Data Wrangling & Data Quality
Turn readable data into analysis-ready data that is trustworthy, reproducible, and traceable: inspect, clean, reshape, integrate, and validate, then complete your own Data Quality & Cleaning Notebook + Data Quality Report.
The course storyline: turn readable data into trustworthy analysis data
The whole course follows one line: Inspect, Clean, Reshape, Integrate, Validate, Document. AI may assist the wrangling; people stay responsible for verification and the conclusion.
- 1. Inspect
What is this data's grain, and what quality problems does it have?
- 2. Clean
How should types, strings, dates, missing values and duplicates be handled?
- 3. Reshape
How should wide and long formats be converted into each other?
- 4. Integrate
How should different sources be merged correctly, avoiding many-to-many?
- 5. Validate
Which rules confirm the data contract has not been broken?
- 6. Document
How can someone else trace and reproduce my judgment?
Your next step
DS 102 · Module 1: Wrangling: From Raw Tables to Analysis-Ready Data
Curriculum
- Module 1Wrangling: From Raw Tables to Analysis-Ready DataStart with grain and cleaning zones, then learn safe type and date cleaning, missing/duplicate judgment, and reshaping.
- Module 2Data Quality: Validation, Integration, and TraceabilityLearn to merge data correctly, normalize units and definitions, build validation rules, and document a traceable cleaning process.
Progress is stored in this browser and does not identify you. It is uploaded only if you join progress sync below.
Sync my progressJoin a session to keep progress across devices and share it with your instructor
Practice and assessment
- Practice A: Type & Date RepairFix a number stored as text and an unparseable date, keeping a trace instead of guessing a fill value.Not done yet
- Practice B: Missing/Duplicate DiagnosisJudge what a missing value means and whether a duplicate row is a true duplicate, and record the handling decision.Not done yet
- Practice C: Join Cardinality ChallengeFind the trap of a site_id repeating across companies, and build the correct join key.Not done yet
- Practice D: Unit Normalization ReviewNormalize units only with a documented conversion rule, and spot same-name columns with different definitions.Not done yet
- Practice E: Data Contract ValidationTurn expected rules into executable checks, and confirm they genuinely fail on bad data.Not done yet
- DS 102 pre-test and post-testPre- and post-tests check how your judgment changes on data quality, keys, dates and reshaping, joins, units and definitions, lineage and AI assistance.Not done yet
- Data Quality & Cleaning Notebook + Data Quality ReportTurn what you learned into your own Data Quality & Cleaning Notebook + Data Quality Report, stating the data, method, verification and limitations.Not done yet
About this course
Outcomes
- Define dataset grain; distinguish formatting from semantic quality; establish raw/staging/clean zones
- Convert types safely; normalize strings; parse dates; preserve failed conversions for review
- Differentiate missing from zero; detect duplicate rows versus duplicate entities; define candidate keys
- Use melt/pivot concepts; understand tidy structure; avoid accidental aggregation
- Use one-to-one, one-to-many, many-to-one joins; detect many-to-many explosions
- Normalize units only with documented conversion rules; distinguish same-name columns with different definitions
- Implement range, allowed-values, uniqueness, referential-integrity, and schema checks
- Produce clean data, validation summary, data dictionary, and an auditable cleaning report
Prerequisites
- No programming background required
- DS 101 is recommended but not required
Who it is for
- NTPU students, faculty and administrative staff who work with structured data
- Learners who completed DS 101 and want to learn systematic data wrangling further
- Excel/spreadsheet users moving toward reproducible analysis
No programming background required. DS 101 is recommended first, but this is advice only and never blocks you from starting.
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.