Help
Options
Didn't know it?
click below
click below
Knew it?
click below
click below
Don't Know
Remaining cards (0)
Know
retry
shuffle
restart
0:00
Embed Code - If you would like this activity on your web page, copy the script below and paste it into your web page.
Normal Size Small Size show me how
Normal Size Small Size show me how
CIS Unit 3
| Term | Definition |
|---|---|
| structured query language (SQL) | Users write lines of code to answer questions against a database. |
| data element (or data field) | The smallest or basic unit of data. |
| metadata | Details about data. |
| attribute | The data elements associated with an entity. |
| record | A collection of related data elements. |
| data redundancy | The duplication of data, or the storage of the same data in multiple places. |
| data integrity | A measure of the quality of data. |
| identity management | A broad administrative area that deals with identifying individuals in a system (such as a country, a network, or an enterprise) and controlling their access to resources within that system by associating user rights and restrictions with the established |
| distributed computing | Processes and manages algorithms across many machines in a computing environment. |
| blockchain | A type of distributed ledger, consisting of blocks of data that maintain a permanent and tamper-proof record of transactional data. |
| proof-of-work | A requirement to define an expensive computer calculation, also called mining, that needs to be performed in order to create a new group of trustless transactions (blocks) on the distributed ledger or blockchain. |
| Bitcoin | A type of digital currency in which a record of transactions is maintained and new units of currency are generated by the computational solution of mathematical problems and which operates independently of a central bank. |
| blocks | Data structures containing a hash, previous hash, and data. |
| hash | A function that converts an input of letters and numbers into an encrypted output of a fixed length. |
| immutable | Simply means unchangeable. |
| Immutability | The ability for a blockchain ledger to remain a permanent, indelible, and unalterable history of transactions. |
| systems development life cycle (SDLC) | The overall process for developing information systems, from planning and analysis through implementation and maintenance. |
| planning phase | Establishes a high-level plan of the intended project and determines project goals. |
| analysis phase | The firm analyzes its end-user business requirements and refines project goals into defined functions and operations of the intended system. |
| business requirement | The specific business requests the system must meet to be successful. |
| design phase | Establishes descriptions of the desired features and operations of the system including screen layouts, business rules, process diagrams, pseudo code, and other documentation. |
| development phase | Takes all the detailed design documents from the design phase and transforms them into the actual system. |
| software engineering | A disciplined approach for constructing information systems through the use of common methods, techniques, or tools. |
| testing phase | Brings all the project pieces together into a special testing environment to eliminate errors and bugs and verify that the system meets all the business requirements defined in the analysis phase. |
| bugs | Defects in the code of an information system. |
| implementation phase | The organization places the system into production so users can begin to perform actual business operations with it. |
| maintenance phase | The organization performs changes, corrections, additions, and upgrades to ensure the system continues to meet its business goals. |
| corrective maintenance | Makes system changes to repair design flaws, coding errors, or implementation issues. |
| preventive maintenance | Makes system changes to reduce the chance of future system failure. |
| algorithms | Mathematical formulas placed in software that performs an analysis on a data set. |
| programming/coding | a computer so that it can perform useful tasks, and the act of writing computer codes is called |
| algorithm | A set of instructions (codes) that complete a task is an |
| sequences | which are a series of actions done in a specific order to complete a task |
| Selections/conditioning | which amounts to answering a true/false question based on data and calculation, then pursuing one set of actions if the answer is true, or a different set of actions if the answer is false. |
| Loops | instruct the computer to repeat a set of actions over and over again until a condition becomes false. |
| expression | A math operation to calculate a new value based on other values is |
| constant | is a value that does not change. Every time the calculation is performed the values 32, 5 and 9 fit into the math expression in the same place. |
| variable | is a place to store a value and that storage location can be changed to a different value at a later time if you want to do the calculation with different data. |
| Assignment | is the instruction that puts a new value into a variable (location of memory). |
| equation | When the result of a calculation is stored in another variable this turns the expression into an |
| integer | which is a whole number without decimal positions or fraction. |
