Analytics Vocabulary

 

General Analytics Terms:

• Algorithm: A set of rules or instructions used by a computer to solve a problem or perform a task. Essential in many analytical processes.
• Data: Raw, unorganized facts that need to be processed to be meaningful. The foundation of all analytics.
• Information: Data that has been processed, organized, and given context, making it useful for decision-making.
• Metric: A quantifiable measure used to track and assess performance, progress, or other relevant aspects. (e.g., website traffic, sales revenue).
• Key Performance Indicator (KPI): A specific type of metric used to evaluate the success of an organization, project, etc., in achieving its objectives.
• Data Mining: The process of discovering patterns, trends, and other useful information from large datasets.
• Data Visualization: The graphical representation of data to make it easier to understand and interpret. (e.g., charts, graphs, maps).
• Dashboard: A visual display of key metrics and KPIs, often used to monitor performance in real-time.
• Report: A structured presentation of data and analysis, often used to communicate findings and insights.
• Insight: A meaningful interpretation or understanding derived from data analysis.
• Predictive Analytics: Using data and statistical techniques to forecast future outcomes.
• Prescriptive Analytics: Going beyond prediction to recommend actions that can optimize outcomes.
• Descriptive Analytics: Summarizing and describing past data to understand what happened.
• Diagnostic Analytics: Investigating why something happened by analyzing data to identify the root cause.
• Big Data: Extremely large and complex datasets that require specialized tools and techniques for analysis.
• Data Analytics: The overall process of examining raw data to draw useful conclusions.
• Business Intelligence (BI): Using data and analytics to improve business decision-making.

Statistical Terms:

• Mean: The average of a set of numbers.
• Median: The middle value in a sorted set of numbers.
• Mode: The most frequent value in a set of numbers.
• Standard Deviation: A measure of how spread out a set of data is.
• Variance: The square of the standard deviation.
• Correlation: A statistical measure that expresses the extent to which two variables are related.
• Regression: A statistical technique used to model the relationship between variables and make predictions.
• Hypothesis Testing: A statistical method used to test a claim about a population based on sample data.
• Statistical Significance: A measure of the likelihood that a result is not due to chance.

Machine Learning Terms (a subset of analytics):

• Machine Learning: A type of artificial intelligence that allows computers to learn from data without being explicitly programmed.
• Supervised Learning: A type of machine learning where the algorithm learns from labeled data (input-output pairs).
• Unsupervised Learning: A type of machine learning where the algorithm learns from unlabeled data, identifying patterns and structures.
• Reinforcement Learning: A type of machine learning where an agent learns to interact with an environment by receiving rewards and penalties.
• Model: A mathematical representation of a real-world phenomenon, used in machine learning and other analytical techniques.
• Training Data: The data used to train a machine learning model.
• Test Data: The data used to evaluate the performance of a machine learning model.
• Overfitting: When a model learns the training data too well, including noise, and performs poorly on new data.
• Underfitting: When a model is too simple to capture the underlying patterns in the data.

Other Important Terms:

• Data Warehouse: A central repository for storing and managing data from multiple sources.
• Data Lake: A repository for storing raw data in its native format, often used for big data analytics.
• Data Governance: The process of managing the availability, usability, integrity, and security of data.

This is not an exhaustive list, but it covers many of the fundamental terms you'll encounter in the world of analytics. The specific vocabulary you need will depend on the type of analytics you're working with. As you delve deeper into specific areas, you'll encounter more specialized terminology.

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By Jerry Ramonyai