WGU - Introduction to Analytics D491

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

What is a primary responsibility of a data analyst?
- Developing data visualizations for stakeholders
- Designing and implementing data storage solutions
- Conducting statistical analysis to identify patterns and trends
- Developing predictive models using machine learning algorithms

Conducting statistical analysis to identify patterns and trends

What component of a data analytics project is typically completed by a data analyst?
- To collect and store data for the organization
- To make decisions based on the insights derived from data analysis
- To clean and preprocess data to prepare it for analysis
- To design and implement machine learning algorithms

To clean and preprocess data to prepare it for analysis

Which task is the data analyst responsible for within a data analysis project?
- Developing and implementing software applications
- Conducting statistical analyses and generating reports
- Creating the project's overall goals and objectives
- Collecting, cleaning, and loading customer data into a data warehouse

Conducting statistical analyses and generating reports

Which data migration skill is necessary for database administrators?
- Ensuring that the database remains secure
- Developing and implementing database software
- Transferring data between different systems or formats
- Troubleshooting network issues within the system

Transferring data between different systems or formats

Which job skill is necessary for a researcher in a data analytics project?
- Analyzing and interpreting data to inform questions
- Designing and implementing data storage solutions
- Identifying business needs and requirements
- Ensuring data privacy and security

Analyzing and interpreting data to inform questions

What are the necessary skills for partners in a data analytics project?

- Machine learning algorithm development
- Data visualization and dashboard development
- Cloud infrastructure management and automation
- Business domain knowledge and communication

Business domain knowledge and communication

Which groups make up the key stakeholders in a data analytics project?

- Shareholders and investors
- Project team members and senior management
- Competitors and regulatory agencies
- Manufacturers and suppliers

Project team members and senior management

What role do stakeholders play in the project cycle?

- Create the project plan and schedule
- Define the project scope and objectives
- Execute the project tasks
- Provide guidance and feedback throughout the project

Provide guidance and feedback throughout the project

Which stakeholder should conduct literature reviews for a data analytics project?
- Database administrator
- End user
- Project sponsor
- Researcher

Researcher

Why is a project sponsor a key stakeholder in a data analytics project?

- They ensure that the project aligns with business goals and objectives.
- They provide funding for the project.
- They are the primary users of the project's outputs.
- They are responsible for implementing the project.

They ensure that the project aligns with business goals and objectives.

How does a data analyst interact with stakeholders during a data analytics project?
- By presenting data analysis results in an easily understandable format
- By providing technical details of data analysis methods
- By making decisions on behalf of stakeholders
- By delegating tasks to stakeholders

By presenting data analysis results in an easily understandable format

What role does a project manager play within a data analytics project?

- Oversee the project team and ensure the project is completed on time and within budget
- Provide funding and resources for the project
- Interpret the project results and make recommendations for future projects
- Collect and analyze data

Oversee the project team and ensure the project is completed on time and within budget

How do stakeholders interact with data analytics projects?
- By providing funding for the project
- By providing consultations at the start of the project
- By providing input throughout the project lifecycle
- By providing finances to complete data visualizations

By providing input throughout the project lifecycle

Why are financial operation stakeholders important in a data analytics project?
- They provide financial resources for the project.
- They are responsible for data cleaning and migration within a project.
- They help design and implement data analytics projects.
- They interpret data and provide insights to improve financial performance.

They interpret data and provide insights to improve financial performance.

In which phase of the data mining process does the data science team investigate the problem, develop context and understanding, learn about available data sources, and formulate initial hypotheses?
Model execution
Data preparation
Model planning
Discovery

Discovery

What is the primary purpose of the discovery phase in the data science process?
To develop interactive visualizations for stakeholder presentations
To evaluate and optimize data-driven predictive models
To clean and preprocess the data for analysis
To understand the business problem and develop initial hypotheses

To understand the business problem and develop initial hypotheses

Which question of interest is appropriate for a data analytics project to increase a store's sales?
What are the store's best-selling products?
Should the store expand to a new location?
Which customer segments will most likely respond to a marketing campaign?
How can the store's social media presence be improved?

