Below we have listed our Data Glossary terms to help assist you on your data journey.
A/B testing, also known as split testing, refers to a randomized experimentation process wherein two or more versions of a variable (web page, page element, etc.) are shown to different segments of website visitors at the same time to determine which version leaves the maximum impact and drives business metrics
OLTP systems must ensure that the entire transaction is recorded correctly. A transaction is recorded correctly only if all the steps involved are executed and recorded. If there is any error in any one of the steps, the entire transaction must be aborted and all the steps must be deleted from the system. Thus, OLTP systems must comply with atomic, consistent, isolated, and durable (ACID) properties to ensure the accuracy of the data in the system. Atomic: atomicity controls guarantee that all the steps in a transaction are completed successfully as a group. That is, if any steps in a transactions fail, all other steps must also fail or be reverted. The successful completion of a transaction is called commit. The failure of a transaction is called abort. Consistent: the transaction preserves the internal consistency of the database. Isolated: the transaction executes as if it were running alone, with no other transactions. That is, the effect of running a set of transactions is the same as running them one at a time. This bahavior is called serializability and is usually implemented by locking the specific rows in the table. Durable: the transaction's result will not be lost in a failure.
Algorithum refers to a sequence of finite steps to solve a particular problem
Amazon Web Services
Analytical data is used by businesses to find insights about customer behavior, product performance, and forecasting. It includes data collected over a period of time and is usually stored in OLAP (Online Analytical Processing) systems, warehouses, or data lakes.
Apache Hadoop is an open source software framework that provides highly reliable distributed processing of large data sets using simple programming models. Hadoop, known for its scalability, is built on clusters of commodity computers, providing a cost-effective solution for storing and processing massive amounts of structured, semi-strucuted and unstructured data with no format requirements. Hadoop can also be installed on cloud servers to better manage the compute and storage resources required for big data. Leading cloud vendors such as Amazon Web Services (AWS) and Microsoft Azure offer solutions. Cloudera supports Hadoop workloads both on-premises and in the cloud, including options for one or more public cloud environments from multiple vendors.
Apache Spark (Spark) is an open source data-proprocessing engine for large data sets. It is designed to deliver the computational speed, scalability, and programmability required for Big Data—specifically for streaming data, graph data, machine learning, and artificial intelligence (AI) applications. Spark's analytics engine processes data 10 to 100 times faster than alternatives. It scales by distributing processing work across large clusters of computers, with built-in parallelism and fault tolerance. It even includes APIs for programming languages that are popular among analysts and data scientists, including Scala, Java, Python, and R. Spark is often compared to Apache Hadoop, and specifically to MapReduce, Hadoop's native data-processing component. The main difference between Spark and MapReduce is that Spark processes and keeps the data in memory for subsequent steps—without writing to or reading from disk—which results in dramatically faster processing speeds. Spark was developed in 2009 at UC Berkeley. Today, it's maintained by the Apache Software Foundation and boasts the largest open source community in big data, with over 1,000 contributors. It's also included as a core component of several commercial big data offerings.
Apache Spark Mllib
One of the critical capabilities of Apache Spark is the machine learning abilities available in the Spark MLlib. The Apache Spark MLlib provides an out-of-the-box solution for doing classification and regression, collaborative filtering, clustering, distributed linear algebra, decision trees, random forests, gradient-boosted trees, frequent pattern mining, evaluation metrics, and statistics. The capabilities of the MLlib, combined with the various data types Spark can handle, make Apache Spark an indispensable Big Data tool.
Application development portfolio
Artificial intelligence (AI) is the ability of a computer or a robot controlled by a computer to do tasks that are usually done by humans because they require human intelligence and discernment. Although there are no Ais that can perform the wide variety of tasks an ordinary human can do, some AIs can match humans in specific tasks.
An association rule is a rule-based method for finding relationships between variables in a given dataset. These methods are frequently used for market basket analysis, allowing companies to better understand relationships between different products. Understanding consumption habits of customers enables businesses to develop better cross-selling strategies and recommendation engines.
