Data Science curriculum

A practical data science track covering the data cycle, Python analysis, probability, modeling, visualization, machine learning, and ethics.

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High school advanced / intro college / Python-based

Data Science curriculum

A practical data science track covering the data cycle, Python analysis, probability, modeling, visualization, machine learning, and ethics.

Pacing

8 units, 20-28 weeks self-paced

Units

8 unit sequence

Practice

16 checked answers

Support

Self-paced or tutor-guided

Outcomes

Move from question to data to analysis to responsible communication.
Use Python tools for cleaning, summaries, visualization, and modeling.
Recognize ethical risks in data collection, inference, and automation.

Unit sequence

Built for independent progress first, then tutor support where the student gets stuck.

01
The data science cycle
- Define questions, stakeholders, data, analysis, and reporting.
- Recognize where bias can enter the process.
Practice: Turn a vague question into measurable variables and a data plan.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "The data science cycle"?
Use notebooks or scripts deliberately.Define questions, stakeholders, data, analysis, and reporting.Load, inspect, and summarize tabular data.Recognize where bias can enter the process.
Practice artifact
Which practice artifact should you produce for "The data science cycle" before asking a tutor for help?
Skip the unit and start a new subject.Turn a vague question into measurable variables and a data plan.Memorize vocabulary without checking any examples.Watch videos without writing or solving anything.
02
Python for data work
- Use notebooks or scripts deliberately.
- Load, inspect, and summarize tabular data.
Practice: Import a dataset and produce a data dictionary.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "Python for data work"?
Use notebooks or scripts deliberately.Define questions, stakeholders, data, analysis, and reporting.Recognize where bias can enter the process.Load, inspect, and summarize tabular data.
Practice artifact
Which practice artifact should you produce for "Python for data work" before asking a tutor for help?
Import a dataset and produce a data dictionary.Memorize vocabulary without checking any examples.Watch videos without writing or solving anything.Skip the unit and start a new subject.
03
Cleaning and preprocessing
- Handle missing values, types, outliers, and joins.
- Record every transformation.
Practice: Clean a messy CSV and write a reproducible cleaning note.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "Cleaning and preprocessing"?
Define questions, stakeholders, data, analysis, and reporting.Use notebooks or scripts deliberately.Handle missing values, types, outliers, and joins.Recognize where bias can enter the process.
Practice artifact
Which practice artifact should you produce for "Cleaning and preprocessing" before asking a tutor for help?
Watch videos without writing or solving anything.Memorize vocabulary without checking any examples.Clean a messy CSV and write a reproducible cleaning note.Skip the unit and start a new subject.
04
Visualization
- Choose charts that match variables and questions.
- Avoid misleading scales and encodings.
Practice: Create three charts for one dataset and explain what each does better.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "Visualization"?
Recognize where bias can enter the process.Define questions, stakeholders, data, analysis, and reporting.Avoid misleading scales and encodings.Use notebooks or scripts deliberately.
Practice artifact
Which practice artifact should you produce for "Visualization" before asking a tutor for help?
Watch videos without writing or solving anything.Memorize vocabulary without checking any examples.Create three charts for one dataset and explain what each does better.Skip the unit and start a new subject.
05
Probability and inference
- Use sampling, uncertainty, intervals, and tests.
- Avoid overclaiming from weak evidence.
Practice: Estimate a population quantity and explain uncertainty clearly.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "Probability and inference"?
Define questions, stakeholders, data, analysis, and reporting.Recognize where bias can enter the process.Use notebooks or scripts deliberately.Use sampling, uncertainty, intervals, and tests.
Practice artifact
Which practice artifact should you produce for "Probability and inference" before asking a tutor for help?
Memorize vocabulary without checking any examples.Skip the unit and start a new subject.Estimate a population quantity and explain uncertainty clearly.Watch videos without writing or solving anything.
06
Regression and prediction
- Fit simple models and evaluate errors.
- Separate explanatory and predictive goals.
Practice: Train a baseline model and inspect residuals or errors.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "Regression and prediction"?
Define questions, stakeholders, data, analysis, and reporting.Recognize where bias can enter the process.Use notebooks or scripts deliberately.Separate explanatory and predictive goals.
Practice artifact
Which practice artifact should you produce for "Regression and prediction" before asking a tutor for help?
Train a baseline model and inspect residuals or errors.Skip the unit and start a new subject.Watch videos without writing or solving anything.Memorize vocabulary without checking any examples.
07
Machine learning overview
- Compare classification, clustering, and neural network basics.
- Use train/test splits and validation.
Practice: Build a simple classifier and report performance honestly.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "Machine learning overview"?
Use notebooks or scripts deliberately.Recognize where bias can enter the process.Compare classification, clustering, and neural network basics.Define questions, stakeholders, data, analysis, and reporting.
Practice artifact
Which practice artifact should you produce for "Machine learning overview" before asking a tutor for help?
Skip the unit and start a new subject.Build a simple classifier and report performance honestly.Watch videos without writing or solving anything.Memorize vocabulary without checking any examples.
08
Ethics and capstone
- Audit privacy, fairness, provenance, and misuse risk.
- Present findings for a real audience.
Practice: Complete a capstone report with methods, limitations, and recommendations.
Original practice checks
Answer in PeerTutor, then use the explanation or bring the miss to a tutor.
2 checked answers
Unit readiness
Which target best matches the Data Science unit "Ethics and capstone"?
Use notebooks or scripts deliberately.Recognize where bias can enter the process.Define questions, stakeholders, data, analysis, and reporting.Present findings for a real audience.
Practice artifact
Which practice artifact should you produce for "Ethics and capstone" before asking a tutor for help?
Watch videos without writing or solving anything.Complete a capstone report with methods, limitations, and recommendations.Skip the unit and start a new subject.Memorize vocabulary without checking any examples.