QuantHub
QuantHub
Support“QuantHub is an exceptional learning tool. The clear, modular design helps students move quickly while giving faculty valuable insight into their progress.”
Applied Data Science
Students learn how to frame data questions, prepare and evaluate datasets, apply statistical and modeling methods, and communicate findings with clear evidence, uncertainty, and limitations.
What You'll Gain
Stronger Data Questioning
Students learn how to turn broad problems into clear research questions, success criteria, and analysis plans.
Practical Data Preparation Skills
Students practice wrangling, joining, profiling, cleaning, and validating data before analysis begins.
Applied Modeling and Inference
Students use probability, simulation, regression, classification, clustering, and other methods to support evidence-based claims.
Clearer Data Communication
Students build charts, narratives, slides, and recommendations that explain what the data shows—and what it does not.
Modules
The curriculum is organized into flexible, task-based modules. Choose the ones that align with your objectives and map them directly into your existing syllabus.
Module 1: Conducting a Survey and Summarizing Data
Design survey questions, summarize responses, and communicate early findings.
- Problem statements and success criteria
- Clear survey questions and response options
- Frequency, relative frequency, and cumulative frequency tables
- Modal categories and probability statements
- Charts for survey results
Module 2: Bivariate Analysis and Conditional Probability
Analyze relationships between two variables and support claims with two-way evidence.
- When to use bivariate analysis
- Combining and wrangling datasets
- Contingency tables
- Joint and conditional probabilities
- Highlighted charts for key takeaways
Module 3: Random Sampling, Simulation, and Inference
Use sampling and simulation to make evidence-based claims about association.
- Inferential research questions
- Simple random sampling
- Simulation for bivariate analysis
- P-values and practical significance
- Narratives for statistical findings
Module 4: Classification and Decision Trees
Build and evaluate classification rules for predicting categories.
- Classification task design
- Data cleaning for models
- Misclassification rates
- Predictions for new examples
- Sequential visual storytelling
Module 5: Linear Regression
Use regression to model continuous outcomes and explain prediction uncertainty.
- Regression objectives and metrics
- Ethical data collection plans
- Residuals and R²
- Coefficients and prediction intervals
- Charts that show uncertainty
Module 6: Comparing Distributions
Compare quantitative variables across groups using summaries and visuals.
- Statistical comparison questions
- Metadata and secondary data
- Quantiles and box plots
- Separation and overlap
- Big ideas and calls to action
Module 7: Measuring Strength of Association
Analyze relationships between two quantitative variables.
- Association research questions
- Binning quantitative variables
- Correlation and scatterplots
- Trend lines, interpolation, and extrapolation
- Charts for numeric relationships
Module 8: Time-Series Analysis
Analyze patterns, variability, and stories in data over time.
- Time-series research questions
- Aggregation by time period
- Moving averages
- Trend and variability observations
- Narratives around time-series charts
Module 9: Cluster Analysis
Use unsupervised learning to identify meaningful groups in data.
- Clustering problem statements
- Encoding categorical data
- K-means evaluation
- Segment prioritization
- Charts for target segments
Module 10: Summarizing Distributions
Explore one-variable distributions with data quality and limitation checks.
- Univariate analysis tasks
- Data quality exploration
- Center and spread
- Histograms and dot plots
- Chart titles and takeaway text
Module 11: Measuring Change Over Time
Prepare time-based data and describe meaningful change.
- Time-series data requirements
- Interpolation for missing values
- Percentage change
- Patterns of change
- Charts for change over time
Module 12: Comparing Proportions and Generalizing Findings
Use proportions, confidence intervals, and p-values to evaluate claims.
- Population and sample size
- Selecting relevant data
- Simulation p-values
- Confidence intervals
- Presentation slides for findings
Module 13: Study Design and Chi-Squared Testing
Analyze categorical association while recognizing causal limits.
- Association questions and sampling plans
- Observational data collection
- Chi-squared testing
- Limits of cause-and-effect claims
- Presentation hooks
Module 14: Collecting, Summarizing, and Comparing Data
Turn structured and unstructured inputs into usable data stories.
- Images as data
- Dataset creation and organization
- Comparing center and spread
- Written comparison summaries
- Metrics as story characters
Module 15: Geographic Analysis
Analyze spatial and time-based patterns with maps.
- Geographic time-series questions
- Geographic joins
- Maps over time
- Trend observations on maps
- Map encoding choices
The Shift in Data Science Education
Data science is no longer just about running models or producing charts. Students must understand how questions are framed, how data is collected, how uncertainty is measured, and how claims should be communicated.
Students must now do more than compute results. They must:
- Ask clear, testable questions
- Prepare and validate data responsibly
- Choose methods that fit the problem
- Interpret uncertainty and significance
- Communicate findings with appropriate limits
Data science principles have not changed. Evidence, context, ethics, and clarity still matter.
Modern data science increases the importance of defending every claim from question to evidence to limitation.
A Look Inside the Learning Experience
Students learn through applied scenarios, hands-on analysis tasks, and communication exercises designed to build real-world data reasoning.
Practical Learning Resources
Students work with surveys, joined datasets, models, time series, clusters, maps, and statistical studies.
Interactive Assessment and Feedback
Students answer questions that test method choice, interpretation, uncertainty, and strength of claim.
Hands-On Validation Exercises
Students check data quality, evaluate model outputs, critique visualizations, and revise claims based on evidence.
Don’t Just Take Our Word for It…
Dr. Uma Gupta
Associate Professor USC Upstate
“QuantHub’s modules put faculty in the driver’s seat. They’re flexible, practical, and meet educators where they are in their AI journey.”
Shani Robinson
Senior Associate Dean, SHSU
“I thought the AI essentials were useful, given how large of a role they play in our lives”
Chloe
Student at UA
“Our school was on the failing list. After using QuantHub, students were excited to see their Science ACT scores jump—it completely changed how they approached data in labs.”
Destiny Langford
Tuscaloosa City Schools
“QuantHub is an easy resource to incorporate valuable lessons into each class. I don’t have to lesson plan around it, and it doesn’t require any extra work on my end.”
Hannah Adams
McAdory High School
“Since adopting QuantHub, I haven’t had a single student banging on my door saying ‘I can’t understand this.’ Previously, Excel questions consumed my office hours.”
Greg
MIS Professor
“QuantHub has completely freed up my ability to do more in class. We spent a lot more time on AI this semester than we ever have before.”
Trent
MIS Professor
What I like most about the software is the gamification aspect. Let’s be honest; learning about data analytics isn’t always fun, but we know how valuable it is. The gamification aspect makes learning about this topic much more fun and engaging!
Angela Santa Cruz
Systems Training Specialist
Bring AI Into Your Accounting Curriculum Without Disruption
We will work with you to map AI capabilities to your existing syllabus, align with core accounting principles, and prepare students for AI-assisted workflows.