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Western University · London · data-science concepts
Western University DS1000A/B — Data Science Concepts
Work on numerical summaries, normal areas, sampling, confidence intervals and supplied regression lines. Python, data visualization and bootstrap labs in the official course need its own resources.
28 original question families · 84 parameter instances
Practise this course →Course alignment and coverage
2026–27 official offering index and public Summer 2026 DS1000A outline checked 25 October 2026. The dated summer outline supplies the topic baseline; the current fall/winter section outline remains unverified on OWL.
Across this collection: 156 distinct original families and 468 parameter instances. Courses share topic banks; translations and shared mappings are not extra questions.
Supported learning objectives
Descriptive statistics
Calculate means, medians and weighted summaries.
Variance and standard deviation
Distinguish sample and population denominators and scale changes.
Probability
Use complements, independence and conditional probability.
Sampling and standard error
Calculate standard errors and sample-size effects.
Confidence intervals for a mean
Calculate known-sigma z intervals; critical values are supplied.
Correlation and regression
Use a supplied least-squares line and interpret residuals and r².
Normal areas and quantiles
Use supplied rounded normal tables for complements, central areas, standardization and a percentile.
What is not covered
Numerical foundations only; no Python notebooks, graphics, randomization/bootstrap, real-data projects or statistical critique. Summer 2026 does not establish the current fall/winter lab sequence. DS1000 is not the former SS1024 course code.
An original worked example
Find the mean of 2, 4, 6, 8.
Worked solution
Answer: 5
- The total is 20.
- Mean=20/4=5.
Official course sources
Independent, original, free self-study practice. Not affiliated with or endorsed by this university; these are not official exam questions. Topic alignment does not establish transfer credit, a current section’s weekly syllabus, full course coverage or a grade prediction. Follow your instructor’s rules and use official course materials for assessed work. No live AI chat or paid access is required.