Data Cleaning and techniques

Your data is considered ___ when you did not remove any data from the data set.

• clean

• raw

• untouchable

• original

Data scientists spend most of their time doing ______

• finding and getting data

• thinking about problems and questions to ask

• preparing and cleaning data

• interpreting analysis results

Why must we worry about missing data?

• Can add bias to a model

• Inaccurate results

• Need to prepare training data

• Analysis not perfect

Data tidying is the process of structuring data in a way that makes it easy to analyze and use.

• True

• False

What action should be taken to handle duplicate data?

• Using Text-to-Columns

• Applying filters

• Removing duplicates

• Formatting data

What is data cleaning?

• The process of organizing and structuring data in a dataset

• The process of analyzing and interpreting data in a dataset

• The process of collecting and gathering data for a dataset

• The process of identifying and correcting or removing errors, inconsistencies, and inaccuracies in datasets to improve their quality and reliability.

What is data filtering and sorting?

• Data filtering and sorting involve organizing data in a random order.

• Data filtering and sorting involve removing all data from a dataset.

• Data filtering and sorting involve rearranging data based on alphabetical order.

• Data filtering and sorting involve selecting a subset of data based on specific criteria and arranging it in a specific order.

True or False: Data cleaning is the process of removing errors and inconsistencies from data.

• Data cleaning is not necessary

• False

• Data cleaning only removes duplicates

• True

We can do visualization on a dashboard, but not in a single question?

• TRUE

• FALSE

Raw data is ______

• A. Data that is ready for analysis
• B. Data that can be merged, subset and transformed
• C. Data that is often or difficult to be analyzed 
• D. May be standards for processing