Nishok Ilangovan

Passionate about uncovering patterns and delivering actionable insights.

Python • SQL • Snowflake • Power BI • Neo4j

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About Me

Over three years of experience in data analytics and machine learning
Worked across insurance, research, education and retail domains
Build dashboards, automate pipelines and develop predictive models
Proficient in Python, SQL, Snowflake and Power BI
Passionate about turning complex datasets into clear stories and collaborating with teams

Experience

Data Analyst

StableCoupons Inc.

Jan 2025 – Present

Built interactive dashboards using Tableau and Power BI to monitor marketing campaign performance.
Designed and maintained Snowflake data warehouses to support enterprise analytics.
Developed SQL and Python scripts to automate data collection and reporting processes.
Collaborated with product and marketing teams to deliver data‑driven insights for decision making.

Data Analyst

JA ASSURE

Jun 2024 – Dec 2024

Built machine learning models for risk assessment and claims analysis.
Performed model selection and hyper‑parameter tuning to improve predictive accuracy.
Communicated analytical findings to stakeholders via dashboards and presentations.

Business Analyst Intern

University of Maryland

Jan 2024 – Dec 2024

Analyzed business processes and identified areas for improvement within academic departments.
Created system requirements and technical specifications for new initiatives.
Worked closely with faculty and administrators to streamline workflows and improve efficiency.

Data Analyst

InterviewDesk

Jan 2024 – Jul 2024

Developed data pipelines using Python, Django and Node.js to collect and process candidate data.
Created dashboards and reports to track recruitment metrics and candidate engagement.
Applied machine learning techniques to enhance candidate matching algorithms.

Business Data Analyst

WNS Global Services

Oct 2021 – May 2023

Analyzed large datasets to provide business insights for clients in various industries.
Developed ETL processes and interactive dashboards to support decision making.
Presented data‑driven recommendations to senior leadership.

Data Analyst Intern

Genik Technologies

Aug 2021 – Oct 2021

Worked on deep learning and computer vision projects using convolutional neural networks.
Prepared and labeled datasets for model training and evaluation.
Developed image classification models using Python and OpenCV.

Featured Projects

Capstone Project for Extended Studies Department, University of Maryland

Jan 2024 – Dec 2024

Automated the course renewal request process for faculty using Salesforce, addressing a 30 % increase in request volume and reducing processing time by 40 % in the first quarter.
Led system analysis by creating Data Flow Diagrams (DFD) and Entity Relationship Diagrams (ERD) to map data processes, improving data accuracy by 25 % and enabling the system to handle all requests.
Produced detailed system specifications and Salesforce integration strategies, reducing processing errors by 30 % and doubling request handling capacity.
Implemented a new information system on time with 100 % of requirements met, increasing faculty satisfaction scores by 20 %.

ScrumKanbanJiraSalesforceSystem Analysis

Video Games Sales & Success Prediction using PySpark

Jan 2024 – Mar 2024

Executed comprehensive feature engineering on Steam video game data for predictive modeling.
Developed a predictive model with 81 % accuracy using Random Forest and Spark GraphFrames.
Applied GraphFrame analysis to identify relationships between games, developers and genres, improving recommendations for marketing strategies and inventory optimisation.

PySparkRandom ForestGraphFramesModel Building

Crime Analysis and Projection of Crime Prevention Costs in Los Angeles

Aug 2023 – Dec 2023

Scraped crime data from the state website of California; cleaned, processed and merged datasets for analysis.
Used pandas to analyse crime statistics and identify key trends driving crime rates in Los Angeles, producing a detailed report.
Created visualisations with Plotly, Folium, Matplotlib and Seaborn, revealing a 15 % increase in property crimes and a 10 % decrease in violent crime rates over the past decade.
Trained predictive models using XGBoost and linear regression to forecast crime prevention costs, accurately predicting required budget increases to maintain reduction rates.

PandasScikit‑LearnXGBoostData VisualisationPlotly

Smith Seekers Inc Research Database

Aug 2023 – Nov 2023

Extracted and compiled data from ranking websites to analyse program ranking trends for a graduate business school.
Built a search‑engine‑style database for prospective students, compiling program details such as tuition, credit requirements and duration.
Designed entity‑relationship diagrams and database schemas using Lucidchart and implemented queries with SQL.

SQLOracleMySQLDatabase Design

Image Classification of Dry and Wet Soil

Dec 2022 – May 2023

Developed a convolutional neural network with OpenCV to classify soil images into wet and dry categories for agricultural applications.
Achieved 92 % accuracy in distinguishing between wet and dry soil images.
Optimised irrigation schedules in test areas, resulting in a 30 % reduction in water usage by applying model predictions.

OpenCVDeep LearningCNNPython

Predictive Analysis of Life Expectancy

Dec 2022 – May 2023

Predicted life expectancy trends from 2000 to 2015 by incorporating immunisation, economic and health data into a single model.
Used R Studio and programming for data analysis and visualisation, identifying a strong correlation (r = 0.85) between immunisation rates and life expectancy.
Integrated socio‑economic factors into the model and provided recommendations that could increase life expectancy by up to five years in some countries.

R StudioData AnalysisVisualisationMachine Learning