Module 2 Dirty to Clean Data

Question 1

Data Mapping

Answer 1

The process of matching fields from one data source to another.

Viewing how your data has evolved and transformed between one database and another. Integral to success of data migration and integration.

Question 2

Compatibility

Answer 2

Describes how well two or more datasets are able to work together.

Question 3

Schema

Answer 3

A way of describing how something is organised.

Question 4

Primary key

Answer 4

References a column in which each value is unique.

Question 5

Foreign Key

Answer 5

A field within a table that is a primary key in another table.

Question 6

CONCATENATE

Answer 6

A function that joins together two or more text strings.

Question 7

.csv files

Answer 7

.CSV files are plain text files with an organised table structure that includes rows and columns. The values in each row are separated by commas. This table structure makes them easy to understand, edit, manipulate, and use for data analysis.

Question 8

COUNTIF

Answer 8

Count the number of cells that make the argument you input true.

Question 9

SPLIT

Answer 9

The split function divides tax around a specific character all string, and puts each fragment of tax into a separate style in the row.

=SPLIT(F2, “-“)..

Question 10

Your cleaning checklist

Answer 10

• Determine size of the dataset
• Determine the number of categories or labels
• Identify Missing Data
• Identify unformatted data
• Explore the different data types

Question 11

Data cleaning - Determine size of data set

Answer 11

Large datasets may have more data quality issues and take longer to process. This may impact your choice of data cleaning techniques and how much time to allocate to the project.

Question 12

Determine number of categories or labels

Answer 12

By understanding the number and nature of categories and labels in a dataset, you can better understand the diversity of the dataset. This understanding also helps inform data merging and migration strategies.

Question 13

Identify missing data

Answer 13

Recognizing missing data helps you understand data quality so you can take appropriate steps to remediate the problem. Data integrity is important for accurate and unbiased analysis.

Question 14

Identify unformatted data

Answer 14

Identifying improperly or inconsistently formatted data helps analysts ensure data uniformity. This is essential for accurate analysis and visualization.

Question 15

Explore the different data types

Answer 15

Understanding the types of data in your dataset (for instance, numerical, categorical, text) helps you select appropriate cleaning methods and apply relevant data analysis techniques.

Question 16

Clean Data

Answer 16

Data that is complete, correct, and relevant to the problem being solved.

Question 17

Conditional Formatting

Answer 17

A spreadsheet tool that changes how cells appear when value meet specific conditions.

Question 18

Data Merging

Answer 18

The process of matching fields from one data source to another.

Question 19

Data Validation

Answer 19

A tool for checking the accuracy and quality of data.

Question 20

Delimiter

Answer 20

A character that indicates the beginning or end of a data item.

Question 21

Dirty Data

Answer 21

Data that is incomplete, incorrect, or irrelevant to the problem to be solved.

Question 22

Duplicate Data

Answer 22

Any record that inadvertently shares data with another record.

Question 23

Field Length

Answer 23

A tool for determining how many characters can be keyed into a spreadsheet field.

Question 24

Incomplete Data

Answer 24

Data that is missing important fields.

Question 25

Inconsistent data

Answer 25

Data that uses different formats to represent the same thing.

Question 26

Incorrect/Inaccurate Data

Answer 26

Data that is complete but inaccurate.

Question 27

TRIM

Answer 27

A function that removes leading,trailing, and repeated space in data.

Question 28

Unique

Answer 28

A value that can’t have a duplicate.