Which customer segments will most likely respond to a marketing campaign?

A data analyst works at an e-commerce company that wants to understand its customer churn rate. Their manager has tasked them with conducting a data analytics project to identify customers at risk of churn and offer these customers targeted promotions to retain their business. What is the primary purpose of the data analytics project's results in this scenario?

To optimize inventory management
To identify customer preferences
To compare the company's churn rate to industry benchmarks
To predict customer churn risk

To predict customer churn risk

A data analyst works at an e-commerce company that wants to understand its customer churn rate. Their manager has tasked them with conducting a data analytics project to identify customers at risk of churn and offer these customers targeted promotions to retain their business. What is the most suitable form of deliverable in this scenario?

Supply chain improvements
Lists of at-risk customers
Monthly sales reports
Updated website design

Lists of at-risk customers

A retail company wants to improve its sales and customer satisfaction by analyzing customer data. The company hired a data analytics team, which has access to the company's customer database, including transaction records, demographic information, and customer feedback. The data analytics team will work closely with the marketing and IT departments to create actionable insights for the company. The team has three months to complete the project, and the company's budget allows purchasing additional software tools or training, if necessary. What is the most critical resource for the data analytics project?

The company's inventory records
The company's financial statements
The customer database
The employee records

The customer database

A retail company wants to improve its sales and customer satisfaction by analyzing customer data. The company hired a data analytics team, which has access to the company's customer database, including transaction records, demographic information, and customer feedback. The data analytics team will work closely with the marketing and IT departments to create actionable insights for the company. The team has three months to complete the project, and the company's budget allows purchasing additional software tools or training, if necessary. Which constraint should impact the data analytics project the most?

Limited budget for purchasing additional software tools
Insufficient time for comprehensive data analysis
Lack of collaboration between departments
Limited access to demographic data on customers

Insufficient time for comprehensive data analysis

An online retail company wants to use data analytics to improve customer satisfaction and increase sales. The company has collected data on customer behavior, purchase history, and customer support interactions. Which outcome is most appropriate for the online retail company's data analytics project?

- Comparing the company's pricing strategy with competitors'
- Understanding the most popular products sold by the company
- Identifying the number of unique customers who visited the website in the past month
- Increasing customer satisfaction and sales through targeted recommendations and improved customer support

Increasing customer satisfaction and sales through targeted recommendations and improved customer support

Which phase of the data analytics lifecycle involves cleaning data, normalizing datasets, and performing transformations?
Data modeling
Data preparation
Data evaluation
Data exploration

Data preparation

What is the primary purpose of the data preparation phase in a data analytics project?
To clean, normalize, and transform data
To build and refine predictive models
To visualize and explore data patterns
To evaluate the performance of models

To clean, normalize, and transform data

Which information tool is a possible source of data in a data analytics project?
Company logo designs
Consumer perception survey questions
Marketing slogans
Corporate information system

Corporate information system

Which data sources would be most relevant for analyzing factors affecting patient satisfaction in a healthcare company?

- Web log data, call-center records, and survey responses
- Printing press run records, noise levels, and census data
- Credit card charge records, telephone call detail records, and point-of-sale data
- Warranty claims, weather data, and economic data

Web log data, call-center records, and survey responses

A company in the renewable energy industry is working on a data analytics project to identify which areas are more likely to adopt solar power. The data science team needs to gather relevant data sources for this project. Which data sources are most relevant for a renewable energy company looking to identify areas more likely to adopt solar power?

- Point-of-sale data, credit card charge records, and telephone call detail records
- Web log data, e-commerce server application logs, and call-center records
- Medical insurance claims data, survey response data, and warranty claims data
- Census and economic data, hourly weather readings, and demographic data

Census and economic data, hourly weather readings, and demographic data

Which tool is commonly used for data preparation?

- OpenRefine
- Tableau
- R
- SAS Enterprise Miner

OpenRefine

OpenRefine is a free, open-source tool for working with messy data, making it suitable for data preparation tasks.

