https://github.com/astrosica/astrosica
https://github.com/astrosica/astrosica
Last synced: 3 months ago
JSON representation
- Host: GitHub
- URL: https://github.com/astrosica/astrosica
- Owner: astrosica
- Created: 2023-10-25T18:22:54.000Z (over 1 year ago)
- Default Branch: main
- Last Pushed: 2025-01-24T14:55:19.000Z (5 months ago)
- Last Synced: 2025-01-24T15:34:02.438Z (5 months ago)
- Size: 78.1 KB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
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Metadata Files:
- Readme: README.md
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README
# Hello, I'm Jess! ✨
I'm an Astronomer turned Data Scientist passionate about building data-driven solutions.
## 👩💻 About Me
:chart_with_upwards_trend: First employee at a fintech startup as a Data Science Advisor and Educator
🔭 8+ YOE in quantitative data-driven research and analysis
📚 PhD in Astronomy & Astrophysics + HBSc in Astronomy & Physics from the University of Toronto
💬 Taught 500+ technical and non-technical students over 17 classes, including on the use of Python
📝 Published several data-driven papers in esteemed research journals
👥 Mentored a student on a year-long data project through to publication
🧵 Fun fact: I cross-stitch realistic astronomy observations on [Etsy](https://www.etsy.com/ca/shop/Astrostitches)
## 🛠️ Skills
Languages: Python (NumPy, Pandas, Matplotlib, Seaborn, Scikit-Learn, SciPy), SQL (BigQuery, MySQL)
Tools: Tableau, Git/GitHub, Jupyter Notebook, MS Office Suite (Excel, PowerPoint, Word)
Techniques: EDA, Statistical analysis, Data pipelines, ML, Fourier Analysis, Documentation, Quantitative Research, Technical writing, Data visualization
## :globe_with_meridians: Contact
Email me at [[email protected]](mailto:[email protected])
Connect with me on LinkedIn at [linkedin.com/astrosica](https://www.linkedin.com/in/astrosica/)
## Data Projects
### Machine Learning (Python)
* [Credit Card Application Prediction](https://github.com/astrosica/data-science-portfolio/blob/main/Machine%20Learning/Projects/Credit%20Card%20Approval/Credit%20Card%20Approval.ipynb): Developed a prediction model that determines whether a credit card application will be approved or denied using Logistic Regression, KNN, and Random Forest models in Python.
### Reporting and Dashboards (SQL, Tableau, Excel)
* [Insurance Analysis](https://github.com/astrosica/data-science-portfolio/tree/main/Reporting%20and%20Dashboards/Insurance%20Claims%20Analysis): Developed an interactive Tableau dashboard to report and analyze 70K insurance claims, providing actionable insights to guide future marketing and budget decisions as a PowerPoint presentation.
* [Marketing Analysis](https://github.com/astrosica/data-science-portfolio/tree/main/Reporting%20and%20Dashboards/e-Commerce%20Marketing%20Analysis): Performed exploratory analysis and data validation of 100K sales records for a sample e-commerce company using Excel and SQL (Google BigQuery). Developed an interactive Tableau dashboard to report sales and marketing metrics.
* [TTC Delay Analysis](https://github.com/astrosica/data-science-portfolio/tree/main/Reporting%20and%20Dashboards/TTC%20Delay%20Analysis): Performed exploratory analysis and data cleaning of 40K subway delays for 2022-2023 using SQL and Tableau to investigate performance metrics, YoY KPIs, and performance strategies.
## Learning Projects
### Machine Learning (Python)
* [Predicting loan repayments](https://github.com/astrosica/data-science-portfolio/blob/main/Machine%20Learning/Learning/Predicting%20Loan%20Repayments%20with%20Decision%20Trees%20and%20Random%20Forest.ipynb): Predicted whether a lender will repay their loan using decision trees and random forest.
* [Classifying anonymized data](https://github.com/astrosica/data-science-portfolio/blob/main/Machine%20Learning/Learning/Classifying%20Anonymized%20Data%20with%20KNN.ipynb): Classified anonymized data into two target classes using k-nearest neighbours (KNN).
* [Predicting ad clicks](https://github.com/astrosica/data-science-portfolio/blob/main/Machine%20Learning/Learning/Predicting%20Ad%20Clicks%20with%20Logistic%20Regression.ipynb): Predicted whether someone will click on an ad using logistic regression.