Vishal Kanna AJK

Leveraged Excel to clean, manipulate, and analyze datasets exceeding 100,000 records.
Refined data accuracy by 20%.
Developed and optimized complex SQL queries to extract, join, and aggregate data from relational databases.
Reduced data retrieval time by 30%.
Crafted 5 interactive Power BI dashboards to visualize KPI metrics.
Increased stakeholder engagement during presentations by 15%.

Leveraged Excel to clean, manipulate, analyze datasets exceeding 100,000 records, Refined data accuracy by 20%., Developed and optimized complex SQL queries to extract, join, and aggregate data from relational databases, resulting in a 30% reduction in data retrieval time., Crafted 5 interactive Power BI dashboards to visualize KPI metrics, leading to a 15% increase in stakeholder engagement during presentations.

Completed self-directed study on the fundamentals of IoT, contributing 2 peer projects that enhanced efficiency., Acquired expertise in both manual and automation testing, utilizing Jira to simulate tracking and management of over 50 hypothetical defects and 30 test cases, which streamlined understanding of project delivery timelines., Studied Generative AI concepts through 15+ hours of focused research, sharing findings with 5 peers and initiating discussions on potential innovations and applications in IoT.

Completed self-directed study on the fundamentals of IoT.
Contributed 2 peer projects that enhanced efficiency.
Acquired expertise in both manual and automation testing.
Utilized Jira to simulate tracking and management of over 50 hypothetical defects and 30 test cases.
Streamlined understanding of project delivery timelines.
Studied Generative AI concepts through 15+ hours of focused research.
Shared findings with 5 peers.
Initiated discussions on potential innovations and applications in IoT.

Completed 3 Python-based projects.
Enhanced proficiency in writing efficient, modular code.
Reduced data processing times by 25%.
Gained foundational knowledge of machine learning algorithms.
Applied machine learning algorithms to achieve a 15% improvement in predictive accuracy on sample datasets.