management, quality, and definition of data assets on behalf of the data owner.

• Manages data quality rules and standards
• Resolves data issues raised by business users
• Works between the data owner and technical teams

Memory trick: Owner decides, Steward manages, Custodian stores, User consumes.

A graphical representation that displays the distribution of a numerical data set, showing the frequency of data points within specified ranges (bins).

• Used for continuous numerical data.
• Shows the shape, spread, and central tendency of data.
• Bars represent frequency or count within a bin.

Memory trick: Discover data's story, pick the chart that fits its glory.

The median is the most appropriate measure of central tendency for ordinal data, as it represents the middle value in an ordered dataset and does not assume equal intervals between categories.

• Suitable for data with a natural order but unequal intervals.
• Less sensitive to outliers than the mean.
• Represents the 50th percentile of the data.

Memory trick: Ordinal data is like a ranked list, and the Median is the perfect middle of that list.

A level of measurement that classifies data into distinct categories where there is no particular order or ranking among the categories.

• Represents qualitative data (labels, names, types).
• Cannot be ordered or measured numerically.
• Examples include gender, country of origin, hair color, marital status.

Memory trick: Remember 'NOIR' for Nominal, Ordinal, Interval, Ratio – like a staircase of increasing data power.

An MDM record-matching technique that calculates similarity scores across multiple attributes to estimate the likelihood that records represent the same real-world entity.

• Used when no reliable unique identifier exists
• Contrasts with deterministic (exact-match) matching
• Often uses weighted scoring algorithms like Fellegi-Sunter

Memory trick: Profile, Standardize, Deterministic first, Probabilistic if fuzzy, Survive, Golden

In the ETL process, the Transformation stage involves applying a set of rules or functions to the extracted data to prepare it for loading into the target data warehouse. This includes cleansing, standardization, aggregation, and schema mapping.

• Key for data quality and consistency.
• Can be resource-intensive depending on complexity.
• Ensures data conforms to the target system's requirements.

Memory trick: Extract, Transform, Load: Get it, Fix it, Put it!

A parametric statistical hypothesis test used to determine if the mean of a single sample is significantly different from a known or hypothesized population mean.

• Compares one sample mean to a fixed value.
• Assumes the sample data is normally distributed (or large enough for CLT).
• Requires interval or ratio level data.

Memory trick: One-sample t-test: You're checking if your single sample's average hits the target.

A directive that suspends normal data retention and destruction schedules to preserve information relevant to pending or anticipated litigation, audits, or investigations.

• Overrides standard retention policy timelines
• Issued typically by legal counsel
• Ends when litigation or investigation concludes

Memory trick: Litigation freezes the retention clock like a courtroom 'HOLD' sign.

The degree to which the same data value matches across multiple systems, records, or points in time.

• Common cause of conflicting golden record values across CRM/ERP
• Different from accuracy, which measures correctness against reality
• Resolved through data integration and MDM survivorship rules

Memory trick: 'CACTUS' - Completeness, Accuracy, Consistency, Timeliness, Uniqueness, Validity Standards

A privacy principle requiring organizations to collect and retain only the personal data necessary for a specific, defined purpose.

• Core principle under GDPR Article 5
• Reduces breach impact and compliance risk
• Applies at collection, not just storage or masking

Memory trick: LAMPS-IA: Lawful, Aim (purpose), Minimal, Precise, Stored briefly, Integrity, Accountable

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

• Used when you want to ensure all records from a specific table (the 'right' one) are included in the result.
• Useful for finding records in one table that do or do not have corresponding records in another.
• Can often be rewritten as a LEFT JOIN by swapping table order.

Memory trick: Joining tables: RIGHT JOIN means 'I want all of your products, and any purchases that match'.

A dashboard element that prominently displays a single, critical metric (KPI) as a numerical value, often with additional context like a trend, target, or comparison.

• Highlights essential business metrics.
• Provides immediate status at a glance.
• Often includes a large number and concise label.

Memory trick: KPI Tiles, Metrics Shine.

SQL functions used to manipulate the case of characters within a string, such as converting to uppercase, lowercase, or proper (initial capital) case.

• UPPER() converts all characters to uppercase.
• LOWER() converts all characters to lowercase.
• INITCAP() (or PROPER()) capitalizes the first letter of each word.

Memory trick: Strings can be shaped, like clay, with SQL's fine hand.

A single, authoritative version of a data entity created by reconciling and merging duplicate or conflicting records from multiple source systems using survivorship rules.

• Core output of master data management (MDM)
• Created via matching and merging duplicate records
• Survivorship rules determine which source value 'wins'

Memory trick: MDM builds the 'golden' trophy record from many silver duplicates.

A statistical technique for analyzing data points collected over a period of time to identify trends, seasonality, and other patterns.

• Data points are ordered chronologically.
• Used for forecasting and understanding historical patterns.
• Components include trend, seasonality, cyclical, and irregular variations.

Memory trick: Time's Arrow points the way for trends.

A tabular visualization where the cells are colored based on the value they contain, using a color gradient to represent magnitude, often used for large datasets to quickly identify patterns or outliers.

• Combines numerical data with visual color cues.
• Excellent for identifying patterns or anomalies in large tables.
• Reduces cognitive load by leveraging pre-attentive processing.

Memory trick: Heatmaps Glow, Outliers Show.

A graph that displays the values for two different numerical variables as points on a Cartesian coordinate system, used to observe the relationship between them.

• Shows correlation or relationship between two variables.
• Identifies patterns, trends, and outliers.
• Each point represents a single data observation.

Memory trick: Two variables meet, their dance to show, a scatter plot helps the insights flow.

A data quality dimension that refers to the extent to which data conforms to defined formats, types, or business rules.

• Ensures data is in the correct format (e.g., numbers are numerical, dates are date types).
• Prevents incorrect or nonsensical entries (e.g., age cannot be negative).
• Crucial for data processing, analysis, and system interoperability.

Memory trick: Data quality is like a diamond's facets: each one (accuracy, completeness, validity, etc.) contributes to its overall brilliance and value.

A data preprocessing technique used to standardize or normalize the range of independent variables (features) within a dataset. This prevents features with larger values from dominating the model's learning process.

• Standardizes or normalizes feature ranges.
• Important for algorithms sensitive to feature magnitudes (e.g., SVM, K-Means, Neural Networks).
• Methods include Min-Max scaling (normalization) and Standardization (Z-score scaling).

Memory trick: Clean, Scale, Transform, Encode: Data's ready to explode!

In classification, precision is the ratio of true positive predictions to the total number of positive predictions (True Positives + False Positives). It answers: 'Of all items predicted as positive, how many were actually positive?'

• Focuses on the correctness of positive predictions.
• Calculated as True Positives / (True Positives + False Positives).
• High precision means fewer false positive errors.

Memory trick: PR-ecision: P-ositive R-eports are Correct.

A design philosophy emphasizing simplicity and the removal of non-essential elements to improve clarity and focus on core information.

• Reduces cognitive load.
• Enhances readability and comprehension.
• Prioritizes essential data and visuals.

Memory trick: Dashboard's power, clear and bright, guides decisions, day and night.