Data-Informed Thinking + Doing

Sentiment Analysis Using Lexicons

Extracting the polarity and emotions from tweets during the 2020 NBA Draft—using WordNet and VADER in Julia, Python, and R.

Topic Classification Using Language Models

Categorizing the topics of the Federalist Papers (by Alexander Hamilton et al.)—using Google's BERT language model.

Topic Classification Using Word Embeddings

Categorizing Jeff Bezos' shareholder letters into one of a predefined set of topics—using Word2Vec, FastText, and ELMo.

AutoML: Scaled & Efficient Unsupervised Learning

Automating the time-consuming task of modeling and hyperparameter tuning—using DataRobot in R, Python, and Julia.

Scaled & Efficient Supervised Learning with AutoML

Accelerating time-to-value by automating modeling tasks on beer consumer data—using H2O.ai in R and Python.

Geospatial Data Analysis

Analyzing Denver's small cell nodes (used for enabling 5G-powered IoT)—using ggmap for R, GeoPandas for Python, and JuliaGeo for Julia.

Topic Classification Using Bag-of-Words

Extracting the important topics from the final 2012 presidential debate transcript—using TF-IDF in R, Python, and Julia.

Aggregating Many Decision Trees Via Boosting

Averaging Predictions Via Random Forest and Extra Trees

Ensemble methods on decision trees for regression tasks—using R, Python, and Julia.

Random Forest and Extra Trees for Majority Vote Predictions

Ensemble methods on decision trees for classification tasks—using R, Python, and Julia.

Aggregating Many Decision Trees Via Bagging

Linear Classifiers Using Support Vector Machines

Feature Engineering

Creating new and insightful baseball pitching stats (features or independent/predictor variables).

Subset Selection, Shrinkage, and Dimension Reduction

Selecting variables for linear models to increase predictive accuracy and interpretability—using Julia, Python, and R.

Numerical Predictions Using Polynomial Regression

Predicting gas mileage based on a non-linear relationship with speed—using polynomial regression in R, Python, and Julia.

Numerical Predictions Using Ridge and Lasso Regression

—using scikit-learn for Python.

Centroid-Based and Hierarchical-Based Clustering

Identifying similar groups of U.S. colleges & universities—using K-Means clustering and HClust in R, Python, and Julia.

Market Basket Analysis Using Association Rules

data—using R, Python, and Julia.

Image Recognition Using Neural Networks

data—using R, Python, and Julia.

Regression With Implementation Ease

Predicting car MSRP for high interpretability—using regression trees in R, Python, and Julia.

Classification With High Interpretability

Modeling categorical predictions that is easy explain—using classification trees in R, Python, and Julia.

Categorical Predictions Based on Past Events

Predicting whether the stock market will move up or down—using Naïve Bayes in R, Python, and Julia.

Binary Classification Based on Similarities

Predicting loan offer acceptance—using KNN in R, Python, and Julia.

Categorical Predictions Based on Probability

Inferring consumer demographics with preference for light beer—using logistic regression in R, Python, and Julia.

Dimension Reduction for Reducing Complexity

Subsetting eBay auction predictors by finding uncorrelated linear data with maximum variance—using PCA in R, Python, and Julia.

Time Series Analysis & Forecasting

Forecasting CO2 levels in Hawaii—using statistical models, machine learning, and deep learning in R, Python, and Julia.

Numerical Predictions Using Linear Regression

Predicting sales based on advertising budget—using linear regression in R, Python, and Julia.

Hypothesis Testing

Testing statistical inferences on delivery data—using R, Python, and Julia.

Inferential Statistics

Drawing inferences about a population based on information about a random sample of NHANES data—using R, Python, and Julia.

Random Sampling

Exploring the simple, stratified, systematic, and cluster sampling methods on coffee rating data—using R, Python, and Julia.

Covariance and Correlation

Analyzing the linear predictive strength of the factors that makes a great wine—using R, Python, and Julia.

Probabilistic Thinking

Making probabilistic inferences about on-time flight arrival—using R, Python, and Julia.

Exploratory Data Analysis on Categorical Data

Univariate, bivariate, and multivariate analyses on the Marvel Comics dataset—using R, Python, and Julia.

Univariate, Bivariate, and Multivariate Analyses on Numerical Data

Exploratory data analysis of the PGA golf dataset—using R, Python, and Julia.

Data Visualization With Grammar of Graphics

Visualizing TikTok data—using ggplot2 for R, plotnine for Python, and Gadfly for Julia.

Summary Statistics

Extracting an initial overview from the 2000 U.S. Census Bureau data—using R, Python, and Julia.