Behavior anomaly detection
Big data refers to the large, diverse sets of information that grow at ever-increasing rates. It encompasses the volume of information, the velocity or speed at which it is created and collected, and the variety or scope of the data points being covered (known as the ""three v's"" of big data). Big data often comes from data mining and arrives in multiple formats.
Binary classification models output a value from a class that contains only two values, for example, a model that outputs either rain or no rain.
Business intelligence combines business analytics, data mining, data visualization, data tools and infrastructure, and best practices to help organizations make more data-driven decisions. Modern BI solutions prioritize flexible self-service analysis, governed data on trusted platforms, empowered business users, and speed to insight.
Class-imbalanced data set
Classification models predict the likelihood that something belongs to a category. Unlike regression models, whos output is a number, classification models output a valuae that states whether or not something belongs to a particular category. For example, classification models are used to predict if an email is spam or if a photo contains a cat.
A commonly used unsupervised learning model employs a technique called clustering. The model finds data points that demarcate natural groupings. Clustering differs from classification because the categories aren't defined by you. For example, an unsupervised model might cluster a weather dataset based on temperature, revealing segmentations that define the seasons. You might then attempt to name those clusters based on your understanding of the dataset.
OLTP systems can have enormously large user populations, with many users trying to access the same data at the same time. The system must ensure that all these users trying to read or write into the system can do so concurrently. Concurrency controls guarantee that two users accessing the same data in the database system at the same time will not be able to change that data, or that one user has to wait until the other user has finished processing before changing that piece of data.
Convolutional neural networks (CNNs)
Convolutional neural networks (CNNs) are usually utilized for image recognition, pattern recognition, and/or computer vision. These networks harness principles from linear algebra, particularly matrix multiplication, to identify patterns within an image.
Customer retention rate
Cyber threat intelligence
Dashboards are a collection of widgets that give you an overview of the repports and metrics you care most about. Dashboards let you monitor many metrics at once, so you can quickly check the health of your accounts or see correlations between different reports.
Data is the driving force of ML. Data comes in the form of words and numbers stored in tables, or as the values of pixels and waveforms captured in images and audio files.
Data centric solution
Data engineering is the practice of designing and building systems for collecting, storing, and analyzing data at scale. It is a broad field with applications in just about every industry. Organizations have the ability to collect massive amounts of data, and they need the right people and technology to ensure it is in a highly usable state by the time it reaches data scientists and analysts.
Data lake store an abundance of disparate, unfiltered data to be used later for a particular purpose. Data from line-of-business applications, mobile apps, social media, IoT devices, and more is captured as raw data in a data lake. The structure, integrity, selection, and format of the various datasets is derived at the time of analysis by the person doing the analysis. When organizations need low-cost storage for unformatted, unstructured data from multiple sources that they intend to use for some purpose in the future, a data lake might be the right choice.
A data mart performs the same functions as a data warehouse but within a much more limited scope—usually a single department or line of business. This makes data marts easier to establish than data warehouses. However, they tend to introduce inconsistency because it can be difficult to uniformly manage and control data across numerous data marts.
Data mining is a process used by companies to turn raw data into useful information. By using software to look for patterns in large batches of data, businesses can learn more about their customers to develop more effective marketing strategies, increase sales and decrease costs. Data mining depends on effective data collection, warehousing, and computer processing.
Once the scope of the problem is defined, it is easier for data scientists to identify which set of data will help answer the pertinent questions to the business. Once they collect the relevant data, the data will be cleaned, removing any noise, such as duplicates, missing values, and outliers. Depending on the dataset, an additional step may be taken to reduce the number of dimensions as too many features can slow down any subsequent computation. Data scientists will look to retain the most important predictors to ensure optimal accuracy within any models.
Data privacy audit
Data protection regulation
A data warehouse is a type of data management system that is designed to enable and support business intelligence (BI) activities, especially analytics. Data warehouses are solely intended to perform queries and analysis and often contain large amounts of historical data. The data within a data warehouse is usually derived from a wide range of sources such as application log files and transaction applications.
