WGU - Introduction to Analytics D491

WGU - Introduction to Analytics D491 11 studiers recently 4.6 (10 reviews) Students also studied Terms in this set (267) Western Governors UniversityD 333 Save Introduction to Analytics - D491 162 terms bigcab33Preview Ultimate D278 Scripting and Progra...Teacher 114 terms Sara_HoeftPreview D426 Study Guide (Red Text ONLY) 225 terms brattynnPreview WGU D 160 term Mo 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.Data science focuses on developing new algorithms and models, while data analytics focuses on using existing models to analyze data.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
• 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

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 What is the function of a data scientist in an organization?

• To design and maintain data visualizations and

dashboards

• To oversee data governance and compliance
• To work independently to analyze data and make

decisions based on their findings

• To conduct statistical analysis and machine learning

modeling To conduct statistical analysis and machine learning modeling

What is the role of a business intelligence analyst?

• Overseeing data governance and compliance
• Developing and implementing data processing

pipelines

• Designing and maintaining data visualizations and

dashboards

• Conducting statistical analysis and machine learning

modeling Designing and maintaining data visualizations and dashboards What is a primary responsibility of a data engineer?

• Designing and implementing data storage solutions
• Designing and developing data visualizations for

stakeholders

• Analyzing and interpreting data to inform business

decisions

• Developing predictive models using machine learning

algorithms Designing and implementing data storage solutions What is a primary responsibility of a machine learning engineer?

• Developing predictive models using machine learning

algorithms

• Analyzing and interpreting data to inform business

decisions

• Designing and developing data visualizations for

stakeholders

• Designing and implementing data storage solutions

Developing predictive models using machine learning algorithms What is a primary responsibility of a machine learning engineer?

• To pilot the model, refine it, and fully deploy it
• Designing and maintaining data visualizations and

dashboards

• Developing predictive models using machine learning

algorithms

• Business domain knowledge and communication

Developing predictive models using machine learning algorithms What is the role and function of a decision scientist within an organization?

• To oversee the company's human resources and ensure

employee satisfaction

• To analyze data and provide insights to support

informed decision-making

• To develop marketing strategies and increase sales

revenue

• To manage the company's finances and ensure

profitability To analyze data and provide insights to support informed decision-making