Exploratory Data Analysis

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1 session

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About

In this session, learners will understand the fundamentals of **Exploratory Data Analysis (EDA)** and why it is a critical first step in any machine learning workflow. We will cover how to explore datasets, identify patterns, detect anomalies, and understand variable relationships using both **statistical summaries and visualizations** such as histograms, bar charts, boxplots, and scatterplots. Learners will engage through guided explanations, live demonstrations, and short interactive discussions on interpreting data insights before building ML models. The goal is to help participants think like data scientists and develop intuition for analyzing real-world datasets. 

Tutored by

Dwij V 🇮🇳

Certified in 2 topics

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I'm a high schooler from India. I’m a motivated learner with a strong interest in mathematics, problem-solving, and analytical thinking. I enjoy breaking down complex concepts into clear, logical steps and helping others build confidence through understanding rather than memorization. I’m joining Schoolhouse to both sharpen my own foundations and contribute by supporting peers in structured, concept-driven learning. I value discipline, consistency, and depth, and I believe collaborative learning is one of the fastest ways to grow intellectually while giving back to a serious academic community.

Sessions

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✋ ATTENDANCE POLICY

Students must join the session on time and remain present for the full duration. Active participation is expected during questions, discussions, and the neural network design activity. Repeated late arrivals, extended inactivity, or leaving early without notice may lead to removal from the session.

SESSION 1

21

Mar

SESSION 1

Even More Math

Sat 7:00 AM - 8:30 AM UTCMar 21, 7:00 AM - 8:30 AM UTC

In this session, we will explore the fundamentals of Exploratory Data Analysis (EDA) and its role in the machine learning pipeline. The session will cover how to understand a dataset, identify variable types, summarize data using statistical measures, and visualize patterns using common plots such as histograms, bar charts, boxplots, and scatterplots. Learners will also see how EDA helps detect outliers, missing values, anomalies, and relationships between variables before model building. The session will include conceptual explanations along with practical examples demonstrating how data scientists explore datasets to extract meaningful insights.

Public Discussion

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Mar 21

1 week

90 mins

/ session

Next session on March 21, 2026

SCHEDULE

Saturday, Mar 21

7:00AM