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Work experience

Data Science Teaching Associate

2020.082022.04
Monash University

Teaching and developing curriculums for postgraduate data science course Data-Driven Decision Making on the following topics:

  • Using open-source tools to collect raw data, detect data anomalies and solve data quality problems, wrangle large scale data in CSV, JSON, XML and PDF formats. 
  • Using graphical exploratory data analysis and statistical inference methods to provide data insights reports and data dashboard.
  • Using machine learning algorithms and statistical analysis skills to mine data patterns and build data models to support decision making.

Research Assistant

2019.042021.12
Department of Economics, Monash University
  • Using open-source tools to collect Economics and Geospatial data, remove syntactic and semantic anomalies and impute missing value to maintain data quality.
  • Conduct statistical tests to provide inference in Economic data and create demographic maps to demonstrate population variation across areas in academic papers.

Education

Master of Artificial Intelligence

2021.032022.12
Monash University
  • Core units: Computer Vision, Deep learning, Machine Learning.

Bachelor of Business Analytics and Econometric

2017.022019.12
Monash University
  • Core units : Statistical Analysis, Database, Data Wrangling.
  • Summer student of HEC Montréal Artificial intelligence and Data Sciences Program.

Projects

Corporación Favorita Grocery Sales Forecasting

Python | R | Machine learning | Multiple time series analysis | Retail data

  • Use data wrangling tools to process, handle missing values, and normalize and encode variables of 125 million grocery sale data from 52 supermarkets.
  • Use visualisation tools to visualise important variables, discover trends and seasonality of data and visualise the variation between feature engineering.
  • Applying hyperparameters tuning, and ensembling learning techniques to develop machine learning models to forecast future sales and increase 16.53% inventory efficiency. 

Melbourne House Price Prediction

Python | R | Machine learning | Predictive Modeling | Property data

  • Use data wrangling tools to process property data with over 700 thousand rows, detect and handle abnormal data, feature engineering, and perform dimension reduction.
  • Use visualisation tools to visualise important variables, discover trends in data and use reporting tools to produce reports of data insight.
  • Use analytical tools to build machine learning models accurately predict the Melbourne house price within 10% variation.

Dashboard for Coronavirus Disease (COVID-19)

R |  Interactive dashboard | Healthcare data

  • Use data wrangling tools in R to collect and transform real-time covid-19 data, detect and handle abnormal data, data profiling and statistical data analysis. 
  • Use visualisation tools in R to visualise the trend, movement and variation of new cases and accumulated cases across regions.
  • Use reporting tools in R to develop an interactive dashboard to deliver real-time case status and data insight.