Data Analytics Cheat Sheet

The core ideas of Data Analytics distilled into a single, scannable reference — perfect for review or quick lookup.

PiqCue — piqcue.com/data-analytics/cheatsheet

Quick Reference

Descriptive Analytics

The foundational tier of analytics that summarizes historical data to answer the question 'what happened.' It uses aggregation, data visualization, and reporting to present past trends and key performance indicators in an understandable form.

Predictive Analytics

The use of statistical models, machine learning algorithms, and historical data to forecast future outcomes. It identifies patterns in past behavior to estimate probabilities and trends that have not yet occurred.

Prescriptive Analytics

The most advanced tier of analytics, which recommends specific actions by combining predictive models with optimization and simulation techniques. It answers the question 'what should we do' by evaluating trade-offs among possible decisions.

ETL (Extract, Transform, Load)

A data integration process that extracts data from source systems, transforms it into a consistent format through cleaning and enrichment, and loads it into a target data warehouse or data lake. ETL pipelines are essential for making raw data analysis-ready.

Data Visualization

The graphical representation of data and information using charts, maps, dashboards, and other visual elements. Effective visualization makes complex datasets accessible to non-technical audiences and helps reveal patterns that are difficult to detect in raw numbers.

Statistical Significance

A measure of whether an observed result is unlikely to have occurred by chance alone, typically evaluated using a p-value threshold (often 0.05). It helps analysts distinguish genuine effects from random variation in data.

Data Warehousing

The practice of collecting and managing data from multiple sources into a centralized repository optimized for analytical querying and reporting. Data warehouses use schemas designed for read-heavy workloads rather than transactional processing.

Regression Analysis

A set of statistical methods for estimating the relationships between a dependent variable and one or more independent variables. It is widely used for prediction, forecasting, and understanding which factors most influence an outcome.

A/B Testing

A controlled experiment in which two variants (A and B) are compared to determine which performs better on a defined metric. Participants are randomly assigned to each group to isolate the effect of a single change.

Data Governance

The framework of policies, processes, roles, and standards that ensures data is managed consistently, securely, and in compliance with regulations across an organization. Good governance addresses data quality, access control, lineage, and privacy.

Key Terms at a Glance

Descriptive Analytics:The tier of analytics that summarizes historical data using aggregation, visualization, and reporting to answer the question 'what happened.'

Predictive Analytics:The use of statistical models and machine learning on historical data to forecast future outcomes and probabilities.

Prescriptive Analytics:The most advanced analytics tier that recommends specific actions by combining predictive models with optimization and simulation techniques.

ETL:Extract, Transform, Load: a data integration process that extracts data from sources, transforms it into a clean and consistent format, and loads it into a target data store.

Data Warehouse:A centralized repository that stores structured, processed data from multiple sources, optimized for analytical querying and reporting using a schema-on-write approach.

Data Lake:A storage system that holds vast amounts of raw data in its native format until needed, supporting schema-on-read and accommodating structured, semi-structured, and unstructured data.

SQL:Structured Query Language: the standard programming language for managing and querying relational databases, widely used for data retrieval, manipulation, and reporting in analytics.

KPI:Key Performance Indicator: a quantifiable metric used to evaluate how effectively an organization or process is achieving its strategic and operational objectives.

Data Visualization:The graphical representation of data through charts, graphs, maps, and dashboards to make complex information accessible and to reveal patterns, trends, and outliers.

Regression Analysis:A statistical method for estimating relationships between a dependent variable and one or more independent variables, used for prediction and understanding variable influence.

A/B Testing:A controlled experiment that compares two variants by randomly assigning subjects to each group, used to determine which version performs better on a defined metric.

Statistical Significance:A determination that an observed result is unlikely to have occurred by chance, typically assessed using a p-value threshold of 0.05.

P-value:The probability of observing results at least as extreme as the data, assuming the null hypothesis is true. Lower p-values provide stronger evidence against the null hypothesis.

Data Governance:The framework of policies, roles, processes, and standards that ensures organizational data is managed consistently, securely, and in compliance with applicable regulations.

Data Lineage:The documentation of data's origins, movements, and transformations throughout its lifecycle within an organization's systems and processes.

Get study tips in your inbox

We'll send you evidence-based study strategies and new cheat sheets as they're published.

We'll notify you about updates. No spam, unsubscribe anytime.