Big Data

Big data encompasses the vast volumes of data generated at high velocity and variety that traditional data processing software cannot handle efficiently. Advanced big data technologies and methodologies allow for the storage, analysis, and utilization of these massive datasets to derive actionable insights.

Business Intelligence (BI)

Business Intelligence (BI) involves using technologies and practices to collect, integrate, analyze, and present business data. BI tools provide historical, current, and predictive views of business operations, empowering organizations to make informed decisions.

Central Tendency

A statistical measure that identifies a single value as that is most representative of an entire distribution/set of data. Descriptors of central tendency are:

• Mean (Average): The sum of all values in a dataset divided by the number of values. It represents the central point of the data. (Formula: Σx / n)
• Median: The middle value in a dataset arranged from least to greatest. Useful for skewed data.

Complexity Science

Complexity science studies systems with many interconnected parts, focusing on how relationships and interactions give rise to collective behaviors and emergent phenomena. This field is applied across disciplines, from biology to social sciences, to understand complex adaptive systems and their dynamics.

Correlation

Correlation is a statistical measure that expresses the extent to which two variables change together at a constant rate.

• Correlation Coefficient: A measure of the strength and direction of the linear relationship between two variables. Ranges from -1 (perfect negative correlation) to +1 (perfect positive correlation).

Data Analytics

Data analytics is the process of examining raw data to uncover patterns, trends, and insights that drive business strategies and decision-making. By transforming data into actionable insights, data analytics helps organizations enhance performance and gain a competitive edge.

Data Engineering

Data engineering involves designing, constructing, and maintaining systems and architecture that enable data collection, storage, and analysis. It focuses on creating data pipelines that transform raw data into usable information, supporting data-driven decision-making processes.

Data Governance

Data governance involves managing the availability, usability, integrity, and security of data within an organization. It ensures data accuracy, consistency, and responsible usage across the enterprise, supporting regulatory compliance and strategic decision-making.

Data Integration

Data integration combines data from different sources into a unified view, enabling comprehensive analysis and informed decision-making. It ensures that disparate data systems harmonize, providing a seamless flow of information across an organization.

Data Lake

A data lake is a centralized storage repository that holds vast amounts of raw data in its native format until needed for analysis. It supports storing structured, semi-structured, and unstructured data, providing flexibility for various analytical approaches.

Data Management

Data management encompasses the practices, architectural techniques, and tools used to achieve consistent access to and delivery of data across an organization. It ensures that data is treated as a valuable resource, enhancing its quality and usability for business processes.

Data Mining

Data mining is the process of discovering patterns, correlations, and anomalies within large data sets using statistical methods, machine learning, and database systems. It uncovers hidden knowledge and insights that can drive strategic business decisions and innovations.

Data Modeling

Data modeling involves creating a conceptual representation of data objects and their relationships, serving as a blueprint for constructing databases or data warehouses. It ensures data is structured effectively, facilitating efficient storage, retrieval, and analysis.

Data Science

Data science is an interdisciplinary field that uses scientific methods, algorithms, and systems to extract knowledge and insights from structured and unstructured data. Combining statistics, computer science, and domain expertise, it solves complex problems and informs data-driven decision-making.

Data Transformation

Data transformation is the process of converting data from one format or structure to another to make it suitable for analysis. This includes normalization, aggregation, and integration steps that prepare data for various analytical applications.

Data Visualization

Data visualization is the graphical representation of data and information using visual elements like charts, graphs, and maps. It helps stakeholders understand complex data sets by highlighting trends, outliers, and patterns in a visually intuitive manner.

Decision Science

Decision science applies analytical and computational methods to support and improve decision-making processes within organizations. Integrating data analysis, modeling, and behavioral science guides strategic and operational decisions, enhancing overall business performance.

Deep Learning

Deep learning is a subset of machine learning that uses neural networks with many layers to model complex patterns in data. It excels in tasks such as image recognition, natural language processing, and autonomous driving, mimicking the human brain's processing capabilities.

Descriptive Analytics

Descriptive analytics involves analyzing historical data to identify trends and patterns, providing insights into past performance. This type of analysis helps organizations understand what has happened over a specific period and informs future strategies.

Distribution

In statistics, the distribution describes the relative numbers of times each possible data value will occur in a data set. Statistical distributions help us understand a problem better by assigning a range of possible values to the variables.

• Normal Distribution (Bell Curve): A symmetrical, bell-shaped distribution where the mean, median, and mode are all equal. In a normal distribution, 68% of all values lie within one standard deviation from the mean. 95% of the values lie within two standard deviations from the mean, and 99.7% lie within three standard deviations from the mean.

Digital Twin

A digital twin is a virtual model of a real-world process, product, or system that uses AI to simulate and predict outcomes.  The virtual representation is continually updated with real-time data, providing insights into performance, potential issues, and optimization opportunities.  Digital twin technology is highly applicable in industries like manufacturing, logistics, airlines, and healthcare.

