KSQM Calendar

Get ready for this year's retreat—it's going to be an amazing experience! Here’s what’s in store:
 
Oral Presentation Sessions: Dive into engaging discussions and share your latest research findings with fellow members.
 
Poster Sessions: Show off your research with a poster presentation, spark conversations, and get feedback from your peers.
This retreat is all about connecting, collaborating, and strengthening the KSQM community. Plus, it's a perfect chance to highlight your work and gather valuable input.
 
Networking Opportunities: Meet and mingle with other members in a relaxed, friendly setting designed for building connections and swapping ideas.
 
We can’t wait to see you there and make KSQM Retreat 2024 something truly special!

Fundamentals of Big Data – Importance, challenges, and application areas of large datasets.
Data Management Framework – Strategies for storage, management, and fast analysis of large datasets.
Data Parallelization & Scalability – Distributed processing, parallelization techniques.
Data Preparation & Analysis – Methods for data cleaning, evaluation, and visualization.
Application Examples
Practical Part:
Introduction to Spark
Hands-on practice with PySpark – Demonstrations and exercises.

Fundamentals of Big Data – Importance, challenges, and application areas of large datasets.
Data Management Framework – Strategies for storage, management, and fast analysis of large datasets.
Data Parallelization & Scalability – Distributed processing, parallelization techniques.
Data Preparation & Analysis – Methods for data cleaning, evaluation, and visualization.
Application Examples
Practical Part:
Introduction to Spark
Hands-on practice with PySpark – Demonstrations and exercises.

Fundamentals of Big Data – Importance, challenges, and application areas of large datasets.
Data Management Framework – Strategies for storage, management, and fast analysis of large datasets.
Data Parallelization & Scalability – Distributed processing, parallelization techniques.
Data Preparation & Analysis – Methods for data cleaning, evaluation, and visualization.
Application Examples
Practical Part:
Introduction to Spark
Hands-on practice with PySpark – Demonstrations and exercises.

Fundamentals of Big Data – Importance, challenges, and application areas of large datasets.
Data Management Framework – Strategies for storage, management, and fast analysis of large datasets.
Data Parallelization & Scalability – Distributed processing, parallelization techniques.
Data Preparation & Analysis – Methods for data cleaning, evaluation, and visualization.
Application Examples
Practical Part:
Introduction to Spark
Hands-on practice with PySpark – Demonstrations and exercises.