Exploratory Data Analysis & Statistical Thinking
Build statistical thinking that separates observed differences from supported conclusions, while exploring data and communicating uncertainty: from center and distribution, variation, and outliers, to population/sample/sampling bias, correlation and confounding, confidence-interval intuition, and statistical vs practical significance, then complete your own Exploratory Data Analysis Report.
The course storyline: from what the data looks like to trustworthy statistical judgment
The whole course follows one line: Question, Distribution, Variation, Outlier, Sample, Association, Uncertainty, Significance, Interpretation, Action. AI may help with calculation; people stay responsible for verification and the conclusion.
- 1. Question
What analytical question does this data answer, and who is the audience?
- 2. Distribution
What do this variable's center, spread, and shape look like?
- 3. Outlier
What outliers are present? Are they errors, exceptions, or an important signal?
- 4. Sample
Can this sample represent the target population?
- 5. Association
What is the relationship between two variables? Is there a confounder?
- 6. Interpretation
What is observation, what is interpretation, and what is still unknown?
- 7. Action
What next action does this evidence support?
Your next step
DS 104 · Module 1: Explore Data: Distributions, Variation, and Outliers
Curriculum
- Module 1Explore Data: Distributions, Variation, and OutliersStart with the purpose of EDA, then learn center and distribution, variation, and how to investigate outliers in a Guided Lab EDA profile.
- Module 2From Samples to Judgment: Association, Uncertainty, and LimitsLearn population, sample and sampling bias, correlation and confounding, confidence-interval intuition, statistical vs practical significance, and complete an Independent Lab EDA report.
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Practice and assessment
- Practice A: Distribution ProfileProfile a skewed group's distribution and judge whether the mean fairly represents it.Not done yet
- Practice B: Robust Summary ComparisonCompare mean/standard deviation against median/IQR to find the more robust summary.Not done yet
- Practice C: Outlier InvestigationInvestigate the likely source of outliers before deciding whether to remove them.Not done yet
- Practice D: Sampling Bias ScenarioFind an under-sampled stratum and state how it would affect inference.Not done yet
- Practice E: Correlation vs Causation RewriteRewrite a causal claim into an evidence statement bounded by the data, naming a confounder.Not done yet
- DS 104 pre-test and post-testPre- and post-tests check how your judgment changes on center and distribution, outliers and sampling, population and association, confounding and uncertainty, data quality and significance, and EDA/AI review.Not done yet
- Exploratory Data Analysis ReportTurn what you learned into your own Exploratory Data Analysis Report, stating the problem, data, method, verification and limitations.Not done yet
About this course
Outcomes
- Use EDA to generate/check questions, not to prove a preselected story
- Compare robust and non-robust summaries; connect metrics to distribution shape
- Describe spread; avoid interpreting a mean without variability
- Investigate outliers before removal; document rationale and sensitivity
- Profile distributions, missingness, group differences, and suspicious observations
- Define target population; assess who is represented and who is missing
- Interpret association without causal overreach; identify plausible confounders
- Separate evidence of difference from magnitude and decision relevance
Prerequisites
- No programming background required
- DS 101 and DS 102 are recommended
Who it is for
- NTPU students, faculty and administrative staff who work with structured data
- Learners who completed DS 101 and DS 102 and want to build stronger statistical judgment
- Excel/spreadsheet users who analyze data and want more rigorous verification
Certificate
Earns a course completion certificate (L1); a capability certificate (L2) is also available through additional artifact review.
Requirements to earn it
- Complete all 2 course modules
- Pass the post-test assessment at 80% or higher
- Submit a project artifact and pass review
Course feedback
It takes 2 to 3 minutes and is used to improve the course.