Introduction to Descriptive Analytics

Study Notes

Descriptive Analytics Overview

Descriptive Analytics, also known as Exploratory Data Analysis, summarizes and describes data using statistical tools and visuals to support decision-making.
Key questions addressed include what happened, why it occurred, and what is currently happening.

Business Analysis Process (BAP) vs Organization Decision Making Process (ODMP)

Similarities exist between BAP and ODMP, with both being frameworks for analysis.
BAP is data-driven, focusing on objectivity and factual data.
ODMP is more descriptive and subjective in analysis.

Purpose of Descriptive Analytics

Clarifies past phenomena through visual representations like graphs and charts.
Facilitates understanding of data contained within datasets or databases.
Aims to identify trends, general data patterns, and meanings from collected information.

Data Characteristics

Data consists of numerical quantitative figures and qualitative facts collected, tabulated, summarized, and analyzed for interpretation.
Elements represent units from which data is collected, while variables are characteristics of those elements.
Observations refer to specific measurements of variables for individual elements.

Role of Descriptive Analytics

Collects and analyzes data to understand variation’s impact on business.
Explores high and low value scenarios and how they vary under different conditions.
Maximizes opportunities while controlling business effects.

Variable Types

Decision Variables: Under direct control of decision-makers.
Random Variables: Values fluctuate due to uncertainty and factors outside direct control.

Data Types

Population Data: Includes all elements from a dataset.
Sample Data: Consists of one or more observations drawn from the population.

Measurement Scales

Quantitative Data: Measurable data suitable for arithmetic operations.
Categorical Data: Countable qualitative data summarized by counting observations within categories.

Categorical Data Types

Nominal Scale Variables: Classify or name categories with no true operational meaning; mutually exclusive and collectively exhaustive.
Ordinal Scale Variables: Classify data into ordered categories with transitivity assumptions (e.g., preference ranking).

Fundamental Terms

Central tendencies, dispersions, frequency distributions, probability, and sampling are vital in describing data sets using descriptive analytics.
Critical for business intelligence and strategy development, descriptive analytics provides insights from historical and current data.

Description

This quiz covers the fundamentals of Descriptive Analytics, focusing on its role in the business analysis process and organizational decision making. Learn about the types of data, scales of measurement, and essential factors to consider during data gathering and processing. Prepare to explore descriptive analytics concepts and their applications in data analysis.