Data Analyst Analysis

Asha Ravilla

Lafayette, LA **************@*****.*** /in/asha-ravilla-22a693137

PROFESSIONAL SUMMARY

Versatile Data Analys with a strong background in data analysis, Python programming, and SQL for developing business insights and data solutions. Demonstrated ability to gather and translate business requirements into technical solutions, leveraging advanced analytical skills. Proven track record in applying machine learning algorithms and developing visualizations to interpret complex datasets. EDUCATION

University of Louisiana at Lafayette Lafayette, LA Masters in Computer Science (GPA: 3.9/4.0) Graduation Date: May 2024 Sardar Vallabhbhai National Institute of Technology (SVNIT) Surat, India B.Tech in Electronics Engineering (GPA: 7.54/10) Graduation Date: May 2019 WORK EXPERIENCE

UL Lafayette Lafayette, LA

Research Assistant Aug 2022 - May 2024

Project: In Collaboration with MITRE Research Team

• Utilized Python for data preprocessing and analysis of the LANL dataset, applying machine learning algorithms to enhance predictive model accuracy by 15%.

• Utilized Pandas Data Frames for data manipulation and analysis, enabling efficient processing and transformation of complex datasets.

• Collaborated with research teams to translate academic requirements into technical solutions, enhancing data quality and research outcomes.

• Conducted in-depth analysis and visualization of the LANL dataset, identifying key trends and insights for academic research.

Wipro Limited Hyderabad, India

Associate Consultant July 2019 – May 2022

Client: HSBC

• Custom BI Reports and Forms Development: Developed comprehensive business intelligence reports and forms using Oracle BI Publisher, which enhanced visibility and management of financial transactions.

• User Acceptance Testing for International Releases: Led the User Acceptance Testing

(UAT) for HSBC’s country-specific releases in China, Egypt, and Hong Kong. Took full ownership of the Expenses module for the China release, managing user setup, test case preparation, and business requirement validation.

• Collaboration on BI Reports Testing: Worked closely with cross-functional teams to test and validate BI reports, ensuring the accuracy of financial data used for strategic planning and decision-making.

TECHNICAL SKILLS

Data Analysis & Visulaization: Python, SQL, Tableau, Power BI Statistical Analysis: Machine Learning Algorithms, Predictive Modeling. Big Data Technologies: Apache Spark, PySpark, SparkSQL Database Systems and Data Warehousing: PostgreSQL, Oracle, Snowflake Cloud Technologies: AWS (Redshift, Kinesis, EMR, Lambda, S3, Glue, Quick Sight, IAM roles and permissions), Azure (Data Factory, Synapse Analytics, Data Lake, Databricks, Key Vault) Version Control: Git, GitHub

Development & Aigle Tools: Jupyter Notebooks, Visual Studio, JIRA PROJECTS

YouTube Data Analysis & Reporting

Technologies: AWS S3, Glue, Lambda, Quick Sight

• Engineered an AWS-based data pipeline for real-time analysis of YouTube video statistics. Utilized AWS services to automate data collection, transformation, and visualization.

• Reduced data processing time by 50% and developed dynamic dashboards for actionable insights on trending content.

Oracle Database Design & Implementation

Technologies & Tools: Oracle SQL, PL/SQL, SQL Developer

• Designed and implemented a comprehensive Oracle database for a civil engineering case study, focusing on efficient schema normalization and data modeling.

• Enhanced database functionality with views and stored procedures, improving data accessibility and processing efficiency.

ACHIEVEMENTS

• Recognized for outstanding academic performance with an Academic Excellence Award during the Spring 2023 Honors Convocation at the University of Louisiana at Lafayette.

• Awarded the "Creating Magic with Customer" accolade in the Walk of Fame for Q2 FY 20- 21 at Wipro for exemplary work and dedication during the challenging HSBC Egypt and UAE releases.

ACADEMIC PAPERS

• Title: ” Supervised Approach to Predicting Network Evolution from Temporal Netflow Graphs” Contributed in Co-authoring a paper on predicting network topology changes using supervised machine learning techniques. Utilized graph-based modeling to forecast link dynamics, achieving a significant improvement in prediction accuracy

Contact this candidate