A popular travel booking platform receives a large volume of web traffic, GPS location data, and user-generated content from various sources. The data analytics team is preparing this data for analysis to better understand customer behavior and preferences. Which tool would be most suitable for preparing this data?

- Microsoft Excel
- Tableau
- Hadoop
- Power BI

Hadoop

Hadoop is an open-source framework designed for the distributed processing of large datasets across clusters of computers. It can handle massive parallel ingestion and custom analysis for web traffic parsing, GPS location analytics, and combining unstructured data feeds from multiple sources. This makes it the most suitable choice for this travel booking platform's data preparation needs.

Which sequence of steps should you follow during the data preparation phase?

- Set up sandbox, extract and transform data, condition data, explore visually
- Generate visuals, modify data, analyze patterns, cooperate with IT department
- Obtain data, store data, create charts, finalize report
- Formulate hypothesis, gather data, examine findings, conclude analysis

Set up sandbox, extract and transform data, condition data, explore visually

These activities occur during the data preparation phase. These activities include setting up a separate testing environment, handling and cleaning the information, gaining insights into the data's characteristics, addressing issues like missing values and inconsistencies, and examining the data visually to better comprehend its structure and distribution.

In the data analytics process, which phase focuses on identifying candidate models for clustering, classifying, or finding relationships and ensuring analytical techniques align with business objectives?

Data transformation
Discovery
Model planning
Data preparation

Model planning

What is the primary purpose of the model planning phase in the data analytics process?

- Identifying methods and aligning techniques with objectives
- Transforming data to bring information to the surface
- Cleaning and conditioning data for analysis
- Assessing resources and framing the business problem

Identifying methods and aligning techniques with objectives

Which activities should be the focus of the model planning phase?

- Transforming data to bring information to the surface
- Visualizing and exploring data patterns
- Cleaning and conditioning data for analysis
- Partitioning the data into training, validation, and test sets

Partitioning the data into training, validation, and test sets

During the data modeling phase, partitioning the dataset into training, validation, and test sets is a crucial activity to build and assess the predictive model's performance.

Which tool is commonly used during the model planning phase?

KNIME
OpenRefine
Hadoop
Data Wrangler

KNIME

KNIME is an open-source data analytics platform for visually creating data workflows.

A healthcare company wants to predict which patients are at risk of developing a certain medical condition. Which model is commonly used for this type of analysis?

Decision tree
Association rules
K-means clustering
Logistic regression

Logistic regression

Logistic regression is a model that predicts the probability of an event occurring.

During a data analytics project, which phase focuses on developing training and test datasets, refining models, and assessing the validity and predictive power of the models?

Model execution
Data preparation
Model planning
Operationalize

Model execution

What is the main purpose of the model execution phase in a data analytics project?

To clean, transform, and aggregate data for analysis
To develop datasets, refine models, and assess validity
To select appropriate models based on project goals
To deploy the model and calculate its financial impact

To develop datasets, refine models, and assess validity

Which activities should the data analytics team perform during the model execution phase of this project?

- Creating data visualizations and capturing essential predictors
- Deploying the model and measuring its return on investment
- Generating training and test sets and refining models to enhance performance
- Grouping categorical variables and standardizing numeric values

Generating training and test sets and refining models to enhance performance

Which tool is suitable for a data analytics team to use during the model execution phase of a project?

SAS Enterprise Miner
Tableau
KNIME
Microsoft Excel

SAS Enterprise Miner

Which phase of a data analytics project involves articulating findings and outcomes for stakeholders while considering caveats, assumptions, and limitations?

Data preparation
Communicate results
Operationalize
Model development

Communicate results

What is the purpose of the communicate results phase in a data analytics project?

Presenting findings and outcomes to stakeholders
Preparing and managing data for analysis
Evaluating the project's financial and technical results
Creating and refining analytical models

Presenting findings and outcomes to stakeholders

Which activity should the data analytics team focus on during the communicate results phase

- Presenting key findings to stakeholders and evaluating the project's success
- Building and testing different predictive models for customer churn
- Analyzing the financial impact of the project on the company's revenue and customer retention
- Performing data cleaning and transforming raw data into usable formats

Presenting key findings to stakeholders and evaluating the project's success

Which tools are commonly used for communicating results in data analytics projects?

