Data Analyst Public Health

Nagaphani Aakash Guduri

DATA ANALYST

VA, USA 551-***-**** **********@*****.*** LinkedIn

SUMMARY

Data Analyst with 3+ years of experience in predictive modeling, statistical analysis, and data visualization. Proficient in SQL, Python, Power BI, and Tableau. Adept at transforming complex data into actionable insights that improve operational efficiency, reduce response times, and optimize decision- making. Strong communication, problem-solving, and collaboration skills, with a proven ability to work effectively with cross-functional teams. Known for attention to detail, adaptability, and delivering high-quality results in fast-paced environments, enhancing both public health and business performance. SKILLS

Methodologies: SDLC, Agile, Waterfall

Languages: Python, R, SQL

IDEs: Visual Studio Code, PyCharm, Jupyter Notebook Packages: NumPy, Pandas, Matplotlib, SciPy, Scikit-learn, TensorFlow, Seaborn, dplyr, ggplot2, Keras Visualization Tools: Tableau, Power BI, Advanced Excel (Pivot Tables, VLOOKUP) Cloud Technology: Amazon Web Services (AWS)

Database: MySQL, SQL Server, PostgreSQL, MongoDB, Oracle Other Skills: SSIS, SSRS, ETL, Machine Learning Algorithms, Probability distributions, Confidence Intervals, ANOVA, Hypothesis Testing, Regression Analysis, Linear Algebra, Advance Analytics, A/B Testing, Data Mining, Data Visualization, Data warehousing, Data transformation, Data Storytelling, Association rules, Clustering, Classification, Regression, A/B Testing, Forecasting & Modelling, Data Cleaning, Data Wrangling, Jira, Git, GitHub Operating System: Windows, Linux, Mac OS

EDUCATION

Master of Science in Information Systems - George Mason University, Virginia, USA Bachelor of Technology in Computer Science and Engineering - Jawaharlal Nehru Technological University, Hyderabad, India WORK EXPERIENCE

Data Analyst CVS Health, VA Aug 2023 – Present

• Developed a comprehensive predictive model to analyze public health data, identifying trends and predicting potential disease outbreaks, ultimately enhancing public health response efforts and resource allocation by 25%.

• Analyzed a dataset of over 1 million public health records using statistical methods (regression analysis, time series forecasting), uncovering trends and risk factors to reduce outbreak response times by 20%.

• Built and optimized an ETL pipeline to automate the extraction of data from multiple health information systems, transform it for analysis, and load it into a centralized data warehouse, improving data accessibility and analysis efficiency by 40%.

• Utilized SQL on AWS RDS to perform complex queries and extract data from 5+ sources, ensuring the accuracy, relevance, and timeliness of datasets for predictive modeling.

• Employed Python (Pandas, NumPy) to automate data cleaning processes, improving data integrity by 30% through efficient handling of missing values and outliers.

• Created interactive Power BI dashboards to visualize KPIs related to disease trends, enhancing stakeholder engagement and increasing decision-making efficiency by 30%.

• Designed and deployed predictive analytics models (logistic regression, decision trees), achieving 85% accuracy in forecasting disease outbreaks, leading to a 15% reduction in potential cases.

• Collaborated with 10+ public health officials to integrate model insights into workflows, reducing response times during outbreaks by 15% and improving overall public health readiness.

Data Analyst Accenture, India Jul 2020 – Jul 2022

• Conducted a comprehensive analysis of the financial impact of supply chain disruptions on overall business performance, quantifying losses and identifying key factors influencing operational efficiency.

• Developed interactive dashboards in Tableau to enable stakeholders to explore financial data dynamically, facilitating deeper insights into cost implications and recovery strategies.

• Defined key performance indicators (KPIs) within an OLAP model to track essential metrics such as total disruption costs, recovery time, and revenue impact, improving data visibility by 30% and facilitating quicker decision-making.

• Performed feature engineering to identify and create relevant features from supply chain data, including supplier reliability, lead times, and inventory levels, enhancing model accuracy by 20% and improving prediction reliability by 15%.

• Utilized Python libraries (Pandas, NumPy) for data cleaning and preprocessing, effectively handling missing values and outliers, which improved data integrity by 30% and enhanced analysis accuracy.

• Developed complex SQL queries to perform aggregations and summarizations, calculating key metrics such as average disruption costs and potential lost revenue, improving reporting efficiency by 40% and reducing data processing time by 25%.

• Established clear objectives for A/B testing to compare the financial effectiveness of different mitigation strategies during supply chain disruptions.

• Conducted a cost variance analysis to identify discrepancies between projected and actual costs during disruptions, enabling more accurate forecasting and budget adjustments.

• Collaborated with cross-functional teams to develop actionable insights and recommendations, leading to the implementation of effective strategies that reduced the impact of future supply chain disruptions by 15%.

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