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QUALIFICATIONS

Education
 

  • Northeastern University, D'Amore McKim School of Business                                                                             Sep 2023 - Dec 2024

        Master of Science in Business Analytics

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  • SRM Institute of Science and Technology                                                                                                                      Jul 2019 - May 2023

        Bachelor's degree in Computer Science Engineering specialization Big Data Analytics

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  • ​National University of Singapore                                                                                                                                        Jun 2021 - Sep 2021

        Summer Program in Data Analytics using Deep Learning

Here are my Credentials. Please take a look

Certifications

  • Certified Business Analysis Professional (CBAP)

  • SAS Visual Business Analytics Professional

  • Data Visualization and Communication using Tableau - Duke University

  • Machine Learning - Stanford University

  • Oracle Database Foundations

  • IBM Enterprise Design Thinking Practitioner

  • Project Management Essentials Certified (PMEC)

Experience

​Expedia Group                                                                                                                                                                                   Apr 2024 - Jul 2024

Competitive Intelligence Analyst Extern                                                                                                                         Boston, Massachussetts

  • Conducted trends analysis on digital advertising across the Asia-Pacific region, using python and data mining practices, contributing in 25% increase in ad revenue by tapping into emerging markets

  • Using data analytics, identified the top retail media networks (RMNs) and countries in the region with either evolved or emerging markets for the industry.

  • Scraped and integrated data from 4 major competitors using ETL tools, and managed staging area loading

  • Applied OLAP methodologies to forecast their prospective market share in the region, leveraging Tableau and Python

  • Recommended solutions resulting in an 18% increase in revenue, involving pricing strategies and targeted initiatives

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​Energy Innovation Capital                                                                                                                                                              Feb 2024 - Apr 2024

Data Analytics and VC Industry Research Extern                                                                                                        Boston, Massachussetts

  • Extracted and preprocessed data from 7 sources on the Geothermal Technology market using SQL and Python on the Geothermal Technology market by assessing the impact of current market trends

  • Recognized USPs and key investors of 5 emerging startups, and 5 established companies, and conducted SWOT Analysis of the market against possible alternatives like traditional energy sources and other renewable sources

  • Projected finances and profitability of SensorEra, a pre-seeded company, and gathered market and financial insights for the next 5 years after analyzing their product maturity

  • Presented 3 Investment Summaries, who are clients of EIC, using Tableau and PowerPoint

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Hewlett-Packard-Enterprise                                                                                                                                                         Jun 2021 - Sep 2021

Data Scientist Intern                                                                                                                                                           Singapore City, Singapore

  • Conducted in-depth analyses on datasets used in deep learning applications.

  • Benchmarked baseline models to assess network performance, focusing on data optimization and hyperparameter tuning. Also developed data-driven business solutions for the company.

  • Developed AI Solutions for the company including a Sign Language-to-Text Conversion System, using a 2D convolutional neural network, achieving 89% accuracy

  • Performed feature engineering from a self-augmented dataset containing 1000 images of 27 different signs

  • Applied a Gaussian Blur filter and enhanced image processing accuracy by 21%, by adding a layer to the model, to differentiate between similar looking signs

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Team 1.618                                                                                                                                                                                              Jan 2020 - Aug 2021    User Interface Developer/App Developer Team Lead                                                                                           Chennai, Tamil Nadu, India

  • Led a team of 8 members to develop a data acquisition application, that displayed various features of our vehicle, like Speed, Acceleration, Temperature, Range, at real time, resulting in a 15% decrease in total energy usage, and 20% increase in lap time efficiency

  • Strategized acquisition and implementation of 200 sensors and managed data storage capabilities of sensor data

  • Utilized the sensor data to develop a Vehicle Dashboard, using Grafana and InfluxDB, and collaborated with the data science team to deploy a flag detection system, to increase vehicle efficiency

Projects

Business Intelligence
British Airways Review - Tableau
Data Professionals Survey - PowerBI

  • Developed a highly interactive Tableau dashboard, allowing users to seamlessly toggle between metrics such as overall ratings, cabin staff service, food, and entertainment ratings

  • Implemented dynamic filters that enable users to drill down into specific data points by date, traveler type, aircraft, and continent.

  • Integrated geographical data to provide an interactive map feature that allows filtering reviews by countries.

  • Demonstrated a realistic data analysis workflow and added detailed tooltips and summary to enhance the user experience

 

Visualization Link

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  • Leveraged survey data from 630 data professionals and collected diverse insights, including job titles, salaries, programming languages, and demographics.

  • Utilized Power Query for data cleaning and transformation​

  • Created various visualizations including clustered bar charts, gauges, and tree maps.

  • Visualized average salaries by job title and favorite programming languages among respondents.

  • Integrated multiple visualizations into a cohesive dashboard and enabled easy filtering of data by country for deeper insights.

  • Utilized gauges to measure respondents' satisfaction with work-life balance and salaries.

  • Analyzed key metrics such as average salary by job title and programming language popularity

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Cloud Computing
RedFin Housing Market Data - 
Amazon Elastic MapReduce
Amazon Best Selling Products - Amazon S3 and QuickSight

  • Configured an EMR cluster with appropriate instance types for primary, core, and optional task nodes, ensuring the cluster is ready for data processing tasks.

  • Established two S3 buckets: one for storing raw data and another for storing transformed data, ensuring organized data management.

  • Set up a Jupyter Notebook within EMR Studio, enabling the use of PySpark for writing and executing data processing scripts.

  • Use Bash commands within the Jupyter Notebook to fetch data from Redfin, storing it in the raw data S3 bucket for further processing.

  • Loaded the raw data into the Jupyter Notebook using PySpark, perform necessary transformations like dropping null values and extracting specific columns.

  • Loaded the transformed data back into the S3 bucket in Parquet format, ensuring efficient storage and retrieval for future use.

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  • Create a data visualization dashboard using Amazon QuickSight to analyze a dataset of 50,000 best-selling Amazon products

  • Utilized Amazon S3 for data storage, Amazon QuickSight for data visualization, and Bright Data as the data source

  • Generated various visualizations to explore data attributes such as brand popularity, product prices, and seller information.

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  • Developed an automated malaria screening system to aid in effective diagnosis, particularly in rural areas.

  • Utilized Convolutional Neural Networks (CNN) and Vision Transformers to classify cell images .

  • Extracted features from cell images, classifies them with high accuracy, and reduces over-fitting through data augmentation, Dropouts, and Batch Normalization.

  • Compared models using ROC curves, classification reports, and confusion matrices.​

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  • Source Code

Malaria Cell Detection System
Machine Learning
Face Detection using PCA

  • Preprocessed the images by resizing and normalizing to ensure consistency and improve model performance.

  • Extracted principal components to capture the most significant features of the facial images.

  • Trained a classifier using the principal components as features. Evaluated the model’s accuracy and fine-tuned it for optimal performance.

  • Used performance metrics such as precision, recall, and F1-score to measure the effectiveness of the face detection model.

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Source Code

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Other ML  Projects

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