Data Concepts Pre-Test

You are given a dataset where each row represents a unique customer's first purchase on an e-commerce platform. Which of the following best describes the granularity of this dataset?

• Per transaction

• Per product

• Per unique customer's first purchase

• Per daily summary

A column in your dataset contains values like "2023-04-25", "2023-05-10", and "2023-06-01". You want to calculate the number of days between purchases for each customer. What is the most appropriate data type this column should be converted to for this analysis?

• String (Text)

• Integer

• Date/Time Object

• Categorical

You've just loaded a new dataset and run a command that shows you the count of NaN values for each column. What specific aspect of data quality are you primarily assessing with this action?

• Data type consistency

• Data uniqueness (cardinality)

• Presence and extent of missing values

• Data distribution and outliers

You are analyzing a dataset of customer reviews. You notice that some reviews contain phrases like "amazing!!!" or "so bad :(." Even if the text is fully present and readable, what hidden data quality challenge might this present for sentiment analysis?

• Incorrect data types

• High cardinality

• Ambiguity due to sarcasm or emotional nuance

• Duplicate entries

What is the primary goal of performing initial data inspection steps like viewing the head()/tail() of a dataset, checking dtypes, and looking at isnull().sum()?

• To immediately build predictive models

• To get a quick overview of the data's structure and identify obvious issues

• To perform complex statistical analysis

• To generate final reports for stakeholders