On the left are topics in Data Science (predictive modeling, text mining, programming, the challenges of "Big Data" and unstructured data).

On the right are topics in Statistics, as used in research and data analysis (biostatistics, inference, statistical models, Bayesian techniques, study design).

Need advice on what which course to take? Contact us with your goals and background, and one of our instructors will provide some suggestions.

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*Evaluated and recommended for college credit by ACE CREDITÂ®

### Data Analytics

### IT/Programming

### Spatial Analytics

### Operations Research and Risk

### Stats for Credit

- Biostatistics for Credit *
- Categorical Data *
- Cluster Analysis *
- Decision Trees *
- Financial Risk *
- Forecasting Analytics *
- Hadoop *
- Intro Stats for Credit *
- Natural Lang. Processing (NLP) *
- Optimization - Advanced *
- Optimization - Intro *
- Predictive Analytics 1 *
- Predictive Analytics 2 *
- Predictive Analytics 3 *
- Python *
- R Programming Interm *
- R Programming Intro 1 *
- R Programming Intro 2 *
- Regression *
- Risk Simulation and Queueing *
- SQL *
- Sentiment Analysis *
- Social Network Analysis *
- Spatial Statistics - GIS *
- Survival Analysis *
- Text Mining *
- Visualization *

### Introductory

### Review/Prep

### Statistical Modeling

### Methods

### Clinical Trials

### Social Science

### Survey Statistics

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