Exploratory Data Analysis With Pandas

02 Sep 2026 07:30 AM CST

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Webinar Overview

In this webinar, we will explore the process of performing Exploratory Data Analysis (EDA) using the Pandas library in Python. Attendees will learn how to inspect, clean, transform, and analyze datasets to uncover patterns, trends, and relationships in the data. The session will focus on practical techniques used in real-world analytics projects, including handling missing values, generating summary statistics, filtering data, and preparing data for further analysis or machine learning. Live demonstrations will help participants understand how to use Pandas effectively for data-driven decision-making.

Key Points

Introduction to EDA: Understand the purpose of exploratory data analysis and its importance in analytics and machine learning workflows.
Data Inspection and Cleaning: Learn how to examine dataset structure, handle missing values, remove duplicates, and correct data types using Pandas.
Data Analysis with Pandas: Perform filtering, grouping, aggregation, and summary statistics to identify trends and patterns in the data.
Practical Insights: Explore how EDA helps in discovering correlations, outliers, and business insights from real-world datasets.

Meet The Trainer

Sharayoo Gaurav Dixit

Mentor | NIIT

Duration: March 2021 – May 2026

Delivered instructor-led training sessions on Microsoft Excel, SQL, Python, Machine Learning, Tableau, and Power BI for students and working professionals.
Designed and developed training content, assignments, exercises, and learning materials for SQL and Python for Data Science programs.
Conducted hands-on workshops and practical sessions focused on real-world data analytics and machine learning applications.
Guided learners through end-to-end industry projects, including:
- Amazon Customer Feedback Analysis – Sentiment analysis and customer insights.
- Football Data Analysis – Performance analysis and data visualization.
- IMDb Movie Data Analysis – Exploratory data analysis, visualization, and predictive modeling.
Mentored students on data cleaning, data visualization, statistical analysis, and machine learning model development.
Assisted learners in applying analytical techniques using tools such as Python, SQL, Tableau, and Power BI to solve business problems.
Evaluated student performance through assignments, assessments, and project reviews while providing continuous feedback and guidance.

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Exploratory Data Analysis With Pandas

Mentor | NIIT