Exploratory Data Analysis for Business Analyst
Quiz by gus reyes
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16 questions
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- Q1What is the purpose of exploratory data analysis for a business analyst?To ignore data and make decisions based on intuitionTo predict future outcomes with certaintyTo manipulate data to fit a certain narrativeTo analyze and summarize data to understand its key characteristics30s
- Q2Which of the following is NOT a common technique used in exploratory data analysis for business analysts?Creating visualizations to summarize dataIdentifying trends and patterns in the dataDetecting outliers and anomalies in the dataMaking definitive conclusions without data support30s
- Q3What is the main goal of exploratory data analysis for business analysts?To manipulate data to support a preconceived conclusionTo discover and understand patterns, trends, and relationships in the dataTo skip data exploration and jump straight to drawing conclusionsTo collect as much data as possible without analyzing it30s
- Q4Which of the following is a common step in exploratory data analysis for business analysts?Jumping straight to creating visualizations without data preparationDrawing definitive conclusions without examining the dataCleaning and preparing the data for analysisIgnoring outliers and anomalies in the data30s
- Q5What is the purpose of creating visualizations in exploratory data analysis for business analysts?To confuse stakeholders with complex graphsTo make the data look more impressive without providing insightsTo hide data outliers and anomaliesTo visually represent data to identify patterns and trends30s
- Q6Why is it important for a business analyst to conduct exploratory data analysis before making business decisions?To ignore data and rely solely on industry trendsTo gain insights and understanding of the data to make informed decisionsTo speed up the decision-making process without data analysisTo make decisions based on intuition and gut feeling30s
- Q7What is the primary source of data used in exploratory data analysis for business analysts?Anecdotes from coworkersGuesswork and random assumptionsStructured and unstructured data sourcesHistorical data from competitors30s
- Q8Which of the following is a key benefit of exploratory data analysis for business analysts?Identifying outliers and anomalies in the dataIgnoring data outliers for simplicitySkipping data exploration to save timeMaking assumptions without data examination30s
- Q9What is the role of exploratory data analysis in the data science process for business analysts?To understand and prepare the data for further analysisTo skip data exploration and move directly to modelingTo predict future outcomes without data examinationTo make business decisions without data insights30s
- Q10Which of the following is a common technique used in exploratory data analysis for business analysts?Jumping straight to building predictive modelsCreating descriptive statistics such as mean, median, and standard deviationIgnoring data outliers and anomaliesSharing raw data without any analysis30s
- Q11What is a common visual tool used in exploratory data analysis for business analysts?Scatter plotLine chartPie chartHistogram30s
- Q12What is the purpose of identifying outliers in exploratory data analysis for business analysts?To investigate data quality issues or potential errorsTo artificially inflate the results of the analysisTo exclude all data points that do not fit the trendTo speed up the data analysis process30s
- Q13What is a common descriptive statistic used in exploratory data analysis for business analysts?ModeVarianceCorrelation coefficientMean30s
- Q14Why is data cleaning an important step in exploratory data analysis for business analysts?To skip unnecessary data processing stepsTo ensure the accuracy and reliability of the analysis resultsTo speed up the data analysis process without validationTo introduce errors into the dataset intentionally30s
- Q15What is the primary objective of exploratory data analysis for business analysts?To skip data preprocessing steps for quick resultsTo understand the structure and key characteristics of the datasetTo manipulate data to fit a preconceived hypothesisTo present data visualizations without analysis30s