Coding/Statistical Analysis Skills

Languages and Libraries

Python (Jupyter Notebook, Google Colab)
- Numpy
- Pandas
- Scikit-learn
- Matplotlib
- Seaborn
- Keras
- BeautifulSoup
R (RStudio)
- GGplot2
- Dplyr
- Caret
SQL (Microsoft SQL Server)

Machine Learning

Supervised Learning
- Regression (Linear and Nonlinear)
- Decision Trees
  - Random Forests
  - XGBoost
  - LightGBM
- Neural Networks
  - Multilayer Perceptron
- K-Nearest Neighbors
- Support Vector Machine
Unsupervised Learning
- Clustering
- KMeans
Ensemble Learning
Dimensionality Reduction/Principal Component Analysis
Feature Selection/Creation

Statistical Analysis

Descriptive Statistics
- Exploratory Data Analysis
Regression Analysis (Linear and Nonlinear)
Correlation/Covariance
Z-Test
T-Test
F-Test
ANOVA/ANCOVA
Chi-squared test
Wilcoxon Signed Rank Test
Kruskal-Wallis Test
Beginner SAS and SAS Studio skills
Familiarity with Minitab