#18 The Institutes for Statistics Education (ACE)

#18 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 Multi-Armed 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
    • Python-intro
  • 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 you worth 3 cr.

This content is reprinted from Chapter 2 of Homeschooling for College Credit 2nd Edition.