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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.

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  1. 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.

  2. 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.

  3. 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.

  4. 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.

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