KWARA STATE UNIVERSITY, MALETE - Fundamentals of Data Analysis

Flashcards on Data Science and Big Data Fundamentals.

Data Science

Domain of study that deals with vast volumes of data using modern tools and techniques to find unseen patterns, derive meaningful information, and make business decisions.

Capture (Data Science Lifecycle)

Data Acquisition, Data Entry, Signal Reception, Data Extraction.

Maintain (Data Science Lifecycle)

Data Warehousing, Data Cleansing, Data Staging, Data Processing, Data Architecture.

Process (Data Science Lifecycle)

Data Mining, Clustering/Classification, Data Modeling, Data Summarization.

Analyze (Data Science Lifecycle)

Exploratory/Confirmatory, Predictive Analysis, Regression, Text Mining, Qualitative Analysis.

Communicate (Data Science Lifecycle)

Data Reporting, Data Visualization, Business Intelligence, Decision Making.

Big Data

Extremely large and diverse collections of structured, unstructured, and semi-structured data that continues to grow exponentially over time.

Big Data

Massive amount of organized and unstructured data that a company encounters on a daily basis, studied for insights.

Volume (Big Data)

The amount of data

Velocity (Big Data)

The rapid collection of data.

Structured Data

Data that has been arranged.

Semi-structured Data

A type of data that is semi-organized and doesn't follow the traditional data structure.

Unstructured Data

Data that has not been arranged and doesn't fit cleanly into a relational database's standard row and column structure.

Cost Savings (Big Data)

Helps in providing business intelligence that can reduce costs and improve the efficiency of operations.

Data

Collection of facts and figures used for analysis or a survey; a series of representations of various values of that quantity.

Quantitative Data

Numerical values are assigned to the characteristics or properties of objects or events, according to logically accepted rules.

Qualitative Data

The researcher takes into consideration the phenomenon as a whole and does not attempt to analyze it in measurable or quantifiable terms

Nominal Scale

Used when a set of objects among two or more categories is to be differentiated on the basis of certain clearly known characteristics

Ordinal Scale

Corresponds to quantitative classification of a set of objects done with the help of ranking on a continuum.

Interval Scale

Based on equal units of measurement; includes how much or how little of a given characteristic or attribute is present.

Ratio Scale

The highest level of measurement; assumes the existence of absolute zero.

Data Characteristics

Attributes include accuracy, completeness, consistency, timeliness, relevance, validity, and reliability.

Accuracy

Data should be free from errors and accurately reflect the real-world situation.

Completeness

Data should include all necessary information fields without missing values.

Timeliness

Data should be current and available when needed for decision-making.

Relevance

Data should be relevant to the specific task or analysis at hand, avoiding unnecessary or irrelevant information.

Reliability

Data should produce consistent results over time and be trustworthy.

Data collection

The process of collecting, measuring and analyzing different types of information using a set of standard validated techniques.

Primary Data

Data collected from first-hand experience directly from the main source.

Secondary data

Information that is been collected or gathered by some other researchers.

Observation Method

Data from the field is collected with the help of observation by the observer or by personally going to the field.

Participant Observation

The researcher actively engages in the daily activities of the subjects being studied.

Non-Participant Observation

The researcher observes subjects without becoming involved in their activities.

Questionnaire Method

A set of questions arranged logically, divided into groups, with the object of collecting information for research.

Schedule Method

The questionnaire but it filled by enumerator.

Survey method

A detailed inspection or investigation.

Panel Method

Data is collected from the same sample respondents at some interval either by mail or by personal interview.

Case Study Method

An intensive investigation of the particular unit under consideration..

Raw Data

Unprocessed data that has been collected and recorded directly from a source without any manipulation, organization, or analysis.

Contextualized Data

Refers to raw data that has been processed, organized, and enriched with additional context or meaning.

Metadata

Data about data

Descriptive Metadata

Aids users in finding, identifying, and selecting resources by describing them for search and discovery.

Structural Metadata

Describes the structure, type, and relationships of data.

Administrative Metadata

Carries technical details about a file or resource and is crucial for its identification, presentation, and preservation.