Hi, I'm Sheng Quan. I am an Analyst Programmer with a strong expertise in full-stack programming, data analysis & engineering, and AI technologies. Experienced in building scalable web applications, automating data pipelines, and applying machine learning for practical insights. Proficient in designing and implementing program to solve real-world problems, and supported by hands-on experience in both the Australian and Chinese industries.
Extracting actionable insights from complex datasets using descriptive and diagnostic analytics techniques
Building full-stack project with Python (analytics) + SQL (databases) + JavaScript/HTML (showcases)
Developing predictive models and pattern recognition systems using supervised or unsupervised learning algorithms
Professional Practice: Gaining practical workplace experience in the Australian ICT industry. Developed strong professional communication, teamwork, and problem-solving skills while adapting to real-world business environments and industry standards.
Data Management & Analytics: Experienced in managing complex datasets and transforming raw information into actionable insights. Skilled in cleaning, organizing, and analyzing data to support decision-making, with hands-on practice in building dashboards and applying machine learning models to solve real-world problems.
Advanced Computer Science: Solid foundation in core computer science principles, with practical experience in designing efficient algorithms, implementing data structures, and developing full-stack web applications. Skilled in modern software development practices, from coding and testing to deployment and maintenance, ensuring scalable and reliable solutions.
Handled office coordination by managing digital check-ins, directing inquiries, and streamlining workflows. Provided frontline IT support for 50 employees and collaborated with vendors to resolve issues. Implementing company-wide device deployment including setup, data migration, and inventory tracking. Produced, filmed, and edited training videos to improve onboarding for warehouse staff.
Applied Ababila's AI model to generate role-specific reference answers, aligning with job profiles and integrating results into a structured Q&A database. Cleaned and transformed over 20,000 unstructured interview questions using Python and regex to create structured, analyzable datasets. Conducted overlap analysis of internal and external job listings using NLP techniques to assess skill and compensation requirements, benchmarking against talent market trends. Built Python scripts (BeautifulSoup and Scrapy) to automate data collection, scraping 10,000+ job postings from 3+ platforms.
Programming Tutor at a children's education institution, teaching Python to young learners in an engaging and accessible way. Designed interactive lessons to build foundational programming knowledge, spark interest in coding, and develop logical thinking skills. Focused on hands-on activities and simple projects to help students understand core concepts like variables, loops, and conditionals, while encouraging creativity and problem-solving.