A database is an organized collection of structured information, or data, typically stored electronically in a computer system. A database is usually controlled by a database management system (DBMS). Data within the most common types of databases in operation today is typically modeled in rows and columns in a series of tables to make processing and data querying efficient. The data can then be easily accessed, managed, modified, updated, controlled, and organized. Most databases use structured query language (SQL) for writing and querying data.
Database management systems (DBMS)
A DBMS serves as an interface between the database and its end users or programs, allowing users to retrieve, update, and manage how information is organized and optimized. A DBMS also facilitates oversight and control of databases, enabling a variety of administrative operations such as performance monitoring, tuning, and backup and recovery. Some examples of popular database software or DBMSs include MySQL, Microsoft Access, Microsoft SQL Server, and Oracle Database.
A dataset is characterized by its size and diversity. Size indicates the number of examples. Diversity indicates the range those examples cover. Good datasets are both large and highly diverse.
This data mining technique uses classification or regression methods to classify or predict potential outcomes based on a set of decisions. As the name suggests, it uses a tree-like visualization to represent the potential outcomes of these decisions.
Deep learning is a subset of machine learning, which is essentially a neural network with three or more layers. These neural networks attempt to simulate the behavior of the human brain allowing it to "learn" from large amounts of data. While a neural network with a single layer can still make approximate predictions, additional hidden layers can help to optimize and refine for accuracy. Deep learning drives many artificial intelligence (AI) applications and services that improve automation, performing analytical and physical tasks without human intervention. Deep learning technology lies behind everyday products and services (such as digital assistants, voice-enabled TV remotes, and credit card fraud detection) as well as emerging technologies (such as self-driving cars). Deep learning requires a tremendous amount of computing power. High performance graphical processing units (GPUs) are ideal because they can handle a large volume of calculations in multiple cores with copious memory available. However, managing multiple GPUs on-premises can create a large demand on internal resources and be incredibly costly to scale.
Directed Acyclic Graph (DAG)
As opposed to the two-stage execution process in MapReduce, Spark creates a Directed Acyclic Graph (DAG) to schedule tasks and the orchestration of worker nodes across the cluster. As Spark acts and transforms data in the task execution processes, the DAG scheduler facilitates efficiency by orchestrating the worker nodes across the cluster. This task-tracking makes fault tolerance possible, as it reapplies the recorded operations to the data from a previous state.
A distributed database consists of two or more files located in different sites. The database may be stored on multiple computers, located in the same physical location, or scattered over different networks.
One of the effective way to handle imbalanced data. This method reduces the count of training samples falling under the majority class. As it helps to even up the counts of target categories. By removing the collected data, it is tend to lose a lot of valuable information.
Ectract Transform Load (ETL)
Emergency response analytics
Euclidean distance (p=2)
This is the most commonly used distance measure, and it is limited to real-valued vectors. Using the below formula, it measures a straight line between the query point and the other point being measured.
Explanatory data analysis
Exploratory data analysis
Extract Load Transform (ELT)
Estimate future data outside your data range
Features are the values that a supervised model uses to predict the label. In a weather model that predicts rainfall, the features could be latitude, longitude, temperature, humidity, cloud coverage, wind direction, and atmospheric pressure.
Fraud detection is a set of activities undertaken to prevent money or property from being obtained through false pretenses. Fraud detection is applied to many industries such as banking or insurance. In banking, fraud may include forging checks or using stolen credit cards. Activities such as reorganization, downsizing, moving to new information systems or encountering a cybersecurity breach could weaken an organization's ability to detect fraud. Techniques such as real-time monitoring for fraud is recommended. Organizations should look for fraud in financial transactions, locations, devices used, and authentication systems.
