David Pham’s Digital Curriculum Vitae
This page contains all my work and project history.
If you would like to see my actual resume, you can view it here.
Professional Summary
Detail-oriented data analyst with extensive experience in managing large-scale data projects, particularly in the
education sector. Skilled in data collection, analysis, and reporting, with a strong focus on student financial aid
and policy programs. Proven ability to develop comprehensive databases, perform methodological reviews, and
deliver insightful reports. Adept at collaborating with cross-functional teams to drive data-driven decisions.
Eager to leverage analytical expertise and technical prowess to contribute to impactful research and learn new
skills to break into the data science world.
Education
Sep 2018 - June 2022
University of Toronto (St. George)
HBSc. Mathematics and Statistics Specialist
Campus Involvement:
Events and Logistics Coordinator
University of Toronto Vietnamese Students Association
- Helped facilitate and successfully organize in-person and online events for 270+ club and community members
- Managed detailed event logistics including event itineraries, inventory lists, and executive roles to plan official social gatherings that incorporate safe and inclusive practices
- Collaborated with other executive members to manage and finalize year-long event programming of 12 biweekly social, cultural, and academic events
Experience
Oct 2022 - Present
Bilingual Data Analyst
Council of Ministers of Education, Canada
- Serve as the primary data analyst for the Pan-Canadian Indicators on Student Financial Assistance(PCISFA) project, working closely with provincial and territorial partners to collect and analyze largescale data on student financial aid programs
- Design and maintain a comprehensive database with over 100 indicators, automating data collection and analysis workflows using Python, Excel and PivotTables
- Produce annual reports featuring detailed visualizations and statistical tables, providing insights to support evidence-based policymaking
- Conduct annual methodological reviews of indicators and data points to ensure accuracy and alignment with provincial, territorial, and federal frameworks
Jan 2022 - Apr 2022
Research Assistant (Statistics Education Developer)
University of Toronto Department of Statistical Sciences
- Planned and developed a statistics-themed virtual escape room using R to promote statistical education
- Created social media content around ethical practice and statistical literacy resulting in over 3500 impressions and 400 combined engagements
- Coordinated with professors and 29 other research assistants as a team to ensure smooth release of posts and delivery of the escape room
Aug 2021 - Apr 2022
Peer Mentor (STA130 Mentorship Program)
University of Toronto Department of Statistical Sciences
- Led one-on-one meetings with up to 60 mentees to talk about navigating life in university, as well as discussing their academic goals and objectives
- Established positive and professional mentoring relationships with students, and maintained a 100% satisfaction rate
- Collaborated with other mentors to help plan and facilitate social events to encourage community building and offer academic/career-oriented advice
May 2021 - Sep 2021
Research Assistant
University of Toronto
- Assisted Professor Scott Schieman’s research on work, stress, and health among Canadians during COVID-19
- Used Python libraries such as NumPy and pandas, as well as Excel to analyze, categorize, and visualize open-ended survey data with over 3000 entries about Canadians working from home during COVID-19
- Visualized findings with statistical tables using Excel and R, and presented summaries in a concise manner
Projects
(Please click the hyperlinks to view my work.)
Independent Readings Course – Ethical Practice for Statisticians
- Was one of three students selected to collaborate with Professor Liza Bolton on statistical ethics research, examining statistical misuse in science and media
- Authored a 9-page educative piece using RMarkdown to spread awareness of statistical literacy and ethics, and used Zotero + BiBTeX to manage references
- Conducted seminars and research presentations for cohort to synthesise findings and key ideas from a large range of sources
Statistical Analysis of Gender Parity (R)
- Worked in a group of 4 for a school project as a consulting firm to assess gender parity in hiring, wages, and promotions for a fictitious software company
- Merged and wrangled large datasets to create data visualizations and summaries using dplyr and ggplot2
- Used judgment to choose appropriate generalized linear (mixed) models, checked their assumptions, and justified the selection in confidence intervals and significance testing
- Balanced the workload as a team and created a seamless report with reproducible code by using RMarkdown
- Liza Bolton (my past instructor for STA303) summarizes the project very well here:
Report on the Effect of Mortality Rate and Alcohol Consumption Using RDD (R)
- Individual school project aimed to replicate and extend Carpenter’s and Dobkin’s 2009 study on mortality rate and alcohol consumption
- Applied linear and quadratic regression discontinuity methods, using minimum legal drinking age as the threshold
- Used dplyr and tidyverse to clean data, and ggplot2 + knitr to visualize and present results.
Data Visualization for COVID-19 Cases in Toronto (R)
- Extensively used tidyr and dplyr libraries to wrangle, parse and transform real-time data from the City of Toronto website on COVID-19 cases in all 140 neighborhoods for a school assignment
- Used ggplot to visualize daily cases, outbreak types, and thematic maps that illustrate the percentage of low-income families and the proportion of COVID-19 cases by region
Comparing Complex Multiple Linear Models for Toronto and Mississauga House Prices (R)
- Implemented multiple linear regression in order to help home buyers predict sale price of homes in the GTA for a school assignment
- Analyzed diagnostic plots and used the Akaike/Bayesian information criterion for optimal probabilistic model selection
Technical Skills
I am comfortable with the following languages/applications:
- Python (NumPy, pandas)
- R (dplyr, tidyverse, ggplot2, knitr)
- Git/GitHub
- HTML & CSS
- SQL
- Tableau
- Microsoft Excel and Access
Relevant Courses
Here is an exhaustive list of relevant courses I have taken:
- CSC108 (Intro to Programming)
- STA130 (Statistical Reasoning)
- MAT137 (Calculus!)
- CSC148 (Intro to Computer Science)
- CSC165 (Mathematical Expression & Reasoning for Computer Science)
- MAT223 (Linear Algebra I)
- MAT224 (Linear Algebra II)
- MAT235 (Multivariable Calculus) (I love you, Professor Leonard.)
- MAT244 (Ordinary Differential Equations)
- MAT246 (Abstract Mathematics)
- STA257 (Probability & Statistics 1)
- STA261 (Probability & Statistics 2)
- MAT301 (Groups and Symmetries)
- STA302/1001 (Methods of Data Analysis I)
- STA303/1002 (Methods of Data Analysis II)
- STA304/1003 (Surveys, Sampling and Observational Data)
- STA305/1004 (Design and Analysis of Experiments)
- MAT334 (Complex Variables) (I love you, Petra Bonfert Taylor and CrystalMath.)
- STA347 (Probability Theory I)
- FSL421 (French IV)
- STA437/2005 (Methods for Multivariate Data)
- STA457/2202 (Time Series Analysis)
- MAT337 (Intro to Real Analysis)
- APM462 (Nonlinear Optimization)
- STA442/2101 (Methods of Applied Statistics)
- STA497 (Readings in Statistics) with Professor Liza Bolton. Thanks, Liza!!! :)