JianfengKuang

Executed Extract,Transform,and Load(ETL)processes for plastic surgeons' data, conducting
comprehensive data analysis. Performed data cleaning, including the removal and replacement of
duplications and outliers. Utilized SQL and Python for scoring analysis and geographic insights. Generated visualization reports and dashboards using Matplotlib and Seaborn libraries Provides valuable
insights for assessing the quality of physician services and patient satisfaction, enhancing data-driven
decision making. ● Successfully developed a high-accuracy stock price predicting model in Python. Implemented data
partitioning into training, validation, and testing datasets. Introduced advanced techniques such as
ARIMA, XGBoost model, and VotingRegressor to enhance the model's predictive capabilities
● Support client service strategies and management decision-making by utilizing Python and SQL to
process ride-hailing service data. Analyze the driver's lifetime value and average income per ride. Construct various dashboards and reports using Power BI, incorporating real-time refresh functions for in- depth and real-time analysis
Marketing Analyst and Administrative

<p>Executed Extract,Transform,and Load(ETL)processes for plastic surgeons' data, conducting comprehensive data analysis<br>
Performed data cleaning, including the removal and replacement of duplications and outliers<br>
Utilized SQL and Python for scoring analysis and geographic insights<br>
Generated visualization reports and dashboards using Matplotlib and Seaborn libraries<br>
Provided valuable insights for assessing the quality of physician services and patient satisfaction, enhancing data-driven decision making<br>
Successfully developed a high-accuracy stock price predicting model in Python<br>
Implemented data partitioning into training, validation, and testing datasets<br>
Introduced advanced techniques such as ARIMA, XGBoost model, and VotingRegressor to enhance the model's predictive capabilities<br>
Supported client service strategies and management decision-making by utilizing Python and SQL to process ride-hailing service data<br>
Analyzed the driver's lifetime value and average income per ride<br>
Constructed various dashboards and reports using Power BI, incorporating real-time refresh functions for in-depth and real-time analysis</p>

<p>Increased revenue by $50,000 through SQL-driven market trend analysis and Excel-based data segmentation using pivot tables and VLOOKUP<br>
Applied time series and trend analysis to forecast enrollment patterns, contributing to strategic school enrollment planning<br>
Optimized student schedule management and personal data handling by automating processes with SQL views and advanced Excel functions<br>
Improved data analysis, reporting, and visualization, significantly increasing operational efficiency in the school<br>
Developed advanced Excel workbooks for dynamic tracking of student enrollment and marketing impact<br>
Enhanced data analysis and strategy adjustment, leading to a significant rise in enrollments</p>

<p>Conducted thorough tracking, monitoring, and detailed analysis of the local real estate market<br>
Executed comprehensive data preparation, including rigorous data cleansing and relevance checks<br>
Created accurate real estate forecasting models, guiding strategic decision-making and contributing to successful investments<br>
Utilized SQL and Excel to conduct in-depth analysis of real estate trend data, incorporating data cleaning, correlation checks, and advanced data modeling<br>
Created Comparative Market Analyses (CMAs), offering clients reliable real estate trends and data-driven recommendations<br>
Developed and implemented dynamic dashboards using Excel and SQL for real-time tracking of crucial real estate market indicators<br>
Streamlined data accessibility and interpretation, fostering faster and more informed decision-making for property investments and client advisories</p>