Geospatial intelligence analysis
Google Big Query
Google Cloud Platform (GCP)
Graph databases are purpose-built to store and navigate relationships. Relationships are first-class citizens in graph databases, and most of the value of graph databases is derived from these relationships. Graph databases use nodes to store data entities, and edges to store relationships between entities. An edge always has a start node, end node, type, and direction, and an edge can describe parent-child relationships, actions, ownership, and the like. There is no limit to the number and kind of relationships a node can have. In graph databases, traversing the joins or relationships is very fast because the relationships between nodes are not calculated at query times but are persisted in the database. Graph databases have advantages for use cases such as social networking, recommendation engines, and fraud detection, when you need to create relationships between data and quickly query these relationships.
Hadoop Distributed File System (HDFS)
A distributed file system for storing application data on commodity hardware. It provides high-throughput access to data and high fault tolerance. The HDFS architecture features a NameNode to manage the file system namespace and file access and multiple DataNodes to manage data storage.
A framework for managing cluster resources and scheduling jobs. YARN stands for Yet Another Resource Negotiator. It supports more workloads, such as interactive SQL, advanced modeling and real-time streaming.
This technique is typically used with Boolean or string vectors, identifying the points where the vectors do not match. As a result, it has also been referred to as the overlap metric. This can be represented with the following formula: As an example, if you had the following strings, the hamming distance would be 2 since only two of the values differ.
High performance computing (HPC)
A classification data set with skewed class proportions is called imbalanced. Classes that make up a large proportion of the data set are called majority classes. Those that make up a smaller proportion are minority classes
Once we are satisfied with the results from evaluating the model, we can use the model to make predictions, called inferences, on unlabeled examples. In the weather app example, we would give the model the current weather conditions—like temperature, atmospheric pressure, and relative humidity—and it would predict the amount of rainfall.
Internet of Thing (IoT)
Estimate missing data within your data range
K- nearest neighbor (KNN)
The k- nearest neighbors algorithum, also known as KNN or k-NN, is a non-parametric, supervised learning classifier, which uses proximity to make classifications or predictions about the grouping of an individual data point. While it can be used for either regression or classification problems, it is typically used as a classification algorithum, working off the assumption that similar points can be found near one another. It's also worth noting that the KNN algorithm is also part of a family of ""lazy learning"" models, meaning that it only stores a training dataset versus undergoing a training stage. This also means that all the computation occurs when a classification or prediction is being made. Since it heavily relies on memory to store all its training data, it is also referred to as an instance-based or memory based learning method. While it is not as popular as it once was, it is still one of the first algorithms one learns in data science due to its simplicity and accuracy. However, as a dataset grows, KNN becomes increasingly inefficient, compromising overall model performance. It is commonly used for simple recommendation systems, pattern recognition, data mining, financial market predictions, intrusion detection, and more."
The label is the ""answer"", or the value we want the model to predict. In a weather model that predicts rainfall, the label would be rainfall amount."
In linear regression, the objective is to fit a hyperplane (a line for 2D data points) by minimizing the sum of mean-squared error for each data point. It assumes that the relationship between the dependant (y) and independent (x) variables are linear.
Logistic regression estimates the probability of an event occurring, such as voted or didn't voted, based on a given dataset of independent variables. Since the outcome is a probability, the dependent variable is bounded between 0 and 1.
Machine learning offers a new way to solve problems and answer complex questions. In basic terms, ML is the process oftraining a piece of software, called a model, to make useful predictions from data. An ML model represents the mathematical relationship between the elements of data that an ML system use to make predictions.
Manhattan distance (p=1)
This is also another popular distance metric, which measures the absolute value between two points. It is also referred to as taxicab distance or city block distance as it is commonly visualized with a grid, illustrating how one might navigate from one address to another via city streets.
Microsoft Power BI
This distance measure is the generalized form of Euclidean and Manhattan distance metrics. The parameter, p, in the formula below, allows for the creation of other distance metrics. Euclidean distance is represented by this formula when p is equal to two, and Manhattan distance is denoted with p equal to one.
