Yuehao Huang (Walter)


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Data Analyst & Full-stack Developer 
  • Remarkable computer literacy (Python, Java, R, SAS, MS office, HTML, JS)
  • On-hand experience in numerous data analysis and program coding
  • Trilingual, proficient in Chinese, English, and Japanese
Collaboration Time-Management Communication Adpative Teamwork
Yuehao Huang
55 Gerrard Street W
Toronto, ON M5B0A5
P: (416)-838-7629
walter.huang@mail.utoronto.ca

Education


University of Toronto

Sep 2018 - Apr 2022
Bachelors cGPA 3.3/4.0

Specialist in Applied Statistical Science: Methods and Practice

Course highlight: STA490 Statistics collaboration, STA437 Multivariable Statistics, STA3144/414 Machine Learning, STA457 Time Series Analysis,CSC207 Software Design, GGR252 Marketing geography. Participated in multiple statistics analysis project including two signing NDAs.

Skill Set


R
Python
Java
HTML
Javascript
SAS
PHOTOSHOP

Work Experience


Gyubee Japanese Grill Dundas

Dec 2019 - PRESENT
Manager Server

Part time job at restaurant

  • Managed table settings to improve the satisfaction of consumers, maintained the cleanness and tidiness of tables.
  • Collected payments and checks with 100% correctness and calculated daily sales and salaries for employees, documented the result for double-checking in the future for the owner.
  • Accomplished $20k averaged daily sales with Gyubee praised as one of the best All-you-can-eat grilling restaurants in Toronto

University of Toronto

May 2020 - Aug 2020
Teaching assistant

Giving online Tutorial, answering students’ questions about their courses in PHL245, A course about Logic and Proofs.

  • Gathered students’ written questions on the platform and classified them into different categories and units.
  • Prepared tutorial plans according to the specific academic chapters, answered questions in a easily understandable way.
  • Researched the teaching targets and requirements of this logic course, developed study guide and practice questions.
  • Cooperated with Professor and other teaching assists, achieved 90% favorable comments after the course finishes.

Research Projects


Analysis of 2019 University of Toronto National Survey of Student Engagement

Apr. 2022
Collaboration Teamwork Mixed Level Logistic model

STA490 Stats Collaboration project with Professor Jeff

  • As a leader of the group, lead and discussed with members how to approach the dataset with 8000+ entries and 200+ features.
  • Data visualization for clear representation of the variable of interest, present to the audience with no statistics background.
  • Collaboration with other teams and actively review/update model to better fit the needs of the project

Analysis of Gender Bias Problem Black Saber Software. Ltd.

Apr. 2021
Teamwork Leadership Mixed Level ZIP model
  • As a leader of the team, investigated the gender bias during the hiring and among current employees of a company.
  • Use Multi-Level Zero-Inflated Poisson Regression model to make analysis since the data has complex correlation structure.
  • Investigated the relation between productivity, income, promotion possibility and gender, visualized the result for presentation.
  • Collaborated with other members together to present a report back to the owner of the company who requested this analysis.

Time Series Analysis on LA pollution study

Mar. 2021
Time series visualization Prediction ARIMA model

STA414 Time series analysis project

  • Use ARIMA model to forecast the time series for prediction and present the visualization result.
  • Researched the LA pollution study from 1970 to 1980 and applied times series model to forecast future pollution development.
  • Result prediction bound contains the future datapoints from 80s, which means prediction is correctly done.

Analysis the difference of 2019 Canada Federal Election if ‘everyone’ voted.

Apr. 2021
post-stratification visualization Prediction Logistic model
  • Use logistic regression model with post-stratification dataset to identify how result would differ from the real one and visualize the result on the map of Canada for a clear distinguishment.
  • Modeling result predicted that Trudeau would have 0.12% more popular votes than Tories which mostly are accurate.

Contact