SAI KIRAN AKULA

Dynamic and detail-oriented data analyst with a strong foundation in data analysis and problem-solving, eager to advance expertise in the field. Possesses a solid understanding of data management principles, complemented by proficiency in Python and SQL. Committed to leveraging analytical and technical skills to drive impactful insights and support data-driven decision-making. Enthusiastic about continuous learning and professional growth within a collaborative environment.

• Introduction to Data Analysis using Microsoft Excel
• Overview of Data Visualisation
• Time Series Data Visualisation and Analysis Techniques
• Advanced Relational Database and SQL
• Getting Started in Google Analytics
• Introduction to Business Analysis Using Spreadsheets

Flight Delay Analysis & Operational Performance Optimization

• Conducted large-scale data acquisition and analysis using Python (Pandas) on flight and airport data to assess industry-wide operational efficiency.
• Identified key operational bottlenecks by analyzing delay trends, peak traffic periods for busiest airports, and historical airline punctuality rates.
• Generated a comprehensive suite of data visualizations (Matplotlib, Seaborn) to uncover actionable insights regarding seasonal flight volumes and areas for potential scheduling and resource optimization.
• Skills Demonstrated: Python (Pandas), Data Analysis, Data Visualization, Business Reporting, Statistical Modeling.

Content Strategy & Audience Engagement Comparative Analysis (Netflix vs. YouTube)

• Executed a comparative analysis of two distinct large-scale media datasets (Netflix and YouTube) to identify patterns in content consumption, genre performance, and audience engagement metrics.
• Conducted robust Data Preprocessing and Exploratory Data Analysis (EDA) using the Python stack (Pandas, Matplotlib, Seaborn, Plotly) to ensure data integrity and uncover latent content trends.
• Generated actionable insights for content acquisition and media strategy by visualizing and contrasting top-performing genres, trending content velocity, and key engagement indicators across both platforms.
• Skills Demonstrated: Python (Pandas), Data Visualization (Matplotlib, Plotly), EDA, Comparative Analysis, Content Strategy, Trend Identification.

RCNN Deep Learning Model for Image Segmentation (COCO-2017)

• Engineered and deployed a Region-based Convolutional Neural Network (RCNN) to perform instance segmentation across four complex object classes (cake, car, dog, person) from the large-scale COCO-2017 dataset.
• Managed the full model lifecycle, including Exploratory Data Analysis (EDA), dataset annotation verification, iterative model training, and performance evaluation within a high-performance compute environment (Google Colab).
• Optimized the segmentation accuracy and generalization of the model on a custom subset by applying advanced data augmentation and rigorous hyperparameter tuning.
• Skills Demonstrated: Deep Learning (RCNN), Computer Vision, Model Training & Evaluation, Advanced Data Preprocessing, Python, TensorFlow/PyTorch.

Deep Learning Financial Time Series Forecasting: Apple Stock

• Designed and implemented a Long Short-Term Memory (LSTM) recurrent neural network to predict short-term and long-term price trends for Apple stock.
• Managed end-to-end data pipeline, including feature engineering for sequence data, rigorous data preprocessing, and advanced hyperparameter optimization to maximize model stability.
• Quantified forecasting accuracy against established benchmarks (e.g., Simple Moving Average or ARIMA), demonstrating the LSTM's superiority for capturing non-linear market dependencies.
• Created dynamic visualizations to illustrate predicted vs. actual prices, providing clear, data-driven insights to support potential investment strategies.
• Skills Demonstrated: Deep Learning (LSTM), Python, Time Series Analysis, Model Optimization, Financial Data Analysis.

• Passionate about football and cricket analytics, often tracking player performance metrics and reviewing game-time strategy. This is a practical application of data analysis and performance measurement principles.
• Dedicated to playing the guitar, which showcases precision, focus, and the discipline required for iterative practice and mastery, qualities essential for complex coding and technical work.

• Organised and won a charity cricket tournament in Canary Wharf, raising £1,302 for Ellenor Charity.
• Strong teamwork, communication, and leadership skills through academic collaborations and volunteering.

• Resolved product issue through consumer testing.
• Used Microsoft Excel to develop inventory tracking spreadsheets.
• Reduced reporting time by 60% by automating weekly sales data extraction using Advanced SQL queries and Python scripts.
• Documented and resolved a recurring data integrity issue in the sales database, which led to 98% confidence in end-of-day financial reporting.