Missing at random (MAR)
Missing completely at random (MCAR)
Missing not at random
In supervised learning, a model is the complex collection of numbers that define the mathematical relationship from specific input feature patterns to specific output label values. The model discovers these patterns through training.
Accuracy is one metric for evaluating classification models. Accuracy = Number of correct predictions/total number of predictions For binary classification, accuracy can also be calculated in terms of positives and negatives as of follows: Accuracy = (TP+TN)/(TP+TN+FP+FN) Where TP = True Positives, TN = True Neagatives, FP = False Positive, and FN = False Negatives.
Multiclass classification models output a value from a class that contains more than two values, for example, a model that can output either rain, hail, snow, or sleet.
Natural language generation (NLG)
Natural language generation is another subset of natural language processing. While natural language understanding focuses on computer reading comprehension, natural language generation enables computer to write. NLG is the process of producing a human language text response based on some data input. This text can also be converted into a speech format through text-to-speech services.
Natural language processing
Natural language processing (NLP) refers to the branch of computer science—and more specifically, the branch of artificial intelligence or AI—concerned with giving computers the ability to understand text and spoken words in much the same way human beings can. NLP combines computational linguistics—rule-based modeling of human language—with statistical, machine learning, and deep learning models. Together, these technologies enable computers to process human language in the form of text or voice data and to 'understand' its full meaning, complete with the speaker or writer's intent and sentiment. NLP drives computer programs that translate text from one language to another, respond to spoken commands, and summarize large volumes of text rapidly—even in real time.
Natural Language Toolkit (NLTK)
The Python programming language provides a wide range of tools and libraries for attacking specific NLP tasks. Many of these are found in the Natural Language Toolkit, or NLTK, an open source collection of libraries, programs, and education resources for building NLP programs. The NLTK includes libraries for many of the NLP tasks, plus libraries for subtasks, such as sentence parsing, word segmentation, stemming and lemmatization (methods of trimming words down to their roots), and tokenization (for breaking phrases, sentences, paragraphs and passages into tokens that help the computer better understand the text). It also includes libraries for implementing capabilities such as semantic reasoning, the ability to reach logical conclusions based on facts extracted from text.
Natural language understanding (NLU)
Natural language understanding is a subset of natural language processing, which uses syntactic and semantic analysis of text and speech to determine the meaning of a sentence. Syntax refers to the grammatical structure of a sentence, while semantics alludes to its intended meaning. NLU also establishes a relevant ontology: a data structure which specifies the relationships between words and phrases. While humans naturally do this in conversation, the combination of these analyses is required for a machine to understand the intended meaning of different texts.
Neural networks, also known as artificial neural networks (ANNs) or simulated neural networks (SNNs), are a subset of machine learning and are at the heart of deep learning algorithms. Their name and structure are inspired by the human brain, mimicking the way that biological neurons signal to one another. Artificial neural networks (ANNs) are comprised of a node layers, containing an input layer, one or more hidden layers, and an output layer. Each node, or artificial neuron, connects to another and has an associated weight and threshold. If the output of any individual node is above the specified threshold value, that node is activated, sending data to the next layer of the network. Otherwise, no data is passed along to the next layer of the network. Neural networks rely on training data to learn and improve their accuracy over time. However, once these learning algorithums are fine-tuned for accuracy, they are powerful tools in computer science and artificial intelligence, allowing us to classify and cluster data at a high velocity. Tasks in speech recognition or image recognition can take minutes versus hours when compared to the manual identification by human experts. One of the most well-known neural networks is Google's search algorithum.
A NoSQL, or nonrelational database, allows unstructured and semistructured data to be stored and manipulated (in contrast to a relational database, which defines how all data inserted into the database must be composed). NoSQL databases grew popular as web applications became more common and more complex.
Online Analytical Processing (OLAP)
OLAP performs multidimensional analysis of business data and provides the capability for complex calculations, trend analysis, and sophisticated data modeling. It is the foundation for many kinds of business applications for Business Performance Management, Planning, Budgeting, Forecasting, Financial Reporting, Simulation Models, Knowledge Discovery, and Data Warehouse Reporting. OLAP enables end-users to perform ad hoc analysis of data in multiple dimensions, thereby providing the insight and understanding they need for better decision making.
