• Data Collection and Wrangling: Increased the data ETL efficiency by 5% through building APIs to collect structured and unstructured data from various clients’ websites and external files, and conducting data queries to organize, clean and analyze data with Python and SQL.• Data Analysis and Visualization: Developed SQL queries to organize and reshape the database up to 5GB, and designed the 20+ dashboards and reports with Power BI and Excel. • Predictive Modeling: Reduced the forecast error by 20% and increased sales by 5% for a retail store via developing and productionizing end-to-end machine learning models to forecast the demand for products and case scenario analysis.• Strategic Data Analysis: Designed and deployed statistical data pipelines and regression models to compare the characteristics of apps and provide strategies to optimize the app functions by analyzing customers’ preference and implementing case scenario prediction using Python and SQL.• API Design and Model Productionizing: Increased product requirement checking efficiency by 20% through converting machine learning/deep learning models into APIs for developer users and designing a Web app to check the products’ numbers, characteristics, and price.

• Large Scale Data Analysis and Optimization: Conducted machine/deep learning and data pipelines to analyze wind/solar energy datasets of up to 8 GB to accelerate investment recovery using SQL, Excel, and Python. • Predictive Modeling: Developed and productized coupled drift-diffusion and Poisson models with novel non-linear regression packages to optimize solar energy conversion processes, leading to 20% increase in solar panel performance.• API Design and Data Wrangling (anomaly detection): Increased the data filter accuracy by 5% through building unique data pipelines to detect and modify outliers and anomalies in transactional data with Python.• Project Management: Organized 20+ inner- and intra-university seminars to present the Excel and Power BI dashboards/reports to stakeholders while ensuring fulfillment of set objectives.• Published a book and 5 peer-reviewed papers and presented findings at national and international conferences.

• Large Scale Data Collection: Developed the Python APIs to collect raw data from websites and external files, and created a data warehouse using SQL and Excel to store and organize datasets of 5GB.• Data Wrangling: Increased the data wrangling efficiency by 5% through modifying and troubleshooting the SQL queries. • Time Series Modeling: Increased sales by 10% via design of time series models to analyze and predict customers’ requirements with Python.• Statistical Analysis and Neural Network Analysis: Increased the order identification accuracy by 20% via developing and maintained a classification pipeline with CNN models to evaluate product data with Python.• Communication and Organization: Designed the Power BI/Excel/Access dashboards and reports, and planned and organized well-attended seminars to present analytical findings to business stakeholders, managers, and colleagues that enabled informed decision-making.

• Satisfying Stakeholder Requirements: Planned and executed undergraduate experimental projects, facilitating teaching laboratory courses, and implementing linear/polynomial regression and judgmental forecasting models to analyze user testimonials that improved electrical devices’ functions and met customers’ requirements.• Analytical Insights: Examined experimental results and identified key insights, contributing to development of groundbreaking research projects at the University of Science & Technology Beijing.

• Predictive Analysis: Developed and Deployed advanced forecasting and time series models to analyze large datasets of high-pressure measurement system electrodes, proposing enhanced methodologies and optimizing electrode performance.• Communication and Organization: Planned and organized well-attended seminars to present analytical findings to business stakeholders, colleagues, and supervisors that enabled informed decision-making.