- Predictive modeling software and programming languages
- Data visualization tools and presentation software
- Database management systems and data warehouses
- Text editors and spreadsheet software

Data visualization tools and presentation software

What do data analytics teams do in the operationalize phase of a data analytics project?

- Apply data transformations to fix problems with data and surface information
- Communicate project benefits, set up the pilot project, and deploy in production
- Explore data, create model sets, and partition them into training, validation, and test sets
- Translate business problems into data mining problems and locate appropriate data

Communicate project benefits, set up the pilot project, and deploy in production

What is the primary purpose of the operationalize phase in a data analytics project?

- To pilot the model, refine it, and fully deploy it
- To develop and train various data models
- To prepare and clean the data for analysis
- To explore data and partition it into training, validation, and test sets

To pilot the model, refine it, and fully deploy it

What should business users and project sponsors do with their findings during the operationalize phase of a data analytics project?

- Develop and refine data models
- Assess benefits, implications, and business impact
- Produce detailed reports and visuals
- Evaluate project completion and goals

Assess benefits, implications, and business impact

What should analysts do with the findings discovered during the operationalize phase of a data analytics project?

- Assess project risks and return on investment (ROI)
- Create technical specifications
- Evaluate the project's success
- Modify reports and dashboards

Modify reports and dashboards

A company recently completed a data analytics project to identify the most energy-efficient products to add to the catalog. The project team comprised business users, project sponsors, analysts, data scientists, data engineers, and database administrators. Now, the team needs to share their findings with various stakeholders. What should the data scientists, data engineer, and database administrator do to share their findings?

- Share code and provide implementation details
- Manage project timelines and budgets
- Assess the benefits and implications of findings
- Create high-level presentations

Share code and provide implementation details

Which type of data is needed to assess whether a new type of web content is increasing user engagement?

Web log
Demographic
Competitor analysis
Advertising cost

Web log

A data analyst is tasked with creating a comprehensive report about a media company's user base for advertisers.
Which data is most useful to include?

Demographic
Advertiser cost
Web log
Competitor analysis

Demographic

A media firm is in talks with a larger conglomerate about a possible merger.
Which data is relevant for a data analyst to include in a report for its manager?

Advertiser cost
Competitor analysis
Demographic
Web log

Competitor Analysis

A grocery store chain collected data on customer purchases, sales transactions, and inventory levels.
Which question can a data analytics project answer using descriptive analytics?

- What is the optimal inventory level for each product?
- Are there segments of customers whose purchase habits differ during the week compared to the weekend?
- What are the most popular products at each store's location?
- Can future customer purchases be predicted based on past data?

What are the most popular products at each store's location?

A manufacturing company collected data on production processes, equipment downtime, and maintenance logs.
Which question can a data analytics project answer using diagnostic analytics?

- How can energy consumption be reduced during production processes without affecting product quality?
- What is the cost per unit of production?
- What was the cause of the production process inefficiency that resulted in a six-hour delay yesterday?
- Can future equipment failure be predicted based on past data?

What was the cause of the production process inefficiency that resulted in a six-hour delay yesterday?

A healthcare company collected data on patient demographics, medical history, treatment outcomes, and hospital readmissions.
Which question can a data analytics project answer using predictive analytics and the data collected by the healthcare company?

- Which treatments are most likely to result in lower readmission in the future?
- What were the causes of readmissions for the majority of patients?
- What are the demographics of those patients who have been readmitted?
- What caused the surge in readmissions last week?

Which treatments are most likely to result in lower readmission in the future?

A retail company collected data on customer demographics, purchase history, and marketing campaigns.
Which question can a data analytics project answer using prescriptive analytics?

- What is the best marketing strategy to target specific customer segments based on their purchase history and demographics?
- Which products are the most profitable during the fourth quarter?
- Can customer demographics be used to target marketing campaigns more effectively?
- What are customers likely to buy in the future?

What is the best marketing strategy to target specific customer segments based on their purchase history and demographics?