Online Transaction Processing (OLTP)
OLTP or Online Transaction Processing is a type of data processing that consists of executing a number of transactions occurring concurrently—online banking, shopping, order entry, or sending text messages, for example. The primary definition for transactions—economic or financial—remains the foundation for most OLTP systems, so online transaction processing typically involves inserting, updating, and/or deleting small amounts of data in a data store to collect, manage, and secure those transactions. Typically, a web, mobile, or enterprise application tracks all those interactions or transactions with customers, suppliers, or partners and updates them in the OLTP database.
Operational data is used for day-to-day transactions and needs to be fresh—for example, product inventory and bank balance. Such data is captured in real-time using Online Transaction Processing (OLTP) systems.
Oracle is the technology provider with a complete suite of integrated cloud applications and a cloud infrastructure platform. The Oracle Cloud offers services to migrate, build, and run IT, from existing enterprise workloads to new cloud-native applications and data platforms. Oracle Cloud Applications is built on top of Oracle's cloud infrastructure with front and back-office modules to support consistent processes and a single source of truth across the most critical business functions.
Precision attempts to answer the following question: What proportion of positive identifications was actually correct? Precision = TP/(TP+FP) Note: a model that produces no false positives has precision of 1.0
Prediction refers to the output of an algorithm after it has been trained on a historical dataset and applied to new data when forecasting the likelihood of a particular outcome, such as whether or not a customer will churn in 30 days.
Process mining leverages data mining techniques to reduce costs across operational functions, enabling organizations to run more efficiently. This practice has helped to identify costly bottlenecks and improve decision-making among business leaders.
Python has become one of the most popular programming languages in the world in recent years. It's used in everything from machine learning to building websites and software testing.
Recall attemps to answer the following question: What proportion of actual positives was identified correctly? Mathematically, recall is defined as follows: Recall = TP/(TP+FN) Note: a model that produces no false negatives has a recall of 1.0
Recurrent neural networks (RNNs)
Recurrent neural networks (RNNs) are identified by their feedback loops. These learning algorithums are primarily leveraged when using time-series data to make predictions about future outcomes, such as stock market predictions or sales forecasting.
Regression analysis is the process of estimating the relationship between a dependent variable and independent variables. One of the basic tools in the area of machine learning used for prediction. Using regression you fit a function on the available data and try to predict the outcome for the future or hold-out data points. Some real-world examples for regression analysis include predicting the price of a house given house features, predicting the impact of SAT/GRE scores on college admissions, predicting the sales based on input parameters, predicting the weather, etc.
A regression model predicts a numeric value. For example, a weather model that predicts the amount of rain, in inches or milimeters, is a regression model.
Reinforcement learning models make predictions by getting rewards or penalties based on actions performed within an environment. A reinforcement learning system generates a policy that defines the best strategy for getting the most rewards. Reinforcement learning is used to train robots to perform tasks, like walking around a room, and software programs like AlphaGo to play the game of Go.
Relational databases became dominant in the 1980s. Items in a relational database are organized as a set of tables with columns and rows. Relational database technology provides the most efficient and flexible way to access structured information.
Resilient Distributed Dataset (RDD)
Resilient Distributed Datasets (RDDs) are fault-tolerant collections of elements that can be distributed among multiple nodes in a cluster and worked on in parallel. RDDs are a fundamental structure in Apache Spark. Spark loads data by referencing a data source or by parallelizing an existing collection with the SparkContext parallelize method into an RDD for processing. Once data is loaded into an RDD, Spark performs transformations and actions on RDDs in memory—the key to Spark's speed. Spark stores the data in memory unless the system runs out of memory or the user decides to write the data to disk for persistence. Each dataset in an RDD is divided into logical partitions, which may be computed on different nodes of the cluster. User can perform two types of RDD operations: transformations and actions. Transformation are operations applied to create a new RDD. Actions are used to instruct Apache Spark to apply computation and pass the result back to the driver.
