Okay, I need to create a Wikipedia-style document about "Categorical Data Points" under the categories of Technology → Data Science → Analysis. Let me start by understanding what exactly is required here.

First, the structure: H1 for the title, an overview section, several sections with H2 and H3 headings, and maybe references or related documents. The content needs to be accurate, objective, detailed enough, easy to read, and use technical terms with explanations. Also, markdown formatting with proper hierarchy, lists, tables, code blocks if needed, and at least 800 characters.

So the title is "범주형 데이터 포인트" which translates to "Categorical Data Points." Let me start by defining what categorical data points are. They are data that can be divided into categories or groups. But I need to explain this in more detail.

In the overview, I should introduce the concept, maybe mention their importance in data science. Then, under sections, perhaps break it down into types of categorical data (nominal, ordinal), how they're used in analysis, methods for handling them, challenges, and examples.

Wait, the user mentioned "분석" as the subcategory, so I should focus on analysis aspects. Maybe include sections like Characteristics of Categorical Data Points, Common Analysis Techniques, Challenges in Handling, Applications, etc.

I need to make sure each section has H2 headings. For example:

1. 개요

2. 범주형 데이터 포인트의 특징

2.1 명목형 (Nominal)

2.2 순서형 (Ordinal)

3. 분석 기법

3.1 빈도 분석

3.2 교차분석

3.3 분류 알고리즘

4. 도전 과제

5. 응용 사례

Also, need to include examples and maybe some code snippets if possible. For instance, using Python's pandas for handling categorical data.

Wait, the user said "기술" as the main category, so technical details are okay. But I should explain terms like one-hot encoding or label encoding when they come up.

I should check that each section is detailed enough. For example, under characteristics, explain nominal vs ordinal with examples. In analysis techniques, describe each method and how it's applied to categorical data.

Need to ensure the document meets 800 characters. Let me count: the initial draft might be around that. Also, avoid markdown in the thinking process but use it in the final answer.

Make sure to mention challenges like high dimensionality from one-hot encoding, or issues with ordinal data's implied order not being meaningful.

Include references or related documents at the end, maybe links to articles on data preprocessing or specific algorithms.

I should also check for clarity and flow. Each section should logically follow the previous one. Use bullet points or tables where appropriate, like a table comparing nominal and ordinal data.

Okay, putting it all together now.