Here’re incredible ideas for group discussion. It is also called GD. Most companies do conduct GD during their campus Interviews.

Preparation for group discussion

  1. Take plenty of water before you start
  2. Don’t be nervous, be cool
  3. Take initiation to tell the solution or explanation
  4. How logical you tell is matter
  5. Don’t focus on the result, do your job

What the observer focuses

  1. Communication Skills: how good you are at speaking assertively and at the same time listening to others’ points of view.
  2. Behavior and Attitude: how contrary or opposing views affect you and your mindset and how you carry weaker team members.
  3. Contribution: How you successfully align your personal goals with those of the group.
  4. Technical: How your knowledge and technical skills can solve complex problems and remove obstacles for others.

Dos and Don’ts

  • Never Argue – Be smile and friendly with others during the discussion.
  • Knowledge is power – Prepare well before you attend for GD.
  • Take initiation – Use good English-phrases to take control of others for your turn.
  • Listen – Listening to others gives you clarity on what they said. So, you can tell your views.
  • Use simple English – Simple English is more than enough to communicate well. Speak politely with a smile. Don’t be angry with anyone.

Audio

Group Discussion

Keep Reading

You May Like : Useful English Phrases for Group Discussions

LATEST POSTS

Ingesting Data from AWS S3 into Databricks with Auto Loader: Building a Medallion Architecture

In this blog post, we will explore efficient methods for ingesting data from Amazon S3 into Databricks using Auto Loader. Additionally, we will discuss how to perform data transformations and implement a Medallion architecture to improve the management and processing of large datasets. What is the Medallion Architecture? The Medallion architecture is a data modeling…

Exploring Databricks Unity Catalog – System Tables and Information _Schema: Use Cases

Databricks Unity Catalog offers a unified governance solution for managing structured data across the Databricks Lakehouse platform. It enables organizations to implement fine-grained access controls, auditing, and monitoring, enhancing data governance and compliance. Key functionalities include centralized metadata management, data discovery, dynamic reporting, and data lineage tracking, optimizing performance and collaboration.

PySpark Functions Real Use Cases

PySpark is an API for Apache Spark in Python that enables big data processing and analytics, featuring a wide array of built-in functions. These functions facilitate data manipulation, aggregation, and statistical analysis. They include column, aggregate, window, string, and date-time functions, allowing efficient processing of large datasets in a distributed environment.

Something went wrong. Please refresh the page and/or try again.