Master’s data science and research student at University of Ottawa with research experience in data science and analytics. Experience in developing data engineering and analytical solutions using Python, PowerBI, Excel, …
Master’s data science and research student at University of Ottawa with research experience in data science and analytics. Experience in developing data engineering and analytical solutions using Python, PowerBI, Excel, Word, ArcGIS, Apache Airflow, AWS, Azure, MySQL, PostgreSQL, Pandas, NumPy, Google BigQuery and Google Cloud Platform. Skilled in prompt engineering and usage of applied AI (GPT-4, Bard AI, etc.). Demonstrated communication, collaboration, critical thinking, curiosity and creativity skills in sharing data findings and suggestions with peers and instructors, following ethical principles and best practices in data analysis and presentation.
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Developed and deployed a highly efficient software solution using Python and Selenium to automate the collection of Google Customer Reviews for multiple businesses and stored as Json files, resulting in a 90% reduction in data collection time.
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Enhanced data collection process by developing an efficient algorithm that successfully bypassed Google Captcha, reducing error rates by 75%.
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Optimized the pipeline process by integrating Apache Airflow and utilizing AWS EC2 hosting services, streamlining data transfer to Amazon S3 Buckets for efficient storage and subsequent data analysis. Additionally, ensured smooth operation of the code by regularly maintaining and updating it as needed.
Developed and deployed a highly efficient software solution using Python and Selenium to automate the collection of Google Customer Reviews for multiple businesses and stored as Json files, resulting in a 90% reduction in data collection time.
Enhanced data collection process by developing an efficient algorithm that successfully bypassed Google Captcha, reducing error rates by 75%.
Optimized the pipeline process by integrating Apache Airflow and utilizing AWS EC2 hosting services, streamlining data transfer to Amazon S3 Buckets for efficient storage and subsequent data analysis.
Ensured smooth operation of the code by regularly maintaining and updating it as needed.
Developed and implemented a Facial Recognition-based biometric system for an Employee Attendance Portal, utilizing machine learning library such as OpenCV and the Landmark Estimation Algorithm, resulting in a 80% accuracy rate for facial identification.
Implemented a scalable front-end using React and a back-end system using Django and PostgreSQL for data storage, resulting in improved website performance with an average response time of less than 1000 milliseconds.
Utilized PowerBI to automate the creation of reports on employee attendance patterns, resulting in improved visibility into workforce trends and enabling strategic decision-making based on real-time data.
Employed Azure Cloud Services to host the Facial Recognition system, increasing scalability and ensuring high availability with 99.9% uptime.
Utilized PowerBI to analyze procurement data and identify cost-saving opportunities, resulting in a 10% reduction in annual purchasing expenses.
Implemented a data visualization dashboard using PowerBI to track supplier performance metrics, leading to a 20% increase in on-time deliveries and a 10% decrease in defective products.
Collaborated with cross-functional teams to develop automated reporting processes using PowerBI, reducing manual data entry time by 80% and improving overall efficiency.
نظام التوظيف التشغيلي المعتمد على الذكاء الاصطناعي أولاً، الذي يوحّد ويؤتمت دورة التوظيف بالكامل للمؤسسات الحديثة حول العالم.
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