Manan Rasool

Data-driven professional with an MTech in Computer Science, specializing in transforming complex datasets into clear, actionable business insights. Backed by a strong technical foundation—including a HackerRank Golden Badge in SQL—I work extensively with Python and Power BI to deliver accurate, business-focused analytics.

Currently working as a Senior Technical Analyst at Intellipaat, where I focus on exploratory data analysis (EDA), SQL-driven data extraction, and automated reporting workflows that improve analytical accuracy and decision-making. My experience spans the full data lifecycle, from data cleaning and transformation to executive-level reporting and dashboarding.

I have delivered measurable impact through applied analytics, including engineering a sales forecasting model that achieved 97.3% predictive accuracy. I combine analytical rigor with practical execution to ensure insights are both reliable and actionable.

Core Technical Stack
• Data Analysis & BI: SQL (Golden Badge), Microsoft Power BI, Excel, KPI Reporting
• Programming: Python (Pandas, NumPy, Scikit-Learn, Matplotlib)
• Advanced Analytics: Predictive Modeling, Statistical Analysis, PCA, RFE
• Visualization: Interactive Dashboards, Seaborn, Plotly

I enjoy connecting with professionals in the Data Analytics and Business Intelligence space to exchange insights on analytics trends, predictive modeling, and data-driven problem solving.

• Universal Healthcare and Database: Developed a hospital management system to improve patient care by automating patient information collection, analysis, and retrieval. Created a COVID Tracker website and presented on hospital outpatient service.
• Exploratory Data Analysis of Hass Avocado Dataset: Conducted exploratory data analysis on avocado sales data in the USA, providing insights and predictions for the market. This project highlights expertise in data science, specifically in exploratory data analysis and sales prediction.
• Apple Sales Prediction of Kashmir using Machine Learning: Developed and evaluated machine learning models using Decision Tree, Random Forest, and K-Nearest Neighbor, achieving up to 97.3% accuracy with feature selection techniques like PCA and RFE.

COVID-19 Outbreak Cases and Healthcare Database:

• Publication: Scopus-indexed, https://ieeexplore.ieee.org/document/9445298
• Abstract: This paper details the development and deployment of a coronavirus tracker website integrated with a hospital management system. The research highlights the critical role of digital tools in optimizing healthcare administration, improving patient care, and streamlining data management during the pandemic. By leveraging web development technologies and healthcare IT systems, this paper underscores expertise in creating impactful solutions for urgent healthcare needs.

An Automatic Method to Predict the Sales of Fruits and Vegetables:

• Publication: Reviewed and accepted [Journal of Computers and Education].
• Abstract: Focused on the economic impact of predictive analytics in agriculture, this paper introduces a machine learning model designed to forecast apple sales based on variables such as climate conditions and apple varieties. The study demonstrates advanced skills in machine learning, data analysis, and economic impact assessment, offering valuable insights into improving sales forecasting accuracy and supporting agricultural stakeholders.

A Survey on WIFI 6:

• Publication: Accepted [Journal of Network and Computer Applications].
• Abstract: This comprehensive review covers the advancements in Wi-Fi 6 technology, emphasizing its enhanced bandwidth utilization and potential to revolutionize IoT and home automation. The paper positions the author as a thought leader in network technologies, showcasing an in-depth understanding of emerging trends in connectivity and their future implications.