Data Modeling

Supervised Learning

• Neural Nets, XGBoost, Random Forest, Linear and Logistic Regression, Naive Bayes, SVM

Unsupervised Learning

• K-Means Clustering, Principal Component Analysis

Combination

• NLP

• OBA 410: Analyzing Big Data

• OBA 433: Information Analysis for Managerial Decisions

• OBA 488: Project and Operations Management Models

Statistics

• Bayes

• Linear Optimization

• Regression

• Inferences

• Correlation

• MATH 241: Calculus for Business and Social Science I

• Math 242: Introduction to Probability and Statistics

• MATH 243: Calculus for Business and Social Science II

• DSC 330: Business Statistics

• OBA 410: Analyzing Big Data

Computer Science

Languages

– Python

– HTML

– CSS

– Relational Databases

• CIS 105: Explorations in Computing

• CIS 110: Fluency with Information Technology

• OBA 488: E-Business

• OBA 340: Business Info Systems

Reporting

EDA

Vizualizations

– ggplot, corrplot (R)

– seaborn, matplotlib (Python)

Data Importing and Cleaning

Loading

– csv, xlsx, xls, html, etc

Web scraper

– rvest (R)

Data tables/ Arrays

– data.table and dplyr (R)

– numpy and pandas (Python)

Dates

– xts and zoo (R)

– datetime (Python)

Image Processing

R

Rstudio

Efficient Code