Data Analytics (Lesson 1-3)

a.) analytics b.) System analytics c.) business analytics - Correct Answer d.) data analytics

The process of understanding the scope, objectives, and complexities of business projects.

Society

When purchasing items online, users may be unaware that the store is likely collecting data on the buying patterns of its customers, which may be used to recommend other items for purchase in the future.

Public data

Agglomerative Hierarchical

Density-Based

DBSCAN

Model-based

Here, all the clusters are hypothesized in order to find the data which is best suited for the model.

Database

A collection of data files that contain information about people, transactions, and /or inventory.

Grid-based

A fast processing time and it is dependent only on the number of cells in each dimension in the quantized space.

False

Analytics can be defined as a variable that involves the use of statistical techniques, information system software, and operations research methodologies to explore, visualize, discover, and communicate patterns or trends in data.

True

As a general technology, data mining can be applied to any kind of data as long as the data are meaningful for a target application.

User Interaction

Background knowledge, constraints, rules, and other information regarding the domain under study should be incorporated into the knowledge discovery process.

hierarchical

Can be agglomerative or divisive approach.

Hierarchical

Chameleon

Web Data

Characterize and classify web pages, and uncover web dynamics and the association and other relationships among different web pages, users, communities, and web-based activities.

Partitioning

Clusters are represented by the prototype and we use the iterative counterstrategy to optimize the clustering.

True

Data analytics became very useful not only for business sector but also in government services.

True

Data analytics helps the retailer in analyzing user behavior and figuring out the best way to organize website based on user preferences.

Information Retrieval

Finding the major topics in a collection of documents and, for each document in the collection, the major topics involved.

Prescriptive

One use of this is to properly allocate resources .

Statistics

This is useful for mining various patterns from data as well as for understanding the underlying mechanisms generating and affecting the patterns.

Does Include: - data discovery - market research - tracking applications Does Not Include: - identifying different areas

Which of the following is/are the application of clustering?

Data Cleanser

Hardware and software used for data remote storage, retrieval, and computational functions

Density-Based

Here, the given cluster will keep on growing continuously as long as the density in the neighborhood exceeds some threshold, i.e, for each data point within a given cluster.

Traffic

Hierarchical clustering

Sequence Data

Historical records, stock exchange data, and time-series and biological sequence data

Efficiency and Scalability

In order to effectively extract information from huge amounts of data in many data repositories or in dynamic data streams, data mining algorithms must have these two factors.

Stock Market

K-means

knowledge discovery from data

Many people treat data mining as a synonym for this another popularly used term

Sequence Data

Mining banking data for changing trends, which may aid in the scheduling of bank tellers according to the volume of customer traffic.

True

Mining multiple data sources of complex data often leads to fruitful findings due to the mutual enhancement and consolidation of such multiple sources.

True

Powerful and versatile tools are badly needed to automatically uncover valuable information from the tremendous amounts of data and to transform such data into organized knowledge.

Data Visualization

Presenting information graphically to illustrate trends and patterns.

Mining Methodology

Should consider issues such as data uncertainty, noise, and incompleteness.

Data Set

A simple collection of data or a data file.

False

Steps 1 through 5 of the knowledge discovery process are different forms of data preprocessing, where data are prepared for mining.

Statistics

Studies the collection, analysis, interpretation or explanation, and presentation of data.

Data mining

The knowledge discovery process step that applies intelligent methods to extract data patterns.

Pattern evaluation

The knowledge discovery process step that identifies the truly interesting patterns representing knowledge based on interestingness measures.

Data Mining

The process of discovering interesting patterns and knowledge from large amounts of data.

False

The transdisciplinary nature of data mining research and development contributes significantly to the success of data mining and its extensive applications.

Diversity of Database Types

The wide diversity brings about challenges to data mining which includes diverse applications that generate a wide spectrum of new data type.

False

Data Analytics helps the search engine in ranking the web pages regardless of its category.

True

Data analytics is a major component of today's manufacturing since most transactions are done in real-time.

Model-Based

EM

Capturing images

Mean Shift clustering

Data cleaning

The knowledge discovery process step that removes noise and inconsistent data.

Data transformation

The knowledge discovery process step that transforms and consolidates data into forms appropriate for mining by performing summary or aggregation operations.

Knowledge presentation (2nd One)

The knowledge discovery process step that uses visualization and knowledge representation techniques to present mined knowledge to users.

Transactional Data

These data are captured transactions such as a customer's purchase, a flight booking, or a user's clicks on a web page.

Database Data

This consists of a collection of interrelated data and a set of software programs to manage and access the data.

Database Systems Approach

Well known for their high scalability in processing very large, relatively structured data sets.

Includes: - more effective marketing -can continuously collect and analyze new data to update business understanding as conditions change. -can boost revenue through increased conversions, ad revenue or subscriptions Does not include: -can reveal hidden information of the employees

What are the benefits of data analysis?

Includes: - data engineer - data architect Does Not include: - data manager - data adminitsrator

What are the possible careers for those who know data analysis aside from data analyst?

Machine Learning

This investigates how computers can learn (or improve their performance) based on data.

Information Retrieval

This is the science of searching for documents or information in documents.

Data Streams

Video surveillance and sensor data, which are continuously transmitted

False

A cluster is a set of similar objects.

Data Model

A way to organize the elements of a data set.

True

Cluster is a connected region of a multidimensional space with a comparatively high density of objects.

Meteorological department

Density based subsequence clustering.

Spatial Data

Describing changes in metropolitan poverty rates based on city distances from major highways.

Multimedia Data

Detecting video sequences corresponding to goals of a hockey game

Multimedia Data

Identifying objects and classifying them by assigning semantic labels or tags from images

Text Data

Identifying the evolution of hot topics in literature

True

In Clustering, falling under the category of unsupervised machine learning, is one of the problems that machine learning algorithms solve.

Partitioning

K-means

Predictive

Need to use advanced statistical, IS or operations research methods that an identify ominous variables and create a model to identify trends and relationships

Mining Methodology

Not all the patterns generated by data mining processes are interesting.

Database Systems Approach

Research focuses on the creation, maintenance, and use of databases for organizations and end-users.

Descriptive

The application of simple statistical techniques that determine what is contained in a data set or database.

Data Warehouse

The data are stored to provide information from a historical perspective in a repository of information collected from multiple sources, stored under a unified schema, and usually residing at a single site.

Statistics

The data mining process can use these models to help identify and handle noisy or missing values in the data.

User Interaction

The data mining process should be highly interactive. It is important to build flexible user interfaces and an exploratory mining environment, facilitating the interaction with the system.

Engineering Design Data

The design of buildings, system components, or integrated circuits

Society

The improper disclosure or use of data and the potential violation of individual privacy and data protection rights are areas of concern that need to be addressed.