Name: EDA
Uploaded: 2025-06-02T21:54:18+00:00
Duration: 1 min 42 s
Description: In this segment, we explore the importance of Exploratory Data Analysis (EDA) in machine learning interviews, especially

In this segment, we explore the importance of **Exploratory Data Analysis (EDA)** in machine learning interviews, especially during notebook-based exercises. This is a key moment to distinguish ourselves by demonstrating analytical depth and real-world intuition.

- We move beyond just **checking for missing values or printing basic stats**, which signals a surface-level approach.
- Instead, we analyze **feature distributions**, identifying skewness, outliers, and unusual patterns that may affect modeling.
- We examine **relationships and interactions between variables**, not just computing correlations, but exploring potential dependencies and dynamics.
- We assess **data quality**, including duplicates, inconsistent categorical values, and signs of data leakage.
- This deeper EDA shows that we can **interrogate the data critically**, a trait that interviewers associate with strong, production-ready ML practitioners.

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In this segment, we explore the importance of Exploratory Data Analysis (EDA) in machine learning interviews, especially

Tech has one of the highest rates of job switching, making this skill incredibly high-leverage and vital to master. Understand what it takes to convince companies you are strongly competent.