Training and Workshops Offered by Premier Analytics Consulting

This hands-on, half-day workshop is designed for data scientists, statisticians, programmers, machine learning engineers, and researchers seeking to train and fine-tune supervised machine learning (ML) models using Python. Attendees will gain practical experience with Python’s open-source libraries to build, fine-tune, and evaluate supervised models for classification and regression tailored to real-world predictive needs. To help attendees master the machine learning toolkit, this workshop helps them develop a range of supervised ML models while learning how to mitigate overfitting and underfitting, evaluate model performance, and interpret results and feature significance. Centered around Python’s scikit-learn (sklearn) ecosystem, this workshop shows attendees an applied, model-driven approach with key concepts including preparing data for ML models, automating data workflows with scikit-learn pipelines, balancing model complexity and interpretability, understanding bias-variance tradeoffs, and comparing statistical models to ML algorithms. Attendees will learn essential data cleaning techniques, perform exploratory data analysis (EDA) to visualize and understand feature relationships, and build end-to-end scikit-learn pipelines. Machine learning algorithms developed in this workshop include multiple linear (OLS) regression, LASSO and ridge regression, decision trees, random forests, and gradient-boosted ensembles for classification and regression. Topics include hyperparameter fine-tuning, feature selection and importance, handling model complexity, and strategies for boosting model performance on unseen data. All attendees will receive the workshop’s PDF slides, an interactive Jupyter Notebook containing the workshop’s documented Python code, and the practical skills to confidently train, optimize, and evaluate predictive models for data-driven AI workflows. Attendees will gain practical experience from using libraries from Python’s package repository PyPi including scikit-learn, statsmodels, pandas, numpy, scipy, matplotlib, and seaborn.

This hands-on, half-day workshop is designed for data scientists, statisticians, programmers, machine learning engineers, and researchers seeking to train and fine-tune supervised machine learning (ML) models using Python. Attendees will gain practical experience with Python’s open-source libraries to build, fine-tune, and evaluate supervised models for classification and regression tailored to real-world predictive needs. To help attendees master the machine learning toolkit, this workshop helps them develop a range of supervised ML models while learning how to mitigate overfitting and underfitting, evaluate model performance, and interpret results and feature significance. Centered around Python’s scikit-learn (sklearn) ecosystem, this workshop shows attendees an applied, model-driven approach with key concepts including preparing data for ML models, automating data workflows with scikit-learn pipelines, balancing model complexity and interpretability, understanding bias-variance tradeoffs, and comparing statistical models to ML algorithms. Attendees will learn essential data cleaning techniques, perform exploratory data analysis (EDA) to visualize and understand feature relationships, and build end-to-end scikit-learn pipelines. Machine learning algorithms developed in this workshop include multiple linear (OLS) regression, LASSO and ridge regression, decision trees, random forests, and gradient-boosted ensembles for classification and regression. Topics include hyperparameter fine-tuning, feature selection and importance, handling model complexity, and strategies for boosting model performance on unseen data. All attendees will receive the workshop’s PDF slides, an interactive Jupyter Notebook containing the workshop’s documented Python code, and the practical skills to confidently train, optimize, and evaluate predictive models for data-driven AI workflows. Attendees will gain practical experience from using libraries from Python’s package repository PyPi including scikit-learn, statsmodels, pandas, numpy, scipy, matplotlib, and seaborn.

This half-day applied modeling workshop is open to data scientists, statisticians, machine learning (ML) enthusiasts, researchers, programmers, and anyone interested in integrating classical statistical modeling with modern machine learning using Python and R. Participants will explore how long-established models such as ANOVA, multiple linear regression (MLR) with interactions, and Cox Proportional Hazards (Cox PH) translate into modern ML counterparts like decision trees, survival trees, and random survival forests, using well-supported open-source libraries in both languages for group comparisons, time-to-event, and risk-based modeling. Attendees will develop practical skills in data preprocessing, visualization, model development, and interpretation using widely adopted packages from the PyPI and CRAN repositories, including: •Python: pandas, statsmodels, scikit-learn, lifelines, scikit-survival •R: tidyverse (including dplyr and ggplot2), survival, randomForestSRC Each model will be introduced with discussion of its assumptions, use cases, output interpretation, and limitations, followed by hands-on comparisons between classical statistical methods and modern machine learning approaches. By the end of the workshop, attendees will understand how to apply and compare group comparison models, interaction terms, and survival analysis workflows. With PDF slides, interactive notebooks in Python and R, and documented code examples, participants will be equipped to confidently implement statistical and ML models across domains such as healthcare, insurance, marketing, finance, banking, clinical trials, and applied research.