Data quality includes

• missing
• inconsistent
• invaild
• implausible(難以置信的)

Data preparation workflow

• 1: How to use data profiling(剖析) methods to
  Characterise data and provide high-level insights
  Investigate data quality so it may be cleaned

• Data preparation workflow includes three steps

• Firstly, Discover
  What data sources and level of detail
  What spatio-temporal coverage(時(shí)空覆蓋) and cost

• Secondly, Wrangle(爭(zhēng)辯)
  **Read in data, reformat(重新格式化), transform(轉(zhuǎn)換), link(鏈接)

• Profile
  Rigorous investigation of data quality

Subset of Data preparation

• I: Look at your data
  Number of rows
  Example of Values
  Data Formate
  Data Type
  How is it encoded?

• 1. Why people must care for Data Encoded
     Explain: If you use anything other than the most basic English text, people may not be able to read your data unless you state the character encoding
• 2. File size & number of rows
• 3. Check the data types
     Check the format yourself
     Don’t rely on heuristics(啟發(fā)法)
     Don’t assume that all your data files use the same format, even if the files come from one source
• 4. Example values

• II: read your data correctly ---->Watch out for special values

• III:Is all the data there?

• 1:Missing values
  Terrible statistical terminology
  Advantages of visualization

• 1.1: Missing at random(MAR)
  -Related to other variables
  – Term is misleading!

• 1.2: Missing completely at random (MCAR)
  – Haphazard
  – Unrelated to values of variable, or other variables

• 1.3: Missing not at random (MNAR)
  – Related to values of the variable itself

• 2:Coverage (e.g. temporal or geographic)

• 2.1: Temporal coverage

• 2.2: Spatialcoverage

• 3:Duplicates(重復(fù)值)

• IV: Rigorously check data quality

• How to write data validation rules
  1.1: Subject-matter special lists typically use free text to describe valid values and explain how to clean them
  1.2: Data scientist may need to write validation & cleaning rules as pseudocode