Data Analyst

Lakshmi Deepika Pinnamaraju

********.***********@*****.*** +1-720-***-**** Denver, CO www.linkedin.com/in/lakshmideepikapinnamaraju Summary:

Data Analyst with a Master’s in Data Science. Proficient in Python, SQL, Tableau, and Excel for data analysis, ETL processes, and machine learning. Experienced in building ETL pipelines, designing dashboards, and providing actionable insights. Skilled in model optimization, data visualization, and automating workflows for scalability and reproducibility.

Education:

Regis University – Denver, CO Aug 2023

Master of Science, Data Science GPA 4.0/4.0

Anil Neerukonda Institute of Technology and Sciences – Andhra Pradesh, India June 2021 Bachelor of Technology, Computer Science GPA 3.46/4.0 Tools and Technical Skillset:

Programming Languages: Python, R, C, C++, Java

Databases: MySQL, SQL

Data Analysis Tools: Microsoft Excel, Tableau, Power BI Work Experience:

EZ4 Technologies LLC Dec 2023 - Present

Data Analyst

Collaborated with cross-functional teams and key stakeholders to identify business challenges and translate them into actionable, data-driven insights and recommendations.

Conducted in-depth data analysis to identify emerging trends, untapped opportunities, and areas for business improvement.

Managed the extraction, transfer, and loading (ETL) of data into a centralized data warehouse from multiple sources.

Oversaw the development and maintenance of both new and existing ETL processes to ensure data integrity and operational efficiency.

Utilized SQL, Microsoft Excel, and Tableau to gather, clean, analyze, transform, and manipulate various datasets, ensuring consistency and accuracy in reporting.

Developed and maintained comprehensive documentation for databases, capturing all changes and updates.

Presented complex datasets in a clear and concise manner, utilizing data visualization tools like Tableau to effectively communicate insights to non-technical stakeholders and enhance data storytelling. Regis University, Denver May 2023 - Aug 2023

Graduate Research Assistant

Conducted research in collaboration with Simwerx and Bit Space to develop emergency medical technology for military and civilian use.

Built a preprocessing pipeline for surgical video data, leveraging Python (pandas, NumPy) for data cleaning, feature extraction, and transformation, version-controlled in GitHub for reproducibility.

Developed and optimized machine learning models (EfficientNet, MoviNet, ResNet) in TensorFlow, achieving 91% accuracy with ResNet, demonstrating a 53% improvement over EfficientNet and a 68% improvement over MoviNet.

Conducted exploratory data analysis (EDA) to uncover patterns in video data and prepared detailed reports on model performance metrics using Matplotlib and Seaborn.

Integrated real-time inference results into a database for further visualization and analysis, streamlining the deployment of insights.

Tab Square Solutions, India June 2020 - Dec 2021 Data Analyst Intern

Analyzed telemetry data from an e-commerce website, including user logins, session durations, and purchase behavior to identify patterns and optimize website performance.

Conducted exploratory data analysis (EDA) using Python (pandas, NumPy) to uncover insights such as cart abandonment and customer journey trends.

Created and maintained interactive dashboards in Tableau to visualize key metrics like sales performance, customer retention, and user engagement.

Performed funnel analysis to identify user drop-off points in the purchase process and recommended strategies to improve conversion rates.

Utilized SQL to extract, clean, and manipulate data from the website’s database, ensuring data accuracy and integrity for analysis.

Generated actionable insights for customer segmentation to support targeted marketing efforts and improve customer retention.

Assisted in A/B testing of website features, analyzing the impact of design changes and promotions on user behavior and sales.

Prepared and delivered regular reports on key performance indicators (KPIs) such as traffic, sales, and customer activity to support business decisions.

Automated data collection and processing workflows to streamline the data analysis process, ensuring timely delivery of insights.

Collaborated with cross-functional teams, providing data-driven recommendations to enhance user experience and increase website conversions.

Academic Projects:

Image Super Resolution May 2023

Python, Deep Learning

Designed and implemented deep learning models using Keras (VGG16, Functional, Sequential) to train image datasets for super-resolution tasks, enhancing low-resolution images.

Optimized model performance through advanced techniques such as learning rate scheduling, dropout, and batch normalization, achieving a 15% improvement in accuracy.

Evaluated models using SSIM, PSNR, and loss metrics, with the Sequential model achieving the highest SSIM

(0.92) and PSNR (29 dB).

Conducted data preprocessing and augmentation to prepare image datasets for training, ensuring data quality and consistency.

Visualized model performance, learning curves, and high-resolution outputs using Matplotlib and Seaborn for final model predictions.

Documented the entire workflow, including model architecture, optimization strategies, and evaluation results, in a version-controlled repository on GitHub for reproducibility.

Utilized Python libraries such as pandas and NumPy for dataset management and exploratory data analysis

(EDA) to understand data distributions and patterns.

Presented project findings through interactive visualizations and structured reports, effectively communicating results to my team members.

Modeling and Forecasting US Inflation Rate March 2023 Python, R, SQL, Tableau

Modeled U.S. inflation (1972-2022) using key economic indicators (oil prices, unemployment, federal funds rate) to forecast inflation for 2023-2024.

Performed univariate time series analysis and built predictive models using ARIMA and NNAR techniques in R.

Collected, cleaned, and merged data from multiple sources, resulting in a dataset of over 600 monthly data points using SQL for efficient data processing.

Designed interactive dashboards in Tableau to visualize inflation trends, correlations with economic indicators and model forecasts.

Ensembled and optimized ARIMA and NNAR models to improve accuracy, achieving a forecast precision of 90%.

Automated data preparation and model evaluation pipelines using Python, enhancing reproducibility and scalability.

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