Matt Poland
Data Scientist and Physician Assistant
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Portfolio
Keystone Projects
Dementia Prediction
-View via GitHub/Colab
-View via ipynb file viewer
Global Burden of Disease
-View via GitHub/Colab
-View via ipynb file viewer
Bioinformatics
- DE Analysis of Breast Cancer VDX Data Using Limma Package
- DE Analysis of AML, ALL, CML Data with Limma
- Pre-processing Bioactivity Data from ChEMBL Database
Prediction Models
Regression
- Housing Data with linear model using scikit
- Housing Data with Keras
- Advertising Data with logistic model using scikit
- Ecommerce with linear model using scikit
Classification
- Cancer Data with Keras
- Breast Cancer Data with SVM using scikit
- Kyphosis Data with decision trees and random forest using scikit
- Loan Data with decision trees and random forest using scikit
Clustering
- Breast Cancer Data with PCA using scikit
- Subjective Target Class Find with KNN using scikit
- Easy Make_blobs with Kmeans using scikit
Neural Networks
- Diabetes with forward feed neural network
- Classic MNIST Image Processing with Tensorflow
- MNIST with CNN and RMSProp
Additional Work
- Feature Engineering - 911 Emergency Log
- Feature Engineering - Lending Club Info
- Natural Language Processing - Spam Collector
- Natural Language Processing - Yelp
- Recommender Systems - Movies
- OpenAI - Recipe Creator
- OpenAI - NLP to SQL
- OpenAI - Auto Exam Generator
- Plotly and Cufflinks
- US Agriculture Exports with Choropleths maps
- Visualizing regression plots with seaborn
- Visualizing dataframes with pandas built-in visualizations
- Meshgrid using NumPy
My Notes
- ChIP-seq with Bioconductor in R
- DE Analysis with limma in R
- RNA-Seq with Bioconductor in R
- Bioconductor
- NannyML
- Machine Learning Monitoring Concepts
- Geospatial Data in Python
- Extreme Gradient Boosting with XGBoost
- Cluster Analysis in Python
- Linear Classifiers in Python
- Decision Tree for Classification
- Sampling in Python
- Hypothesis Testing using Python
- Unsupervised Learning
- Supervised Learning with scikit
- Introduction to Apache Pulsar
- Intermediate Regression with statsmodels
- Intermediate Regression in R
- Introduction to Regression with statsmodels
- Introduction to Regression in R
- Working with Categorical Data in Python
- Dates and Times in Python
- Git
- Intermediate Seaborn
- matplotlib
- Exploratory Data Analysis using Python
- Cleaning Data with Python
- Importing Data with Python
- Data Communication Concepts
- Writing Efficient Python Code
- Writing Functions in Python
- Python Data Science Toolbox
- Data Types for Data Science within Python
- Statistics
- Foundations of Probability in R
- Introduction to Statistics in R
- Odds Ratio
- Confidence Intervals
- Rsquared
Certifications
- Analyzing Genomic Data in R
- Practical Apache Pulsar Application Development
- Data Scientist with Python
- Python for Data Science and Machine Learning
- Data Analyst with Python
- Complete SQL
- Math for Data Science Masterclass
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