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Python For Data Analysis

28 Aug 2026   02:30 AM CST

  • Free Webinar
  • Live Q&A
  • Free Participation Certificate
  • Learn from Industry Experts

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Webinar Overview
In this webinar, we will introduce the fundamentals of data analysis using Python and demonstrate how to extract meaningful insights from real-world datasets. Participants will learn how to load, clean, analyze, and visualize data using Pandas and Matplotlib. The session is designed to be practical and beginner-friendly, with live demonstrations focused on common data analysis tasks used in business and reporting scenarios.
Key Points
  • Introduction to Python for Data Analysis: Understanding the role of Python in analytics and an overview of Pandas and Matplotlib.
  • Data Cleaning: Handling missing values, duplicates, and basic data transformations.
  • Exploratory Data Analysis: Performing simple statistical analysis and identifying trends and patterns in data.
  • Data Visualization: Creating basic charts and graphs to present insights effectively.
  • Live Demo: End-to-end demonstration using a sample dataset.
Meet The Trainer
Sharayoo Gaurav Dixit
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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