Module 2(Data Analytics)

35問 • 1年前

問題一覧

The process of using statistical analysis and machine learning to discover hidden patterns, correlations and anomalies within large data sets.

Data Mining

involves identifying a specific business problem or objective to be achieved through data analysis.

Problem Defenition

Involves gathering relevant data from a variety of sources including both internal and external sources

Data Collection

In this step, statistical methods and algorithms are used to analyze the data in order to uncover patterns, relations and insights.

Data Analysis

Data Analysis techniques: 1.Regression anaylsis 2.Clustering analysis decision trees

TRUEEEE

Allows you to determine whether your analysis has addressed the initial problem or needs further refinement.

Evaluation

Effective deployment involves clear communication and education around how findings should be applied and ongoing monitoring to assess progress and make adjustments when necessary.

KNOWLEDGE DISCOVERY a process that extracts implicit, potentially useful or previously unknown information from the data.

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

Database Data

Each record in a transactional database captures a transaction.(Transactional Data)

A repository of information collected from multiple sources, stored under a unified schema and usually residing at a single site.

Data Warehouses

Other Kinds of Data: • Sequence data • data streams • spiral data • engineering design data • hypertext and multimedia data • graph and network data • The Web.

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

Statictics

Machine Learning - Investigates how computer can learn based on data.

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

Database system research

The science of searching for documents or information in documents.

Information Retrieval

MAJOR ISSUES Mining methodologies should consider issues such as data uncertainty, noise, and incompleteness.

User Interaction • The user plays an important role in the data mining process. • The data mining process should be highly interactive. Discovered knowledge should be easily understood and directly usable by humans.

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These two factors are especially critical.

Efficiency and Scalability

Algorithms must be efficient and scalable in order to effectively extract information from huge amounts of data.

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Diverse Application generate a wide spectrum of new data types. (Diversity of Data Types) Challenging and Fast- Evolving data mining fields: 1. Web Mining 2. Multisource Data Mining 3. Information Network Mining

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Data Mining and Society • It is important to study the impact of data mining on society. We cannot expect everyone in society to learn and master data mining techniques.

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refers to pointing out the flaws of the data. The anomaly can be referred to as an erroneous entry which doesn't match with its corresponding values.

Anomaly or Outlier Detection

An object or entry which divulges from the average value of the data set is known as an

Outlier

Is about finding relations or interesting patterns from the large data set. method of identifying hidden relations in the database.

Assosiation Rule learning

is the process of identifying similar data from the large data-set and grouping them together. Can help a business understand the similarities and differences of the data.

Clustering Anaysis

is the method of obtaining information about the data. Classification of the data means dividing data in terms of different relevant categories.

Classification Analysis

Analysis that is complementary to “Clustering Analysis”

Classification Analysis

. It analyses the effect of changing the value of one variable on the whole data set. It can help them understand customer relations and calculate customer satisfaction.

Regression Analysis

technique of storing large quantities of structured data securely. Is also responsible for the maintenance and security of data.

Data Warehousing

Visualization: is the process of tabulating data in the form of graphs, charts and diagrams and digital images. This helps businesses calculate and draft their growth chart. They can also compare their growths with their competitors and determine their position in the market.

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This alculates the mean, mode, and median of the data to predict future patterns. •Calculates their ROI

Statistical Techniques

Is the method of identifying patterns from the current database. businesses can track patterns to understand the customer's behavior and use this knowledge to draft a successful marketing campaign.

Tracking Patterns

It can help them track the sales pattern. It can also help companies develop an understanding of the series of events happening in their

Sequence Patterns

Data Structure (history)

iam noone · 64問 · 2年前

Data Structure (history)