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Deva Abhishik Reddy Janga

Data Analyst • Bikkavolu, East Godavari District, Andhra Pradesh • a**************@gmail.com • +91*******184

Career Objective

Analytical and results-driven Data Analyst (MCA 2026) with hands-on experience in Python, SQL, Power BI, and Excel. Experienced in data wrangling, exploratory data analysis, statistical modeling, and dashboard creation. Delivered end-to-end projects in demand forecasting and user behavior analysis, applying machine learning models and data storytelling to generate actionable business insights. Seeking an entry-level Data Analyst role to apply technical and visualization skills to solve business problems and support data-driven decision making.

Education

Adikavi Nannaya University
Master of Computer Applications (CGPA-8.88/10.0) • Ramachandrapuram, Andhra Pradesh • Sep 2024 – June 2026
Adikavi Nannaya University
Bachelor of Computer Science (CGPA-7.77/10.0) • Ramachandrapuram, Andhra Pradesh • Sep 2021 – June 2024

Technical Skills

Programming Languages: Python,R
Frameworks and Libraries: Pandas,NumPy,Matplotlib,Seaborn,Scikit-learn
Databases: SQL,MySQL,PostgreSQL
Data and Analytics: Linear Regression,Logistic Regression,K-Means Clustering,Decision Trees,Recurrent Neural Networks,Power BI,Tableau,Microsoft Excel,Google Sheets,Data Cleaning,Exploratory Data Analysis,Feature Engineering,A,B Testing Concepts,ETL Basics,KPI Reporting,Data Storytelling
Tools and Methodologies: Jupyter Notebook,Google Colab,Git,GitHub,VS Code

Projects

Real-Time Prediction Of Taxi Demand Using Neural Networks
Tools Used: Python, Keras, TensorFlow, PyTorch, Pandas, Feature Engineering, RNN
  • Developed a real-time taxi demand forecasting system using recurrent neural networks, achieving R² = 0.891 (reported as 89.1% accuracy) on held-out test data.
  • Engineered and preprocessed large-scale taxi trip datasets spanning 263+ city zones to create time-series features, demand aggregates, and zone-level demand signatures for model training.
  • Built and validated a deep learning pipeline using Python with TensorFlow Keras and PyTorch, implementing sequence batching, windowing, and model checkpointing to stabilize training.
  • Optimized model performance through feature selection and tuning, improving generalization and reducing forecasting error during cross-validation.
  • Designed a low-latency inference component for live predictions, targeting and achieving ~200ms latency per prediction batch for near real-time use cases.
Spotify Listening History Analysis
Tools Used: Python, SQL, Pandas, Power BI, Data Cleaning, Exploratory Data Analysis
  • Cleaned and transformed 20,000+ raw streaming records using Pandas and SQL to standardize timestamps, remove duplicates, and normalize artist and track metadata for reliable analysis.
  • Performed exploratory data analysis to identify top artists, peak listening hours, genre preferences, and session-duration patterns, informing user engagement insights.
  • Built interactive Power BI dashboards visualizing listening trends, top artists, and temporal patterns to support storytelling and stakeholder review.
  • Improved reporting efficiency by 40% through automated data pipelines and reusable dashboard components for regular analysis and sharing.

Certifications

Data Analytics Job Simulation (Forage) — Deloitte Australia • Jul 2024
Data Analysis with Python Certification — IBM Skills Network • Apr 2026
SQL and Relational Databases Certification — IBM Skills Network • Feb 2026

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