# Data Types and Organization Explained | CFA Level I Quantitative Methods

PREREQUISITE LESSON

This lesson is a prerequisite for the course. While you won’t be directly tested on its content in the exam, it’s assumed you’ve gained this knowledge or skill during your university studies. We strongly recommend reviewing this lesson, as its content may be essential for understanding subsequent parts of the curriculum.

Welcome to our guide on data types and organization! Here, we’ll explore different approaches to organizing, visualizing, and describing data. We’ll cover numerical vs. categorical data, cross-sectional vs. time series data, and structured vs. unstructured data.

## Numerical vs Categorical Data

Numerical data, or quantitative data, can be counted or measured. It comes in two flavors:

• Discrete data: Countable and finite number of values.
• Continuous data: Can take any fractional value, with infinite possibilities within a range.

Categorical data, or qualitative data, consists of labels used to classify data into groups. Unlike numerical data, you can’t perform mathematical operations on categorical data. There are two types:

• Nominal data: Labels without a logical order, e.g., industry classification.
• Ordinal data: Ranked labels in a logical order, e.g., mutual fund performance quartiles.

EXAMPLE

Annualized return: Continuous numerical data, since it can be measured on a scale.

Quartile ranking: Ordinal data, because it only indicates the relative positions of funds.

## Cross-Sectional and Time Series Data

Cross-sectional data: Comparable observations taken at one specific point in time.

Time series data: Observations taken periodically over time, usually representing a single variable.

These data types can be combined to form panel data, organized as a two-dimensional array or data table. This structure is useful for comparing trends across different entities.

## Structured and Unstructured Data

Structured data is organized in a defined way, such as time series, cross-sectional, and panel data. Examples include market data, fundamental data, and analytical data.

Unstructured data has no defined structure and can be generated by individuals (social media posts), business processes (corporate filings), or sensors (traffic monitoring). Analyzing unstructured data offers potential sources of returns but can be challenging due to its nature.

Financial models usually require structured data as inputs, so unstructured data must first be transformed into structured data for processing.

That wraps up our introduction to data types and organization! In our next lesson, we’ll explore data summarization and visualization. Stay tuned!

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