WGU D491 INTRODUCTION TO ANALYTICS EXAM A, B, C

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1 WGU D491 INTRODUCTION TO ANALYTICS EXAM A, B, C &

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WGU D491 INTRODUCTION TO ANALYTICS EXAM A

Which comparison describes the difference between data analytics and data science?

• Data analytics focuses on descriptive analysis, while data science focuses on prescriptive

analysis.

• Data analytics is the process of analyzing data to extract insights, while data science involves

building and testing models to make predictions.

• Data analytics focuses on statistics, and data science mainly focuses on qualitative

reasoning.

• Data science involves analyzing data from structured sources, while data analytics involves

analyzing data from unstructured sources.Data analytics is the process of analyzing data to extract insights, while data science involves building and testing models to make predictions.Which type of data analytics project aims to determine why something happened in the past?

• Diagnostic
• Descriptive 1 / 4

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• Predictive
• Prescriptive

Diagnostic What are the different types of data analytics projects?

• Data warehousing, data mining, data visualization, and business intelligence
• Regression analysis, time series analysis, text analytics, and network analysis
• Data collection, data cleaning, data transformation, and data visualization
• Descriptive, diagnostic, predictive, and prescriptive analytics

Descriptive, diagnostic, predictive, and prescriptive analytics What is the difference between exploratory and confirmatory data analytics projects?

• Exploratory projects involve testing hypotheses and finding patterns in data, while

confirmatory projects involve verifying existing hypotheses.

• Exploratory projects involve analyzing data that is already structured, while confirmatory

projects involve analyzing unstructured data.

• Exploratory projects involve analyzing large datasets, while confirmatory projects involve

analyzing smaller datasets.

• Exploratory projects involve analyzing data from a single source, while confirmatory projects

involve integrating data from multiple sources.Exploratory projects involve testing hypotheses and finding patterns in data, while confirmatory projects involve verifying existing hypotheses.

NOT CORRECT 2 / 4

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3 Which project is considered a data analytics project?

• Developing a recommendation system to suggest new products to customers based on their

past purchases

• Creating a dashboard to visualize sales data and monitor inventory levels for a grocery store

chain

• Building a predictive model to forecast stock prices for a financial services company
• Designing a database schema to store customer information for a retail store

Creating a dashboard to visualize sales data and monitor inventory levels for a grocery store chain Why is quality control/assurance crucial for data engineers in a data analytics project?

• It ensures that the data is analyzed in a timely manner.
• It ensures that the data is stored in a secure location.
• It ensures that the data is accurate and reliable.
• It ensures that the data is accessible to all stakeholders.

It ensures that the data is accurate and reliable.What does a data analyst do in a data analytics project?

• Conducts exploratory data analysis to identify trends and patterns
• Focuses on building machine learning models
• Oversees data governance and data quality assurance
• Designs and develops databases and data pipelines

Conducts exploratory data analysis to identify trends and patterns 3 / 4

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4 What is data analytics?

• The process of analyzing data to extract insights
• The process of encrypting data to keep it secure
• The process of storing data in a secure location for future use
• The process of collecting data from various sources

The process of analyzing data to extract insights What is data science?

• The practice of using statistical methods to extract insights from data
• A field that involves creating data visualizations to provide insights
• The process of creating computer programs to automate tasks
• The study of how computers interact with human language

The practice of using statistical methods to extract insights from data How is data science different from data analytics?

• Data science focuses more on tracking experimental data, and data analytics is based on

statistical methods and hypotheses.

• Data science focuses on developing new algorithms and models, while data analytics

focuses on using existing models to analyze data.

• Data science focuses more on data visualization, while data analytics focuses on data

cleaning and preprocessing.

• Data science involves creating new algorithms, while data analytics uses existing statistical

methods.

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