The Institutes for Statistics Education
Limited Transfer
www.statistics.com
Statistics and data sciences attract a special kind of student. The Institutes for Statistics Education elevated itself in 2016 when they collaborated with Thomas Edison State University to create the first ever degree in Data Science that can be earned almost entirely by ACE credit. This, by extension, allows your teen to earn this degree as a homeschooled student.
Collaboration with Thomas Edison State University
Thomas Edison State University TESU requires enrolled students to be 21 years of age, however, when your teen is 18 with college credit and their high school diploma complete, they can enroll. Therefore, the resourceful homeschool student will start taking the courses and working on this degree at home in high school DIY style with the goal of completing all or nearly all of the coursework at home. ACE courses are open to students of all ages, so when your teen “applies” to TESU, they will also be nearly ready to graduate with their bachelor’s degree. Learn more about the TESU Data Science Degree. This degree requires 12 specific courses through The Institutes for Statistic Education, 1 course through Thomas Edison, and 27 courses through your choice of ACE, CLEP, AP, or other alternative credit opportunities.
You Don’t Have to Pursue a Degree
You can still choose from any of their 30+ courses on statistics and data sciences even if you don’t pursue a degree! Review the list below.
Every course with a “check mark” next to it is worth college credit.
DATA SCIENCE

Data Mining and Prediction
 Anomaly Detection ✓
 Deep Learning ✓
 Persuasion Analytics ✓
 Predictive Analytics 1 – Python ✓
 Predictive Analytics 1 – R ✓
 Predictive Analytics 1 ✓
 Predictive Analytics 2 – Python ✓
 Predictive Analytics 2 R ✓
 Predictive Analytics 2 ✓
 Predictive Analytics 3 – Python✓
 Predictive Analytics 3 – R✓
 Predictive Analytics 3 ✓
 Predictive Analytics Preview
 Predictive Analytics Project Capstone
 Weka
 Winter Break 2018 Data Science Courses

Data Analytics
 Choice Modeling
 Cluster Analysis ✓
 Content Optimization with MultiArmed Bandits & Python
 Customer Analytics in R ✓
 Forecasting Analytics ✓
 Logistic Regression
 Network Analysis ✓
 Visualization ✓
 Weka

Text Analytics
 Natural Language Processing (NLP) using NLTK
 Natural Language Processing (NLP) ✓
 Sentiment Analysis
 Text Mining ✓
 Weka

Using R
 Bayesian – R
 Customer Analytics in R ✓
 Mapping in R
 Meta Analysis in R
 Predictive Analytics 1 – R ✓
 Predictive Analytics 2 R ✓
 Predictive Analytics 3 – R✓
 R Modeling
 R Programming Advanced
 R Programming Interm ✓
 R Programming Intro 1✓
 R Programming Intro 2 ✓
 R Statistics
 R ggplot2
 Spatial Statistics for GIS – Using R ✓
 Winter Break 2018 Data Science Courses

Python
 Natural Language Processing (NLP) using NLTK
 Predictive Analytics 1 – Python ✓
 Predictive Analytics 2 – Python ✓
 Predictive Analytics 3 – Python✓
 Python for Analytics ✓
 Python for Data Science
 Pythonintro

IT/Programming
 Anomaly Detection ✓
 Big Data Ingestion via API’s
 Predictive Analytics Project Capstone
 SAS Programming
 SQL ✓
 Winter Break 2018 Data Science Courses

Spatial Analytics
 Mapping in R
 Spatial Statistics for GIS – Using R ✓
STATISTICS

Introductory
 Afraid of Statistics?
 Intro Stats Preview
 Intro Stats for Credit ✓
 Statistics 1 ✓
 Statistics 2 ✓
 Statistics 3
 Winter Break 2018 Data Science Courses

Review/Prep
 Designing Valid Statistical Studies
 Intro Stats Preview
 Matrix Algebra ✓
 Resampling
 Winter Break 2018 Data Science Courses

Statistical Modeling
 Count Data
 GLM
 Logistic Regression – Adv
 Logistic Regression
 Longitudinal Data
 Mixed Models
 Multivariate
 R Modeling
 Regression ✓
 Structural Equation Modeling (SEM) – Adv
 Structural Equation Modeling (SEM)

Bayesian
 Bayesian – R
 Bayesian Computing
 Bayesian Hierarchical Models
 Bayesian MCMC
 Bayesian Statistics

Methods
 Bootstrap
 Categorical Data ✓
 Cluster Analysis ✓
 Factor Analysis
 Maximum Likelihood Estimation (MLE)
 Missing Data – Sensitivity
 Missing Data
 Regression ✓
 Resampling

Social Science
 Clinical Trials – Cluster
 Item Response Theory
 Longitudinal Data
 Rasch – Core
 Rasch – Facets
 Rasch – Further
 Rasch Applications 1
 Rasch Applications 2
 Sample Size
 Structural Equation Modeling (SEM) – Adv
 Structural Equation Modeling (SEM)
 Survey – Complex
 Survey Analysis
 Survey Design

Survey Statistics
 Survey – Complex
 Survey Analysis
 Survey Design

Biostatistics
 Biostatistics 1 ✓
 Biostatistics 2 ✓
 Biostatistics for Credit ✓
 Biostats Preview
 Clinical Trials – Cluster
 Epidemiologic Statistics
 Meta Analysis 2
 Meta Analysis in R
 Meta Analysis
 Sample Size
 Survival Analysis ✓

Clinical Trials
 Clinical Trials – Adaptive
 Clinical Trials – Cluster
 Clinical Trials – Introduction
 Clinical Trials – Missing Data
 Clinical Trials – Monitoring
 Clinical Trials – PK & Bioequivalence
 Clinical Trials – R

Engineering
 Design of Experiments
 Survival Analysis ✓

Environmental
 Clinical Trials – Cluster
 Environmental – Sampling
 Spatial Statistics for GIS – Using R ✓
DSST offers a basic statistics exam worth 3 credits
This content is reprinted from Chapter 2 of Homeschooling for College Credit 2nd Edition.