Explainable AI (XAI)

Explainable AI (XAI) includes techniques that clarify how an AI model arrives at a specific outcome, making its decision-making process transparent and understandable.  XAI is critical for compliance, transparency, and user trust, especially in regulated sectors like finance and healthcare.

Generative AI

Generative AI is an area of artificial intelligence (AI) that focuses on creating or generating new content, such as images, music, text, or other creative outputs. It is powered by machine learning models, which are designed to understand and mimic the characteristics of the training data, allowing them to produce novel and unique outputs based on that understanding.

Hypothesis Testing

A type of statistical analysis in which you put your assumptions about a population parameter to the test.

• Null Hypothesis (H₀): The statement we aim to disprove, typically that there is no difference between groups.
• Alternative Hypothesis (H₁): The opposite of the null hypothesis, what we hope to prove.
• P-value: The probability of observing a result at least as extreme as the one we obtained, assuming the null hypothesis is true. A low p-value suggests rejecting the null hypothesis.
• Type I Error (Alpha): The probability of rejecting a true null hypothesis.
• Type II Error (Beta): The probability of failing to reject a false null hypothesis.
• Chi-Square Test: A statistical test used to determine if there is a significant association between two categorical variables.The test compares the observed values in your data to the expected values that you would see if the null hypothesis is true.

Inquisitive Analytics (Exploratory Analytics)

Inquisitive analytics is the practice of exploring data to discover underlying causes and relationships, going beyond surface-level observations. It involves detailed queries and analysis to understand why certain outcomes occurred, aiding in root cause analysis and problem-solving.

Large Language Model (LLM)

Large language models are deep learning algorithms that can recognize, summarize, translate, predict, and generate human-like language using very large datasets.

Machine Learning (ML)

Machine learning (ML) is a subset of artificial intelligence (AI) that focuses on developing algorithms allowing computers to learn from and make predictions based on data. ML models improve their performance over time as they are exposed to more data, enhancing predictive accuracy.

Multi-Agent Systems (MAS)

Multi-agent systems consist of multiple autonomous agents working together within an environment to accomplish tasks, often through collaboration or competition.  Each agent operates based on its programming and objectives, but the collective system can solve complex problems.  MAS are essential for scenarios requiring decentralized decision-making, such as supply chain management, network optimization, and robotics.

Natural Language Processing (NLP)

Natural language processing (NLP) is a branch of artificial intelligence that enables computers to understand, interpret, and respond to human language. NLP techniques are used in applications like chatbots, sentiment analysis, and language translation, bridging the gap between human communication and machine understanding.

Outlier

A data point that falls significantly outside the overall pattern of the data.

Predictive Analytics

Predictive analytics uses statistical techniques and machine learning algorithms to forecast future events based on historical data. It helps organizations anticipate outcomes and trends, enabling proactive decision-making and strategic planning.

Prescriptive Analytics

Prescriptive analytics combines predictive analytics with actionable recommendations to optimize decision-making. By suggesting the best course of action based on data analysis and predictive models, it supports effective strategy development and implementation.

Probability

The likelihood of an event occurring, expressed as a value between 0 (impossible) and 1 (certain).

Regression Analysis

Regression analysis is a set of statistical methods used to estimate relationships between a dependent variable and one or more independent variables.

• Linear Regression: A statistical method to model the relationship between a dependent variable and one or more independent variables using a straight line.
• R-squared: Represents the proportion of variance in the dependent variable explained by the independent variable(s) in a regression model.
• Logistic Regression: A statistical method used to model the relationship between a binary dependent variable (e.g., yes/no) and one or more independent variables.

Reinforcement Learning (RL)

Reinforcement learning is a type of machine learning where an agent learns through trial and error, receiving rewards or penalties based on its actions.  It’s particularly effective for optimizing decision-making in complex, ever-changing environments.  RL can be applied in scenarios such as dynamic pricing, resource allocation, and personalized recommendations, offering adaptive strategies that respond to real-world changes.

Sampling

A subset of data drawn from a larger population. Used to estimate population characteristics.

• Population Mean (μ): The true average value of a variable in the entire population.
• Sample Mean (x̄): The average value of a variable in a sample. Used to estimate the population mean.
• Confidence Interval: A range of values that is likely to contain the true population parameter (e.g., mean) with a certain level of confidence.

Statistical Significance

The probability of observing a statistically meaningful result, not due to chance.

T-Test

A statistical test used to compare the means of two groups, independent or paired is statistically significant or not.

Variability

The difference exhibited by data points within a data set, as related to each other or as related to the mean. Three key measurements of variability are:

• Range: The difference between the highest and lowest values in a dataset. Shows the spread of the data. (Formula: Maximum Value - Minimum Value)
• Variance: The average squared deviation of all values from the mean. Measures how spread out the data is. (Formula: Σ(x - μ)² / n)
• Standard Deviation (SD): The square root of the variance. Represents the average distance from the mean. (Formula: √(Σ(x - μ)² / n) )