VL 1.1 Business Analytics: Big Data & Statistics

BestSellingIntellect avatar
BestSellingIntellect
·
·
Download

Start Quiz

Study Flashcards

30 Questions

Descriptive Statistics involves using machine learning techniques to identify future outcomes.

False

Prescriptive Statistics analyzes past performance and trends to determine future actions.

True

Big Data Analytics only refers to the high volume of data, not data types and structures.

False

Predictive Statistics focuses on identifying the likelihood of future outcomes based on historical data.

True

The main purpose of Big Data Analytics is to provide historical data insights only.

False

Decision Trees are an example of Descriptive Statistics techniques.

False

As data type becomes more disorderly, less data preparation or pre-processing time is required.

False

Semi-structured data can be easily addressed by using parsing, a function available in Excel.

True

Quasi-structured data is defined as having consistent and easily interpretable formats.

False

Parsing semi-structured data may lead to issues if the data is not well-organized.

False

Successful parsing of semi-structured data allows for proper processing of the data.

True

Quasi-structured data requires no additional effort for formatting and can be easily handled.

False

Cropital is a healthcare-focused crowdfunding platform that uses Big Data to assess the creditworthiness of potential borrowers.

False

Financial institutions purchase data from data collectors and aggregators to optimize pricing strategies.

False

Statista collects data on topics such as consumer behavior, industry trends, and market research.

True

Retailers purchase data from data aggregators to assess credit risk.

False

Marketing agencies purchase data from data aggregators to develop personalized financial products.

False

Data Users/Buyers are entities that collect data on consumer behavior, industry trends, and market research.

False

Data profiling involves examining tables, files, and database structure before the extraction process.

True

Failure during the ETL process is impossible if the data is considered 'Too Dirty'.

False

Incorrect relationships between tables are examples of data quality issues related to database structures.

True

Data quality issues related to having two or more files about a single subject, with mismatched unique identifiers, are common in the ordering process.

False

Intervention to address data quality issues includes reviewing the source details manually and making corrections as necessary.

True

Conducting data gathering is the first step in the Extract-Transform-Load (ETL) process.

False

A perceptron is a plane that maximizes the margin between two classes.

False

Support Vectors are equivalent to two lines at the edge of a plane.

False

The perceptron separates classes in a non-parametric way.

True

The perceptron is used to determine the class of any particular dot based on its position relative to the line.

True

Default values classified as 'yes' are represented by negative numbers relative to the perceptron.

False

Pre-processing of data involves plotting distance normalized values after normalization.

False

Learn about Big Data Analytics and different statistical methods such as Descriptive Statistics and Predictive Statistics. Understand concepts like data processing and different statistical measures like Central Tendency and Standard Deviation.

Make Your Own Quizzes and Flashcards

Convert your notes into interactive study material.

Get started for free

More Quizzes Like This

Big Data Analytics Quiz
5 questions
Business Analytics and Big Data Quiz
6 questions
Big Data Management Challenges
18 questions
Introducción al Big Data
4 questions
Use Quizgecko on...
Browser
Browser