| real number | that does contain decimal positions or fractions |
| Strings | Values that contain letters and symbols are called |
| Pseudocode | An informal description of how the computer program should work. |
| Alpha testing | assesses if the entire system meets the design requirements of the users |
| Development testing | tests the system to ensure it is bug free |
| Integration Testing | verifies that separate sytsems can work together passing data back and forth correctly |
| Project | temporary activity a company undertakes to create a unique product, service, or result |
| System Testing | verifies that the units or pieces of code function correctly when integrated. |
| testing phase | Brings all the project pieces together into a special testing environment to eliminate errors and bugs and verify that the system meets all the business requirements defined in the analysis phase. |
| unit testing | test individual units or pieces of code for a system |
| User Acceptance Testing | determines if the system satisfies the user and business requirements |
| After party | Instructions to finalize the task after the loop ends (this component is optional and is not always provided.) |
| Algorithms | is a set of steps used to complete a specific task |
| Condition | A test that determines TRUE or FALSE. In the case of a the loop, a TRUE determination tells the computer to continue running the loop, but a FALSE determination stops the loop. |
| Data Types | Data warehouses generally consist of data that is extracted from transactional or source systems and consist of quantitative metrics and the attributes that describe them. Data lake saves all data in raw form, regardless of source and structure |
| Exit Strategy | changing variables associated with the condition so the loop eventually stops. |
| Function | a named set of computer logic to accomplish a specific task sometimes called procedures |
| IF statement | logical comparisons between a value and what you expect in order to choose between two alternate outputs (the true condition output or the false condition output). |
| Loop Work | The instructions you want repeated. |
| Loops | direct the computer to repeat a set of instructions multiple times |
| Setup | Preparing the data or information needed for a task. For a loop, this establishes the initial values of variables for the condition and loop work. |
| While Loop | similar to an if statement but will continue to execute this code over and over again as long as the condition is true |
| Feasibility | The measure of the tangible and intangible benefits of an information system. |
| Intangible benefits | Difficult to quantify or measure. |
| Tangible benefits | Easy to quantify and typically measured to determine the success or failure of a project. |
| Artificial Intelligence (AI) | area of computer science that emphasizes the creation of intelligent machines that work and react like humans. A technology approach to enable machines to do what we formerly thought only humans could do. AI simulates human thinking and behavior, such as |
| Augmented Reality | The viewing of the physical world with computer-generated layers of information added to it. |
| Deep Learning | a subset of ML and refers to artificial neural networks that are composed of many layers. |
| Expert Systems | computerized advisory programs that imitate the reasoning process of experts in solving difficult problems by using a knowledge base containing accumulated experience and a set of "if-then" rules for applying the knowledge base to a particular situation. |
| Fuzzy Logic | is a mathematical method of handling imprecise or subjective information |
| General AI | machines that have all the senses (maybe more), all the reason, and think just like people do. |
| Genetic Algorithms | an AI technique that mimics the evolutionary, survival-of-the-fittest processes to generate increasingly better solutions to a problem. GA optimizes solutions by finding the best combination of inputs associated with the best output. |
| Machine Learning | method of data analysis that automates data model building. ML uses algorithms that learn iteratively from data and can find insights without explicit programming. |
| Narrow AI | technologies that can perform specific tasks as well as, or better than, humans. |
| Neural Networks | Computing systems inspired by biological neural networks. These systems learn (progressively improve performance) to do tasks by considering examples. The original goal of neural networks was to solve problems in the same way that a human brain would. Ove |
| Natural Language Processing | an AI technique using software to interpret natural languages (the languages spoken by people, such as English, French, Chinese and others). These techniques deal with speech recognition, understanding and generation |
| Reinforcement Learning | The training of machine learning models to make a sequence of decisions. |
| Virtual Reality | a computer-simulated environment that can be a simulation of the real world or an imaginary world |
| Unsupervised learning | analyzing all the profiles, to find general similarities and useful patterns |
| Reinforcement learning | uses an iterative approach to gather feedback about which medications, dosages, and treatments are most effective |
| Supervised Learning | uses human guidance to check the accuracy of the algorithm |
