Advanced Data Analytics Flashcards

The composite hardware, software, network resources, and services required

for the existence, operation, and management of an enterprise IT environment.

Information that is delivered immediately after collection, with no delay in the timeliness of the information.

Data related to the physical properties and location of rocks and minerals found within the Earth.

The process of inspecting, cleansing, transforming, and modeling data to discover useful information and inform conclusions.

The analysis of large datasets and the attempt to discover meaningful insights and dependencies in these data sets.

Analyses not only the content but also the sentiment and emotions of a text.

Organizing different works in terms of characteristics.

Grouping different texts together based on their genre.

Provide text which is most relevant to the task at hand.

Categorizing whether an opinion (in text form) is positive, negative, or neutral.

The analysis of information contained in photographs, medical images, or graphics.

The analysis of information contained in a video footage.

Obtains information from audio recordings of spoken content.

The use of data, statistical algorithms, and machine learning techniques to identify the likelihood of future outcomes based on historical data.

An analytical framework that consists of building a model that shows how a final output would look like in several different cases.

It can be used to assess the possible realizations of different strategic choices or to create a combination of scenarios.

Indicates of the strength of the relationship between two variables.

A statistical method used to understand the relationships between two or more variables, identifying how they change together.

Helps us determine how well a dependent variable is explained by the development of one or more independent variables.

The practice of iterative, methodical exploration of an organization's data, with an emphasis on

statistical analysis, used to drive decision-making and improve performance.

Relies on past data and the data of a certain variable in the past to predict the future

and allows us to quantify the impact of management decisions on the future outcome.

A forecasting method that assumes future values will equal the most recent actual value available,

often used as a benchmark for more complex forecasting methods.

Statistical models for analyzing and forecasting time series data, which use the dependency between an observation and a number of lagged observations.

Forecasting models that smooth out short-term fluctuations in time series data by calculating the average of different subsets of the total dataset over time.

They are usually plotted on a line chart.

A behavioral analytics technique that breaks the data into related groups before analysis, often used to study the behavior of customers over time.

Network of devices connected to the internet, exchanging data.

A branch of artificial intelligence that helps computers understand, interpret, and manipulate

human language to perform tasks like translation, sentiment analysis, and topic extraction.

AI capability to identify and interpret images or visual data.

A math technique used to find an optimal solution to a problem that has several known constraints.

Computer programs that are modelled on the human brain and the way we as humans learn.

Computational models inspired by the human brain's neurons, used in neural networks to process and transmit information in artificial intelligence systems.

Artificial neurons are arranged in a series of layers:

Input layer (receives information)

Hidden layers (transform information)

Output layer (uses information)

A subset of machine learning where ANNs, algorithms inspired by the human brain, learn from large amounts of data.

Deep learning allows machines to solve complex problems even when the dataset is very diverse, unstructured, or interconnected

by performing a task repeatedly, each time tweaking it a little to improve the outcome.

A method used in artificial neural networks to calculate the gradient of the loss function with respect to the weights, used for training the network.

A form of machine learning where the optimal behaviour or action is reinforced by a positive reward.

Applying a change in one part of the organization and comparing it against the other where the change hasn't been applied yet.

A collection of techniques used for data reduction and structure detection when working with big datasets and a large number of variables.

Elements or variables that are considered in data analysis and modeling to understand their impact on outcomes,

used in statistical analyses to identify potential causal relationships.

Dynamic data that is generated continuously from a variety of sources. Each record needs to preserve its relations to the other data and sequence in time.

When analytics is brought to the data to generate insights while the data is in the 'stream' instead of stored.

A structured repository that contains structured data in rows and columns.

'Distributed storage' means using cheap, off-the-shelf components to create high-capacity data storage,

which is controlled by a program that keeps track of where everything is, and finds it upon request.

Refers to extremely large data sets that may be analyzed computationally to reveal patterns, trends, and associations.

Democratizes 'big data exploration'. It enables small businesses to take advantage of big data so they can have access to large data sets.

A mathematical problem-solving and risk-assessment technique that approximates the probability

or the risk of a certain outcome using computerized simulations of a random variable.