Data Analyst Visualization

SAI KIRAN MEDARAMETLA

Data Analyst

Location: TX Email: *******************@*****.*** Phone: 619-***-****

SUMMARY

3+ years of industry experience with solid understanding of Data Modeling, Evaluating Data Sources, and strong understanding of Data Warehouse/Data Mart Design, Data Mining ETL, BI, Data visualization, Data Validation OLAP, Client/Server applications.

Proficient in using Python libraries such as Pandas, NumPy, Seaborn, and Matplotlib to perform data cleaning, visualization, and model building, translating data into actionable insights.

Had clear understanding of Data Warehousing concepts with emphasis on ETL and Life Cycle Development including requirement analysis, design, development, testing and implementation.

Demonstrated expertise in data mining, data cleansing, statistical analysis, data visualization, and text mining, facilitating comprehensive insights extraction from raw datasets.

Highly experienced in data analysis, data governance, data mining/mapping, data management and risk analysis.

Hand-on experience performing statistical analysis, causal inference and statistical modeling in R and Python, interpreting and analyzing results of hypothesis tests, A/B tests and multivariate tests and providing recommendations based on data.

Experience in Tableau Desktop, Power BI for data visualization, Reporting and Analysis; Cross tab, Scatter Plots, Geographic Map, Pie Charts and Bar Charts, Page Trails and Density Chart.

Familiar with both Agile and Waterfall methodologies, adapting to project-specific needs, and ensuring efficient project management, collaboration, and delivery.

Adept at leveraging cloud services such as AWS (EC2, S3, Lambda) and Azure for scalable data storage, processing, and analysis, resulting in cost-effective solutions.

Experienced in managing various databases including MySQL, PostgreSQL, Oracle, and MongoDB, ensuring efficient data retrieval, storage, and querying.

Skilled in containerization with Docker and orchestration with Kubernetes, streamlining deployment and management of data analysis environments.

SKILLS

Programming Languages:

Python, Java, SQL

Libraries:

NumPy, Pandas, Scikit Learn, Keras, Pyspark, Tensor Flow, SciPy

Database:

MySQL, PostgreSQL, Oracle, MongoDB

Visualization Tools:

Power BI, Tableau, MS Excel

Analytical Skills:

Data Mining, Data Cleansing, Statistical Analysis, Data Visualization, Text Mining

Methodologies:

Agile, Waterfall

Clouding:

AWS( EC2, S3, Lambda), Azure

Others:

Google Colab, Visual Studio Code, Git, Pip, PyCharm, Docker, Kubernetes, GitHub

Operating System:

Windows, MacOS, Linux

EDUCATION

Master of Science, Computer Science 2023

University of North Texas

Bachelor of Technology (Electrical and Electronics Engineering) 2020

JNTUK University

EXPERIENCE

Data Analyst Aug 2023 – Current

BCBS TX

Utilized Python and advanced text mining techniques to analyze customer feedback, resulting in a significant 15% increase in customer satisfaction by proactively addressing pain points.

Developed advanced data visualizations using libraries like Matplotlib and Seaborn, effectively conveying insights to both technical and non-technical stakeholders, leading to improved understanding and informed decision-making.

Engineered and fine-tuned machine learning models using Python libraries, including Scikit-Learn, Tensor Flow, and Keras, achieving exceptionally high predictive accuracy and superior model performance, contributing to more accurate predictions.

Employed statistical analysis to identify pivotal market trends, leading to the formulation of data-driven marketing strategies that resulted in an impressive 18% boost in ROI, demonstrating the direct impact of data-driven decision-making.

Integrated ETL workflows seamlessly with data warehouses, data lakes, and cloud storage solutions, facilitating uninterrupted data flow and accessibility throughout the organization, enhancing overall data efficiency.

Designed interactive and visually appealing Power BI reports and dashboards, incorporating advanced features such as drill-through, bookmarks, and custom visuals, resulting in engaging and informative data presentations for stakeholders.

Developed and optimized SQL queries for real-time analytics, resulting in a remarkable 30% reduction in query execution time, significantly improving operational efficiency and reducing wait times.

Successfully implemented MongoDB for storing and analyzing unstructured data, augmenting the organization's ability to extract insights from diverse data sources, expanding data capabilities and insights.

Skillfully adapted the Waterfall methodology for regulatory compliance projects, ensuring the precision and traceability of data analysis results, contributing to meticulous compliance and accuracy.

Leveraged AWS Lambda for automating data processing tasks, leading to a substantial 15% reduction in operational costs and enabling real-time data analysis, demonstrating cost-efficiency and agility in data processing.

Data Analyst Sep 2019 – Nov 2021

KPMG India

Incorporated advanced statistical analysis techniques and custom Python scripts into Tableau to enhance the sophistication of statistical modeling within dashboards, resulting in more insightful visualizations and a 20% increase in data visualization efficiency.

Leveraged NumPy and Pandas for conducting comprehensive analyses of experimental data, A/B tests, and hypothesis-driven investigations, yielding data-driven recommendations and contributing to a 10% improvement in decision-making accuracy.

Instituted robust data governance practices within Tableau, encompassing data source certifications, data lineage tracking, and precise access control, which ensured data security and compliance with regulatory requirements, achieving a 98% compliance rate.

Implemented stringent SQL-based security measures, including user role definition and access permissions, fortifying data protection and ensuring regulatory compliance, resulting in a 99.9% data security compliance rate.

Orchestrated the design and implementation of normalized database schemas in MySQL, diligently applying constraints to preserve data integrity, which led to a 15% reduction in data redundancy.

Prepared textual data for sentiment analysis using meticulous techniques such as tokenization, stemming, and stop-word removal, refining data quality for analysis and improving sentiment analysis accuracy by 12%.

Engineered table partitioning and table inheritance in PostgreSQL, optimizing data storage and query performance, particularly for extensive datasets, resulting in a 30% reduction in query execution time for large datasets.

Developed robust error-handling mechanisms and logging procedures within ETL workflows, swiftly identifying and resolving data quality issues, upholding data integrity.

Employed Tableau Prep for data preparation tasks, encompassing data cleansing, shaping, and profiling, ensuring data quality and accuracy in preparation for analysis.

PROJECTS

Bank Enterprise Database - Jan 2022 – May 2022

Designed ER schema diagram and mapped relational database schema.

Programmed Python codes for creating and loading inputs to the database and SQL queries for processing the banking system.

UCI seed categorization - Jan 2022 – Mar 2022

Implemented Hierarchical Clustering algorithm from scratch.

Clustered the UCI seed dataset by dividing it into cluster groups and by using cluster IDs as labels for subsequent K nearest neighbor classifiers to identify the seed species.

The result determines a good number of clusters and the similarity between clusters and data points.

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