Return on investment (ROI)
Sentiment analysis attempts to extract subjective qualities—attitudes, emotions, sarcasm, confusion, suspicion—from text. In particular, sentiment analysis enables brands to monitor their customer feedback more closely, allowing them to cluster positive and negative social media comments and track net promoter scores. By reviewing comments with negative sentiment, companies are able to identify and address potential problem areas within their products or services more quickly.
Spark Core is the base for all parellel data processing and handles scheduling, optimization, RDD, and data abstraction. Spark Core provides the functional foundation for the Spark libraries, Spark SQL, Spark Streaming, the Mllib machine learning library, and GraphX graph data processing. The Spark Core and cluster manager distribute data across the Spark cluster and abstract it. This distribution and abstraction make handling Big Data very fast and user-friendly.
In addition to having API capabilities, Spark has Spark GraphX, a new addition to Spark designed to solve graph problems. GraphX is a graph abstraction that extends RDDs for graphs and graph-parallel computation. Spark GraphX integrates with graph databases that store interconnectivity information or webs of connection information, like that of a social network.
Spark Streaming is an extension of the core Spark API that enables scalable, fault-tolerant processing of live data streams. As Spark Streaming processes data, it can deliver data to file systems, databases, and live dashboards for real-time streaming analytics with Spark's machine learning and graph-processing algorithums. Built on the Spark SQL engine, Spark Streaming also allows for incremental batch processing that results in faster processing of streamed data.
"Speech recognition, also called speech-to-text, is the task of reliably converting voice data into text data. Speech recognition is required for any application that follows voice commands or answers spoken questions. What makes speech recognition especially challenging is the way people talk—quickly, slurring words together, with varying emphasis and intonation, in different accents, and often using incorrect grammar."
Structured Query Language (SQL)
SQL is a programming language used by nearly all relational databases to query, manipulate, and define data, and to provide access control. SQL was first developed at IBM in the 1970s with Oracle as a major contributor.
Supervised learning models can make predictions after seeing lots of data with the correct answers and then discovering the connections between the elements in the data that produce the correct answers. This is like a student learning new material by studying old exams that contain both questions and answers. Once the student has trained on enough old exams, the student is well prepared to take a new exam. These ML systems are ""supervised"" in the sense that a human gives the ML system data with the known correct results. Two of the most common use cases for supervised learning are regression and classification.
Supply chain optimization
Tableau was founded in 2003 as a result of a computer science project at Stanford that aimed to improve the flow of analysis and make data more accessible to people through visualization. Co-founders Chris Stolte, Pat Hanrahan, and Christian Chabot developed and patented Tableau's foundational technology, VizQL—which visually expresses data by translating drag-and-drop actions into data queries through an intuitive interface. Tableau was acquired by Salesforce in 2019. Today, organizations everywhere—from non-profits to global enterprises, and across all industries and departments—are empowering their people with Tableu to drive change with data.
Text summarization uses NLP techniques to digest huge volumes of digital text and create summaries and synopses for indexes, research databases, or busy readers who don't have time to read full text. The best text summarization applications use semantic reasoning and natural language generation (NLG) to add useful context and conclusions to summaries.
Before a supervised model can make predictions, it must be trained. To train a model, we give the model a dataset with labeled examples. The model's goal is to work out the best solution for predicting the labels from the features. The model finds the best solution by comparing its predicted value to the label's actual value. Based on the difference between the predicted and actual values—defined as loss—the model gradually updates its solution. In other words, the model learns the mathematical relationship between the features and the label so that it can make the best predictions on unseen data.
Unsupervised learning models make predictions by being given data that does not contain any correct answers. An unsupervised learning model's goal is to identify meaningful patterns among the data. In other words, the model has no hints on how to categorize each piece of data, but instead it must infer its own rules.