Sequence the steps in EDA from the initial stage to the final stage:

Question

BlackTom AI · Accepted Answer

In approaching the question about the sequence of steps in Exploratory Data Analysis (EDA) from the initial stage to the final stage, I will evaluate each option in terms of logical order and typical EDA workflow.
Option a: 'Data modeling, data visualization, dimensionality reduction, data transformation' This sequence starts with data modeling, which is generally not part of initial EDA; modeling often follows after understanding the data. Visualization and dimensionality reduction can occur during EDA, but placing data transformation after visualization and before inspection is atypical. Overall, the order mixes tasks that are not aligned with the conventional EDA pipeline.
Option b: 'Data visualization, dimensionality reduction, data cleaning, pattern detection' Here, data visualization and dimensionality reduction are exploratory techniques, but performing data cleaning after some visualization and reduction is counterintuitive; cleaning is usually an early, preparatory step to ensure accurate analysis. Pattern detection is a broad term that could occur later, not as an early end goal in EDA; the order is therefore problematic.
Option c: 'Data cleaning, hypothesis testing, data transformation, data visualization' This option places hypothesis testing—an inferential step—early in an EDA sequence, whereas EDA is typically about exploration to inform potential hypotheses, not about formal testing. Data visualization at the end is also unusual since visualization is often used throughout EDA to uncover patterns and guide decisions.
Option d: 'Data gathering, data cleaning, data inspection, data analysis' This option reflects a coherent progression: first gather data (collect the dataset), then clean it (handle missing values, outliers, inconsistencies), then inspect or explore (summary statistics, basic examinations, visuals to understand structure), and finally analyze (draw insights, identify patterns, hypotheses, or models). This aligns well with standard EDA workflows where preparation precedes exploration and analysis.
Across all options, the key concepts of EDA sequencing emphasize: (1) initial data collection, (2) cleaning and preprocessing to ensure quality, (3) inspection/exploration to understand characteristics and quirks, (4) analysis to derive insights. The only choice that cleanly maps to this logical flow is option d, while the others misplace steps or mix in inferential or modeling tasks prematurely. It’s important to recognize that while visualization and dimensionality reduction can occur during EDA, their placement should follow or accompany proper data cleaning and initial inspection, not precede them in ways shown in options a–c.

Data Science Principles (202504-LLecture)

Sequence the steps in EDA from the initial stage to the final stage:

查看解析

登录即可查看完整答案

类似问题

Which of the following is NOT a goal of EDA?

Sequence the steps in EDA from the initial stage to the final stage:

Assertion: EDA helps in understanding the data’s characteristics. Reason: Visualizing data is optional in EDA and does not significantly contribute to data understanding.

Assertion: EDA helps in understanding the data’s characteristics. Reason: Visualizing data is optional in EDA and does not significantly contribute to data understanding.

Select the most complete steps in the exploratory analysis workflow (note some items are only one component of a step)

In RStudio, perform IDA on the iris data. Which species does the last flower in the iris dataset belong to?

In RStudio, perform IDA on the iris data. How many different species of iris flowers does the dataset have?

更多留学生实用工具

考试浏览器助手

风格化写作助手

论文查重助手

文献引用助手

课堂转译助手

课堂笔记助手

Quiz搜索助手

学校历年真题

智能刷题助手

智能匹配练习

希望你的学习变得更简单