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AI and machine learning · 30 lessons · up to 10 minutes each

Python for Data Science

A planned practical path through numerical arrays, tabular data, and quality checks.

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Python for Data Science

Permanent access to the lessons published in this track when you buy. Future lessons are not included. 0 of 30 lessons are published; 0 are in owner review and 30 are Coming Soon.

$29.0030 lessons still in progress

Beginner

Lessons 1–10

  1. 01

    Python for Data Science: Sketch array shape and dtype

    Topic: array shape and dtype

    Draw a small map of array shape and dtype in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner4 minComing Soon
  2. 02

    Python for Data Science: Name the parts of tabular rows and columns

    Topic: tabular rows and columns

    Label the main pieces in tabular rows and columns in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner5 minComing Soon
  3. 03

    Python for Data Science: Trace an example of missing value inspection

    Topic: missing value inspection

    Follow one input through missing value inspection in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner6 minComing Soon
  4. 04

    Python for Data Science: Compare choices for selection and filtering

    Topic: selection and filtering

    Write down one practical difference between selection and filtering in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner7 minComing Soon
  5. 05

    Python for Data Science: Find the boundary in grouping and aggregation

    Topic: grouping and aggregation

    Mark where grouping and aggregation in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner8 minComing Soon
  6. 06

    Python for Data Science: Inspect an example of joins and alignment

    Topic: joins and alignment

    Annotate a minimal example of joins and alignment in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner9 minComing Soon
  7. 07

    Python for Data Science: Connect vectorized transformations to a neighboring concept

    Topic: vectorized transformations

    Show how vectorized transformations in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner10 minComing Soon
  8. 08

    Python for Data Science: Spot a failure mode in summary statistics

    Topic: summary statistics

    Describe one likely failure for summary statistics in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner3 minComing Soon
  9. 09

    Python for Data Science: Read an official example of reproducible data loading

    Topic: reproducible data loading

    Summarize an official example of reproducible data loading in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner5 minComing Soon
  10. 10

    Python for Data Science: Choose a use case for data quality assertions

    Topic: data quality assertions

    Match one small use case to data quality assertions in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Beginner6 minComing Soon

Intermediate

Lessons 11–20

  1. 11

    Python for Data Science: Build an example with array shape and dtype

    Topic: array shape and dtype

    Create a small working example that uses array shape and dtype in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate4 minComing Soon
  2. 12

    Python for Data Science: Compose tabular rows and columns with another component

    Topic: tabular rows and columns

    Combine tabular rows and columns in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate5 minComing Soon
  3. 13

    Python for Data Science: Configure missing value inspection for a small case

    Topic: missing value inspection

    Set up missing value inspection in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate6 minComing Soon
  4. 14

    Python for Data Science: Validate inputs around selection and filtering

    Topic: selection and filtering

    Add a check around selection and filtering in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate7 minComing Soon
  5. 15

    Python for Data Science: Use grouping and aggregation in a short workflow

    Topic: grouping and aggregation

    Add grouping and aggregation in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate8 minComing Soon
  6. 16

    Python for Data Science: Refactor joins and alignment into a reusable unit

    Topic: joins and alignment

    Extract joins and alignment in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate9 minComing Soon
  7. 17

    Python for Data Science: Exercise vectorized transformations with sample data

    Topic: vectorized transformations

    Run sample data through vectorized transformations in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate10 minComing Soon
  8. 18

    Python for Data Science: Observe summary statistics outputs

    Topic: summary statistics

    Capture the result of summary statistics in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate3 minComing Soon
  9. 19

    Python for Data Science: Recover from a reproducible data loading failure

    Topic: reproducible data loading

    Handle one expected failure from reproducible data loading in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate5 minComing Soon
  10. 20

    Python for Data Science: Document data quality assertions behavior

    Topic: data quality assertions

    Write a short usage note for data quality assertions in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Intermediate6 minComing Soon

Advanced

Lessons 21–30

  1. 21

    Python for Data Science: Test a boundary around array shape and dtype

    Topic: array shape and dtype

    Add an edge-case test for array shape and dtype in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced4 minComing Soon
  2. 22

    Python for Data Science: Harden tabular rows and columns

    Topic: tabular rows and columns

    Add the relevant access, safety, or validation control to tabular rows and columns in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced5 minComing Soon
  3. 23

    Python for Data Science: Measure missing value inspection

    Topic: missing value inspection

    Record a small baseline for missing value inspection in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced6 minComing Soon
  4. 24

    Python for Data Science: Reduce cost in selection and filtering

    Topic: selection and filtering

    Change one measurable cost in selection and filtering in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced7 minComing Soon
  5. 25

    Python for Data Science: Diagnose a grouping and aggregation failure

    Topic: grouping and aggregation

    Use an observable signal to explain a failure in grouping and aggregation in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced8 minComing Soon
  6. 26

    Python for Data Science: Make joins and alignment repeatable

    Topic: joins and alignment

    Show that rerunning joins and alignment in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced9 minComing Soon
  7. 27

    Python for Data Science: Review trade-offs in vectorized transformations

    Topic: vectorized transformations

    Compare two documented approaches to vectorized transformations in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced10 minComing Soon
  8. 28

    Python for Data Science: Improve summary statistics maintainability

    Topic: summary statistics

    Refine summary statistics in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced3 minComing Soon
  9. 29

    Python for Data Science: Verify reproducible data loading end to end

    Topic: reproducible data loading

    Check that a complete small flow using reproducible data loading in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced5 minComing Soon
  10. 30

    Python for Data Science: Audit data quality assertions against its source docs

    Topic: data quality assertions

    Check the implementation of data quality assertions in the context of Python arrays, tables, and reproducible data preparation; save a short example or check that demonstrates the result.

    Advanced6 minComing Soon

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