Course 3: Prepare Data for Exploration
Are you searching for ‘prepare data for exploration course challenge answers’? Look no further! This guide offers all the solutions for Course 3: Prepare Data for Exploration in the Google Data Analytics Professional Certificate. Whether you’re preparing for quizzes or tackling the course challenge, these answers are accompanied by explanations to deepen your understanding.
Below, you’ll find all the modules in this course series, featuring complete and accurate answers with detailed explanations to help you master the core concepts.
Week 1 – Data types and structures
data types and structures coursera quiz answers Weekly Challenge 1 1. A data analyst at a book publisher is working on an urgent report for executives. They are using only historical data. What is the most likely reason for choosing to analyze only historical data? Answers The project has a
Week 2 – Bias, credibility, privacy, ethics, and access
database where data lives coursera week 2 quiz answers 1. Fill in the blank: A preference in favor of or against a person, group of people, or thing is called _____. It is an error in data analytics that can systematically skew results in a certain direction. Answers data collection
Week 3 – Databases: Where data lives
prepare data for exploration weekly challenge 3 answers 1. Primary and foreign keys are two connected identifiers within separate tables. These tables exist in what kind of database? Answers Metadata Primary Relational Normalized 2. When working with data from an external source, what can metadata help data analysts do? Select
Week 4 – Organizing and protecting your data
prepare data for exploration weekly challenge 4 answers 1. Fill in the blank: File-naming conventions are _____ that describe a file’s content, creation date, or version. Answers general attributes frequent suggestions consistent guidelines common verifications 2. A data analytics team uses data about data to indicate consistent naming conventions for
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Related content Answers:
Course 1: Foundations: Data, Data, Everywhere
Course 2: Ask Questions to Make Data-Driven Decisions
Course 4: Process Data from Dirty to Clean
Course 5: Analyze Data to Answer Questions
Course 6: Share Data Through the Art of Visualization
Course 7: Data Analysis with R Programming
Course 8: Google Data Analytics Capstone: Complete a Case Study
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