Data Analyst Power Bi

SUCHITRA PRASAD

E-mail: ********.********@*****.***

Professional Summary:

Analytical and detail-oriented Data Analyst at NumPy Ninja, specializing in Tableau and Power BI for dynamic data visualization and insightful reporting. Skilled in SQL and PostgreSQL for optimizing database performance and extracting meaningful trends, particularly in healthcare and business datasets. Experienced in developing compelling dashboards that drive data-driven decision-making. Technical Skills

● SQL, PostgreSQL, PgAdmin – Table Definition, ER Diagram, CRUD, Stored procedures, Triggers, Window functions, and database performance tuning

● Tableau – Creating dynamic dashboards, treemaps, bullet charts, and healthcare data visualizations

● Microsoft Power BI - Data extraction for dashboards & facilitating an improvement in report generation.

● Python – Data manipulation and algorithm development (from your hackathon experience)

● Data Analysis – SEPSIS tracking, maternal health calculations, and healthcare data modeling Achievements

● SQL Hackathon – Competed in query optimization and database problem-solving challenges.

● Python Hackathon – Worked on real-world coding tasks and algorithmic problem-solving.

● Publications

o https://www.numpyninjaacademy.com/post/how-to-create-a-visually-appealing-donut-chart-in-t ableau-a-comprehensive-guide

o https://www.numpyninjaacademy.com/post/understanding-postgresql-database-crud-operations

-with-acid-transaction-control-principles

o https://www.numpyninjaacademy.com/post/how-to-make-tableau-dashboard-more-interactive- with-dynamic-dimensions

QUALIFICATION

1.Bachelor of Engineering in Electronics & Instrumentation - R.G.P.V University, Bhopal (2009) WORK EXPERIENCE

NumPy Ninja,USA

Data Analyst

Jan 2025 – Present

● Worked with clinical dataset for designing a KPI dashboard (Length of stay, throughput etc.) for ICU Sepsis patients using Tableau calculated field, Level of Detail (include, exclude, fixed), filters, parameters, sets which will help to improve ICU operational efficiency by 30%.

● Determined SIRS using advanced Tableau visualization feature and created a dashboard for the identified SIRS patients' using vitals - HR, temperature, Respiration and clinical parameters – PaCo2 and WBC, thus helping the stakeholders to identify and monitor patients more susceptible to the further stages of Sepsis.

● Analyzed the role of demographic parameters with respect to sepsis which helped in inferring that patients in the higher age bracket (65+) especially males have a longer length of stay and are more vulnerable to sepsis as compared to females.

● Identified the dominant biomarkers - WBC, Platelets, base Excess and lactate which plays a significant role in the incidence of Sepsis by using concepts of advanced Tableau like Dual axis, Blended axes, control charts, sparkline charts, funnel charts, pareto charts, correlation charts in addition to basic charts like Bar plots, Box plots, scatter plots, heat map and thus requires regular monitoring by the clinicians.

● Used python pandas / numpy library for data cleaning, data wrangling and missing data analysis.

● Adept in Python visualization libraries like matlplotlib and seaborn for deriving insights through exploratory data analysis.

● Data preparation from relational database using postgreSQL by using SQL queries.

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