Analyzing Visualizing Storytelling
MSc Student at Coventry University (Graduating Sept 2026)

Ramya Kalakonda

Transforming complex data into actionable insights. Aspiring Data Analyst specializing in predictive modeling and visual storytelling.

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SQL / PYTHON
VISUALIZATION
STATISTICS
Ramya Kalakonda

4

End-to-End
Data Projects

Curious by Nature,
Analytical by Choice.

Hi, I'm Ramya. I'm a Data Analyst with a passion for uncovering hidden patterns within complex datasets. Currently pursuing my MSc at Coventry University, I bridge the gap between technical data science and business decision-making.

My approach combines rigorous statistical methodology with intuitive visual design. I believe that data is only as good as the stories it tells, which is why I focus on creating high-impact visualizations and clear, actionable summaries for stakeholders.

Data Modeling

Expert in SQL and database structuring for efficiency.

Exploratory Analysis

Deep dives into raw data to find growth levers.

Technical Expertise

Programming Languages

PythonRSQLC++

Data Visualization

TableauPower BIMatplotlibSeaborn

Statistical Analysis

RegressionHypothesis TestingData ModelingProbability

Tools & Technologies

Advanced ExcelJupyter NotebookGitPandasNumPy

Databases

MySQLPostgreSQLMongoDB

Soft Skills

Problem-solvingCommunicationData StorytellingTeamwork

Academic & Project Experience

Big Data Analytics Coursework

Coventry University · Module 7006SCN Coventry, UK
2025 - 2026
  • Built four independent end-to-end distributed PySpark pipelines as part of the MSc Big Data Analytics module, each processing datasets ranging from 17M to 40M+ records.
  • Engineered reusable ML pipelines (StringIndexer, OneHotEncoder, VectorAssembler, StandardScaler) and benchmarked Logistic Regression, Decision Tree, Random Forest and Gradient-Boosted Trees with cross-validated tuning.
  • Reached 95.1% accuracy and a 0.97 AUC-ROC classifying NYC taxi trip duration with Gradient-Boosted Trees, the best result across all four coursework projects.
  • Profiled distributed execution via Spark UI (caching, repartitioning) and communicated findings through SHAP explainability and Tableau dashboards.

Education

2024 – 2026 (Expected)

MSc in Data Science & Analytics

Coventry University

Coventry, UK

Focusing on Advanced Machine Learning, Big Data Science, and Statistical Methods.

Developing a final thesis on predictive maintenance using deep learning architectures.

2019 – 2023

B.Tech in Computer Science and Engineering

Narayanamma Institute of Technology and Science (GNITS)

Hyderabad, India

Specialized in Artificial Intelligence and Database Management Systems.

Graduated with First Class with Distinction.

Active member of the Data Science Student Chapter.

Data Projects

01

NYC Taxi Trip Duration Classification

Built a distributed PySpark pipeline on 17.6M+ NYC Yellow Taxi trips to classify long vs. short journeys before a ride even ends, benchmarking four MLlib classifiers.

PySparkPythonMLlibTableau
Gradient-Boosted Trees hit 95.1% accuracy and a 0.97 AUC-ROC, the strongest result across all four models tested.
Code base
02

NYC Yellow Taxi Fare Prediction

Engineered an end-to-end PySpark pipeline over six months of NYC Yellow Taxi trip records to model fare amount, from raw ingestion through automated Tableau-ready exports.

PySparkPythonMLlibTableau
Benchmarked four MLlib classifiers and generated 30+ evidence figures feeding four live Tableau dashboards.
Code base
03

NOAA Global Surface Weather Analysis

Processed 40M+ global weather station-day records (1929-2024) in a distributed PySpark pipeline to flag extreme-weather days from historical NOAA GSOD data.

PySparkPythonSHAPTableau
Benchmarked four MLlib classifiers with cross-validation, using SHAP to explain the features behind each prediction.
Code base
04

Flight Cancellation Risk Prediction

Developed a distributed PySpark classification pipeline on U.S. BTS on-time performance data to predict whether a scheduled flight will be cancelled before departure.

PySparkPythonMLlibClassification
Compared four MLlib models to flag high-risk flights, supporting airline disruption management and resource planning.
Code base

Get In Touch

I'm currently looking for data analyst opportunities. Whether you have a question or just want to say hi, I'll try my best to get back to you!