Expertise Areas
Our areas of subject expertise
Mathematics
- Pre-algebra
- Algebra
- Basic math skills
- Pre-calculus
- Calculus I
- Calculus II
- Multivariable Calculus
- Differential Equations
- Discrete Mathematics
- Geometry
- Trigonometry
- Liberal Arts Math
- Linear Algebra
Statistics
- Statistics
- Business Statistics
- Advanced Statistics
Biology
- Biology I & II
Physics
- Physics (algebra- and calculus-based)
Chemistry
- General chemistry I & II
- Organic chemistry I & II
Anatomy
- Introductory human anatomy & physiology (A&P)
Accounting
- Auditing
- Advanced accounting
- Corporate tax
- Cost accounting I & II
- Governmental/not-for-profit
- accounting
- Individual income tax
- Introductory accounting
- Financial accounting I
- Managerial accounting I
- Intermediate accounting I & II
- Payroll accounting
Economics
- Principles of macroeconomics
- Principles of microeconomics
- Essentials of economics
Finance
- Introductory finance
Statistics
- Business statistics
Programming
- C
- C++
- C#
- Java
- Python
- SQL
Web development
- Introductory web development
- CSS
- HTML
Computer science
- Introductory computer science
Operating systems
- Windows® 7/10
- Linux
Microsoft® Office applications
- Access
- Excel®
- PowerPoint®
- Word
Adobe® Creative Suite® applications
- Illustrator
- InDesign
- Photoshop ®
Academic writing review
- Writing review for all subjects
Math Courses
- College Algebra
- General Calculus I
- General Calculus II
- Introduction to Statistics
- Introductory Algebra
- Precalculus
- Quantitative Reasoning
Business Courses
- Accounting I
- Accounting II
- Business Communication
- Business Ethics
- Business Law
- Business Statistics
- Financial Accounting
- Introduction to Business
- Macroeconomics
- Managerial Accounting
- Microeconomics
- Organizational Behavior
- Personal Finance
- Principles of Management
Science Courses
- Anatomy & Physiology I
- Anatomy & Physiology I Lab
- Anatomy & Physiology II
- Anatomy & Physiology II Lab
- General Chemistry I
- General Chemistry I Lab
- General Physics I
- General Physics I Lab
- Introduction to Biology
- Introduction to Biology Lab
- Introduction to Environmental Science
- Introduction to Nutrition
- Microbiology
- Microbiology Lab
- Introduction to Environmental science
IT Courses
- CompTIA A+ Certification Training
- CompTIA Network+ Certification Training
- Introduction to Programming in C++
- Introduction to Programming in Java
- Introduction to Programming in Python
- Information Technology Fundamentals
Introductory Statistics
- Introduction to Data Literacy
- Introductory Statistics for College Credit
- Statistics 1 – Probability and Study Design
- Statistics 2 – Inference and Association
- Statistics 3 – ANOVA and Regression
Analytics
- Customer Analytics in R
- Discrete Choice Modeling and Conjoint Analysis
- Financial Risk Modeling
- Forecasting Analytics
- Integer and Nonlinear Programming and Network Flow
- Interactive Data Visualization with Tableau
- Introduction to Data Literacy
- Introduction to Design of Experiments
- Introduction to Network Analysis
- Mapping in R
- Optimization with Linear Programming
- Persuasion Analytics and Targeting
- Predictive Analytics – Project Capstone
- Predictive Analytics 1 – Machine Learning Tools
- Predictive Analytics 1 – Machine Learning Tools with Python
- Predictive Analytics 1 – Machine Learning Tools with R
- Predictive Analytics 2 – Neural Nets and Regression
- Predictive Analytics 2 – Neural Nets and Regression with Python
- Predictive Analytics 2 – Neural Nets and Regression with R
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with Python
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with R
- Predictive Analytics for Healthcare
- Python for Analytics
- Recorded Webinar on Content Optimization with Multi-Armed Bandits & Python
- Responsible Data Science
- Risk Simulation and Queuing
- Spatial Statistics for GIS Using R
- Statistical and Machine Learning Methods for Analyzing Clusters and Detecting Anomalies
Machine Learning
- Introduction to NLP and Text Mining
- NLP and Deep Learning
- Predictive Analytics – Project Capstone
- Predictive Analytics 1 – Machine Learning Tools with Python
- Predictive Analytics 1 – Machine Learning Tools with R
- Predictive Analytics 2 – Neural Nets and Regression with Python
- Predictive Analytics 2 – Neural Nets and Regression with R
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with Python
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with R
- Predictive Analytics for Healthcare
- Responsible Data Science
Using Python
- Introduction to NLP and Text Mining
- Introduction to Python Programming
- Persuasion Analytics and Targeting
- Predictive Analytics 1 – Machine Learning Tools with Python
- Predictive Analytics 2 – Neural Nets and Regression with Python
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with Python
- Python for Analytics
Using R
- Bayesian Statistics in R
- Customer Analytics in R
- Introduction to R Programming
- Mapping in R
- Modeling in R
- Predictive Analytics 1 – Machine Learning Tools with R
- Predictive Analytics 2 – Neural Nets and Regression with R
- Predictive Analytics 3 – Dimension Reduction, Clustering, and Association Rules with R
- Predictive Analytics for Healthcare
- R Programming – Intermediate
- R Programming – Introduction Part 2
- Spatial Statistics for GIS Using R
- Structural Equation Modeling (SEM) Using R
- Visualization in R with ggplot2
Operations Research
- Financial Risk Modeling
- Integer and Nonlinear Programming and Network Flow
- Optimization with Linear Programming
- Risk Simulation and Queuing
Biostatistics
- Biostatistics 1 – For Medical Science and Public Health
- Biostatistics 2 – For Medical Science and Public Health
- Biostatistics for College Credit
- Clinical Trials – Pharmacokinetics and Bioequivalence
- Designing Valid Statistical Studies
- Epidemiologic Statistics
- Independent Data Monitoring Committees in Clinical Trials
- Introduction to Statistical Issues in Clinical Trials
- Meta Analysis 1
- Meta Analysis 2
- Meta Analysis in R
- Sample Size and Power Determination
- Survival Analysis
Statistical Modeling
- Bayesian Statistics in R
- Bootstrap Methods
- Categorical Data Analysis
- Generalized Linear Models
- Introduction to Bayesian Hierarchical and Multi-level Models
- Introduction to Bayesian Statistics
- Introduction to MCMC and Bayesian Regression via rstan
- Introduction to Resampling Methods
- Introduction to Structural Equation
- Modeling (SEM)
- Matrix Algebra
- Maximum Likelihood Estimation
- Mixed and Hierarchical Linear Models
- Modeling Count Data
- Modeling in R
- Multivariate Statistics
- Principal Components and Factor Analysis
- Regression Analysis
- Sample Size and Power Determination
- Spatial Statistics for GIS Using R
- Statistical and Machine Learning Methods for Analyzing Clusters and Detecting Anomalies
- Structural Equation Modeling (SEM) Using R
- Survival Analysis