Quantitative Resume
Rausser College’s Master of Climate Solutions practices holistic admission, where each applicant’s unique experience, in addition to their professional experience and academic background, is incorporated into the application review. Applicants are allowed to submit their GRE scores, but it is not mandatory. Those who submit high GRE scores will not be considered “better” or “more qualified” than those who do not submit their GRE scores.
In order to assess the applicant’s quantitative aptitude, the application requires a Quantitative Resume. It is important that each Quantitative Resume follows the below format so that the information is easily readable. It is also important to include academic, research, professional experiences where quantitative skills were acquired/used.
For academic courses, be sure to list the following information:
- Course title
- Institution Name
- Term taken
- Grade
- A brief description (including bullet points) of what the course covered.
For professional experience, be sure to include the following information:
- Job title
- Institution name
- Appointment period
- A brief description of a project and the bullet points of tools utilized.
Sample Quantitative Resume
| Courses and Experiences | Description |
|---|---|
| Sociology 106 UC Berkeley Fall 2015 A- | Quantitative Sociological Methods Probability theory and models of distributions Confidence intervals Chi-square Linear and logistic regression |
| Engineering 182 UC Berkeley Fall 2015 A | Introduction to Data Science with Python Algorithms, Pandas (Python Package), Data Science, Artificial Intelligence, R (Programming Language), NumPy, Scientific Methods, Python (Programming Language), Matplotlib, Scikit-learn (Machine Learning Library), Parsing, Machine Learning |
| Environmental Science 176 UC Berkeley Spring 2015 A- | Geospatial Analysis with ArcGIS ArcGIS Pro Network analysis Heatmaps and hot spot analysis Spatial data for storytelling |
| Data 4AC UC Berkeley Fall 2016 A- | Data and Justice Data collection, visualization, and analysis Collaborative and creative projects with data science using real-world data |
| Statistics 20 UC Berkeley Spring 2016 A | Introduction to Probability and Statistics Relative frequencies Discrete probability Random variables Expectation Testing hypotheses Estimation |
| Research Assistant UC Berkeley Summer 2015 | Worked with Professor Cybelle Fox on the impact of the rise of legal status restrictions in state welfare policy in the 1970s Data cleaning Initial data analysis using STATA |
| Affirm – Intern Summer 2016 | Quantitative Markets Analyst Data analysis Expanded structuring, monitoring, and analysis capabilities Developed automation tools and optimize models Built data-driven models to analyze and predict the performance of Affirm’s consumer loans |
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