
Yujun Liu
Technology / Internet
About Yujun Liu:
A recent graduate of a master's program in data science and data engineering. Passionate about discovering trends in data and constructing workflows to transform raw data into knowledge.
Experience includes analyzing genomic data for a laboratory using statistical techniques in Python and R, and migrating pipelines from SQL Server to Azure using Python and SQL.
I am seeking a collaborative and fast-paced work environment where I can further develop my skills and make positive impacts for both the company and its clients.
Experience
8 months of experience at Bond Brand Loyalty, where tasks involved building pipelines in Microsoft SQL Server and migrating them to Azure Databricks.
- Collaborated with an agile team to meet biweekly deadlines
- Translated SQL code to Pyspark for over 20 tables, taking care to maintain the same transformation logic
- Assembled and tested pipelines on Azure data factory
- Proposed automation strategy to quicken production of JSON metadata files
1 year of experience at The Centre for Addiction and Mental Health, helping answer research questions by cleaning raw data and applying statistical tests to calculate p and r^2 values.
- Researched analyses in similar works to determine appropriate methods to analyze data
- Cleaned and normalized various data types for analysis, including multimodal and circular
- Managed time across multiple projects to ensure all deadlines were met
- Presented results by visualizing data in plots formed by ggplot2 and plotly in R
Education
MSc Data Science and Engineering, University of Dundee, graduated 2023:
Relevant coursework and topics included:
- Gathering customer requirements, designing a star schema, then collecting and merging customer data from multiple sources in Python to build a data warehouse for the customer based on the designed schema
- Collaborated to present relational vs. non-relational databases, focusing on RavenDB and how it compares to alternatives
- Analyzed distributed data by using virtual machines on Google Cloud Platform, Docker, and Hadoop
- Set up microservices to construct a streaming service, using GitHub and Jenkins to automate builds
Master's Project was to improve upon a U-Net convolutional neural network model used for differentiating parts of bone images, to help forensic analysts estimate age
- Carefully annotated bone images, to ensure that the training data for the model was correct
- Implemented pre-trained architectures in Google Colab, using tensorflow and keras
- Researched appropriate performance metrics to monitor to assess model performance
- Analyzed effects of design changes on model performance to identify and fix weaknesses
- Presented improvements to the model through a written report and verbally at a seminar
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