| Field | are specific categories that provide additional organization by placing like data in a single column. |
| structured query language (SQL) | Users write lines of code to answer questions against a database. |
| Tables | Composed of rows and columns that represent an entity. |
| record or rows | A collection of related data elements. |
| Attribute | The data elements associated with an entity. |
| One-to-one relationship (1:1) | A relationship between two entities in which an instance of one entity can be related to only one instance of a related entity. |
| one-to-many relationship (1:M) | A relationship between two entities in which an instance of one entity can be related to many instances of a related entity. |
| many-to-many relationship (M:N) | Between two entities in which an instance of one entity is related to many instances of another and one instance of the other can be related to many instances of the first entity. |
| Primary Key | makes it possible to identify every record uniquely in a table |
| Relational database model | Stores information in the form of logically related two-dimensional tables |
| Entity | Stores data about a person, place, thing transaction or event |
| Database management system DBMS | creates, reads, updates, and deletes data in a database while controlling access and security |
| foreign key | A primary key of one table that appears as an attribute in another table and acts to provide a logical relationship between the two tables |
| SELECT | In SQL this is done by listing the specific field names to be retrieved, with commas between each after the word |
| FROM | Identifying which table is done by providing the table name after the word |
| WHERE | lets us add selection criteria to a statement |
| business intelligence (BI) | Information collected from multiple sources such as suppliers, customers, competitors, partners, and industries that analyze patterns, trends, and relationships for strategic decision making. |
| business analytics | The scientific process of transforming data into insight for making better decisions. |
| Data Mining | The process of analyzing data to extract information not offered by the raw data alone. |
| data visualization | Describes technologies that allow users to “see” or visualize data to transform information into a business perspective. |
| Descriptive Analytics | Uses techniques that describe past performance and history. |
| predictive analytics | Uses techniques that extract information from data and useit to predict future trends and identify behavioral patterns. |
| prescriptive analytics | Uses techniques that create models indicating the best decision to make or course of action to take. |
| Bar chart | is a chart or graph that represents grouped data with rectangular bars with lengths proportional to the values they represent. |
| Pie chart | is a type of graph in which a circle is divided into sectors that each represent a proportion of the whole. |
| Histogram | is a graphical display of data using bars of different heights. It is similar to a bar chart, but a histogram groups numbers in to ranges. |
| Sparkline | is a small, embedded line graph that illustrates a single trend. Sparklines are often used in reports, presentations, dashboards and scoreboards. They do not include axes or labels; content comes from the related content. |
| Time-series | is a graphical representation showing change of a variable over time. Time-series charts are used for data that changes continuously, such as stock prices. They allow for a clear visual representation of the chagne in one variable over a set amount of tim |
| Data Visualization | describes technologies that allow users to see or visualize data; to transform information into a business perspective. |
| Infographic | is a representation of information in a graphic format designed to make the data easily understandable at a glance. |
| extract, transform, and load (ETL) | method to aggregate structured and unstructured data from multiple sources. |
| Data mining | or data discovery, typically uses automation to quickly analyze data to find patterns and outliers which provide insight into the current state of business. |
| Dirty Data | Erroneous or flawed data. |
| Data cleansing or scrubbing | A process that weeds out and fixes or discards inconsistent, incorrect, or incomplete data. |
| business intelligence dashboard | Tracks corporate metrics such as critical success factors and key performance indicators and include advanced capabilities such as interactive controls, allowing users to manipulate data for analysis. |
| Consolidation | is the aggregation of data from simple roll-ups to complex groupings of interrelated information |
| Data Lake | A storage repository that holds a vast amount of raw data in its original format until the business needs it |
| Data Mart | Contains a subset of data warehouse information. |
| Data warehouse | A logical collection of information, gathered from many different operational databases, that supports business analysis activities and decision-making tasks. |
| Raw data | Data that has not been processed for use. |
| Drill down | enables users to view details, and details of details, of information |
| Pivot | also known as rotation, rotates data to display alternative presentations of the data |
| Slice and dice | is the ability to look a information from different perspectives |
Created by:
adrianp87
Popular Science sets