Luis Campos
Statistician
Cambridge, Massachusetts, United States
Summary
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RockstarLuis Campos is a statistician with 11 years of practical experience applying statistical modeling and data science techniques from Cambridge, Massachusetts. He has hands-on experience developing educational lab materials and project notebooks that demystify linear and logistic regression and forecasting workflows using scikit-learn and Jupyter. Luis combines rigorous statistical thinking with clear documentation practices, as evidenced by contributions to CS109 materials and a food inspection forecasting project that emphasize reproducible analysis and interpretable results. Comfortable both in exploratory data analysis and model evaluation, he bridges theory and applied work for real-world datasets. Known to be "statistician by day, statistician by night," he brings persistent curiosity and a teaching mindset to collaborative data science efforts.
11 years of coding experience
Github Skills (15)
data-analysis10
scikit10
scikit-learn10
pandas10
logistic-regression10
jupyter-notebook10
python10
linear-regression10
matplotlib9
statsmodels9
machine-learning9
seaborn8
data-visualization8
data-visualisation8
data-visualizations8
Programming languages (2)
Github contributions (5)
cs109/2015lab4
Sep 2015 - Sep 2015
Lab for Linear and Logistic Regression, SciKit Learn
Role in this project:
Data Scientist
Data ScientistContributions:11 commits, 3 pushes in 16 days
Contributions summary:Luis contributed to the development of lab exercises focused on linear and logistic regression using the SciKit Learn library. Their commits show the initial draft and subsequent examples for linear regression, likely implementing and demonstrating the models and related concepts. These changes involve code for model training and evaluation, and are framed in the context of applying statistical methods.
logistic-regressionscikit-learn
cs109/2015lab11
Nov 2015 - Nov 2015
Overview of a project: Food Inspection Forecasting
Role in this project:Data Scientist
Data ScientistContributions:7 commits in 1 day
Contributions summary:Luis's initial commit introduces a Jupyter Notebook detailing a project focused on food inspection forecasting. The subsequent commits add project guidelines and update results, indicating a focus on documentation, data analysis, and result interpretation within a data science workflow. The initial commit, which includes importing common data science and ML libraries and reading in the food inspections dataset, suggests that the user is engaged in data exploration and analysis.
forecastingpythoninspectionrisk-assessmentmachine-learning
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