The companies/organizations in this category sell courses “for college credit” that have been evaluated by the American Council on Education (ACE). ACE credit is not as transferable as credit earned directly through a college, but can be very transferable when used with the intention of transferring to one of the partner colleges. ACE is a third party credit evaluator that evaluates all types of learning that happens outside of a college. This kind of college credit is guaranteed to transfer into partner colleges (varies by provider) but transfers poorly outside of partnerships.

Using ACE credit requires creating an account with another third party, Credly to “hold” your teen’s credit until they’re ready to use it. HS4CC List of ACE Partners has a catalog of ACE-evaluated courses in a variety of areas of statistics and data analytics that can be used for undergraduate or graduate credit. A bonus is that they have partnerships extend beyond just guaranteeing their courses transfer. These partnerships have degree plans that use the courses as the major. This, by extension, allows your teen to earn most or all of a degree in statistics or data science while Homeschooling for College Credit. Many students can finish the whole, or most, of the degree in high school.

Any student of any age may enroll- test scores are not required. Each course lists the suggested prerequisites (if applicable). Several of the courses offer a self-assessment to help you assess your background and readiness.

GET 20% off ANY COURSE when you use HS4CC coupon code at checkout! (multi-course bundles excluded) has over 50 instructors, who are recruited based on their expertise in various areas in statistics (most are the authors of well-regarded texts in their area). Senior faculty who have made important contributions to the field of statistics or online education in statistics (each with over five years of teaching experience online at advises the president on curriculum and standards.

Courses are scheduled for specific calendar periods (3 or 4 weeks), but do not require students to be online at particular times. Students discuss statistical questions with the instructor via a private class discussion forum, teaching assistants offer feedback on homework assignments (most of which involve the use of statistical software), and administrative staff handle student services and inquiries by email or phone.

These courses can also be used at any other college or university that accepts their credits. MANY of these courses are evaluated as upper-level (300-400) college credit.

Biostatistics for Medical Science and Public Health 1 & 2STAT-000205/01/202104/30/20243
Calculus ReviewSTAT-003805/01/202104/30/20242
Categorical Data AnalysisSTAT-000606/01/202105/31/20243
Customer Analytics in RSTAT-003106/01/202105/31/20243
Designing Valid Statistical StudiesSTAT-004205/01/202104/30/20241
Epidemiological StatisticsSTAT-004505/01/202104/30/20243
Financial Risk ModelingSTAT-000806/01/202105/31/20243
Forecasting AnalyticsSTAT-002106/01/202105/31/20243
Integer & Nonlinear Programming and Network FlowSTAT-001906/01/202105/31/20243
Interactive Data VisualizationSTAT-002806/01/202105/31/20243
Introduction to Data LiteracySTAT-003605/01/202104/30/20241
Introduction to Network AnalysisSTAT-002606/01/202105/31/20243
Introduction to NLP and Text MiningSTAT-001805/01/202104/30/20243
Introduction to Python ProgrammingSTAT-003705/01/202104/30/20243
Introduction to R ProgrammingSTAT-001409/01/202108/31/20243
Introduction to Statistical Issues in Clinical TrialsSTAT-003905/01/202104/30/20242
Introductory Statistics for College CreditSTAT-000106/01/202105/31/20243
Matrix Algebra ReviewSTAT-003206/01/202105/31/20243
Meta AnalysisSTAT-004105/01/202104/30/20241
NLP and Deep LearningSTAT-003405/01/202104/30/20243
Optimization – Linear ProgrammingSTAT-002206/01/202105/31/20243
Persuasion Analytics and TargetingSTAT-003505/01/202104/30/20243
Predictive Analytics 1 – Machine Learning ToolsSTAT-002906/01/202105/31/20243
Predictive Analytics 2 – Neural Nets and RegressionSTAT-003006/01/202105/31/20243
Predictive Analytics 3 – Dimension Reduction, Clustering, and Association RulesSTAT-002406/01/202105/31/20243
Predictive Analytics for HealthcareSTAT-004405/01/202104/30/20243
Python for AnalyticsSTAT-001106/01/202105/31/20243
R Programming – IntermediateSTAT-001505/01/202104/30/20243
R Programming – Introduction 2STAT-001305/01/202104/30/20241
Regression AnalysisSTAT-000906/01/202105/31/20243
Responsible Data ScienceSTAT-004305/01/202104/30/20243
Risk, Simulation and QueuingSTAT-001006/01/202105/31/20243
Spatial Statistics with Geographic Information SystemsSTAT-001606/01/202105/31/20243
SQL – Introduction to Database QueriesSTAT-001705/01/202104/30/20243
Statistical and Machine Learning Methods for Analyzing Clusters and Detecting AnomaliesSTAT-004605/01/202104/30/20243
Survival AnalysisSTAT-002706/01/202105/31/20243
list retrieved 9/21/2022

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