process data from dirty to clean coursera answers
Looking for ‘process data from dirty to clean answers‘? This guide provides all the solutions for Course 4 in the Google Data Analytics Professional Certificate. Whether you’re preparing for quizzes or the course challenge, these answers come with explanations to deepen your understanding.
Below, you will find all the modules in this course series, which include complete, accurate answers along with detailed explanations to help you grasp the core concepts.
Week 1 – The importance of integrity
process data from dirty to clean weekly challenge 1 answers 1. Fill in the blank: If a data analyst is using data that has been _____, the data will lack integrity and the analysis will be faulty. Answers wide compromised public clean 2. A financial analyst imports a dataset to
Week 2 – Sparkling-clean data
process data from dirty to clean weekly challenge 2 answers 1. As part of the data-cleaning process, a data analyst creates a rule to highlight any empty cells in a bright blue color. This is an example of data visualization. Answers True False 2.A data analyst at a nonprofit organization
Week 3 – Cleaning data with SQL
process data from dirty to clean weekly challenge 3 answers 1. Fill in the blank: Data analysts usually use _____ to deal with very large datasets. Answers web browsers spreadsheets SQL word processors 2.What are some of the benefits of using SQL for analysis? Select all that apply. Answers SQL
Week 4 – Verify and report on your cleaning results
process data from dirty to clean weekly challenge 4 answers 1. Verification and reporting come directly before the data-cleaning process. Answers True False 2. What is the first step in the verification process? Answers Compare cleaned data with the original, uncleaned dataset and compare it to what is there now
Not started yet? Enroll today and explore the Official Google Data Analytics Professional Certificate on Coursera.
Related content Answers:
Course 1: Foundations: Data, Data, Everywhere
Course 2: Ask Questions to Make Data-Driven Decisions
Course 3: Prepare Data for Exploration
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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