A data analyst is planning a new analytics project for a retail company and needs to collect data from different sources to complete the project.

Which question should be asked regarding the sources and quality of the available data for the project?

- Will the data support the hypothesis?
- What is the time frame of the data, and how often is it updated?
- Is the data being obtained from a third party, a public company, or a private company?
- Is the data in a .csv format or a .xls format?

What is the time frame of the data, and how often is it updated?

A data analyst is planning a new analytics project for a toy manufacturing company. Customer survey data is provided.

Which question should be asked regarding the sources or quality of the data?

- Was the survey completed on a device connected to Wi-Fi?
- What font was used in the survey?
- Was the survey sent to a random sample of customers?
- Was the survey collected on a site or filled out via email?

Was the survey sent to a random sample of customers?

A pharmaceutical company collected data on patient outcomes for a new drug it is testing.

Which question regarding the source or quality of the available data is most appropriate to ask before analysis?

- Did the data come from a completely unbiased source?
- Was the data collected in secret, without the knowledge of the doctors?
- Was the data collected from electronic health records (EHRs) of patients using the drug?
- Can data be excluded to decrease the impact of side effects on the analysis?

Was the data collected from electronic health records (EHRs) of patients using the drug?

Which data source for a retail company analyzing customer behavior is an example of an external source?

- Sales data from the company's website
- Customer demographic data from the loyalty program
- Social media activity of the company's competitors
- Employee surveys

Social media activity of the company's competitors

A data analyst is assigned to analyze sales data for a multinational retail company to identify which products have the highest profit margins.
Which data quality requirement is most critical for this project?

- Consistency
- Completeness
- Accuracy
- Timeliness

Accuracy

A data analyst is tasked with understanding customer satisfaction data and is emailed a file with the data.
Which question should the data analyst ask about the data regarding where it is sourced from?

- Is the data backed up?
- Can the data be improved?
- Has the data been copied into multiple languages?
- When was the data collected?

When was the data collected?

Which question should be asked to determine if a data set is biased?

- Is the data from a self-reported survey?
- Is the market research data too comprehensive?
- Is there too much data?
- Is the financial data objective?

Is the data from a self-reported survey?

Which technique is the most appropriate for analyzing customer demographics?

- Decision trees
- Neural network
- Clustering
- Linear regression

Clustering

Clustering is best used for customer demographics because it can group individuals or entities based on their characteristics or behavior. This can be useful in identifying patterns or segments within a population, which can then inform targeted marketing or outreach efforts.

What is the most appropriate analytics technique for predicting sales for the next quarter?

- Bar chart
- Tree map
- Heat map
- Regression analysis

Regression analysis

Regression analysis is a statistical technique used to determine the relationship between a dependent variable and one or more independent variables.

What is the most appropriate data analytics technique for analyzing website traffic patterns?

Scatterplot
Regression analysis
Line chart
Heat map

Heat map

What is the advantage of using a decision tree over a linear regression model in a data analytics project?

Decision trees are faster and require fewer computational resources.
Decision trees can produce more accurate predictions.
Decision trees can handle missing data more effectively.
Decision trees can handle nonlinear relationships between variables.

Decision trees can handle nonlinear relationships between variables.

Decision trees can model complex, nonlinear relationships between variables, while linear regression models are limited to linear relationships.

A retail grocer wants to use association rules in retail marketing to increase sales.
What would be the impact of using an association rule on sales data?

- By analyzing sales data, the data analyst can apply association rules to discover frequent item sets, which are groups of items often purchased together.
- By analyzing sales data, the data analyst can apply association rules to discover stockpiling behavior, which can be used for coupons.
- By analyzing sales data, the data analyst can apply association rules to predict revenues in the future, which can be used in business strategy.
- By analyzing sales data, the data analyst can apply association rules to discover rare purchases, which can be used for future product generation.

By analyzing sales data, the data analyst can apply association rules to discover frequent item sets, which are groups of items often purchased together.

A company wants to predict the likelihood of a customer responding to a marketing campaign. The data set contains both numerical and categorical variables.
Which analytics technique should the company use?

Logistic regression
K-means clustering
Random forest
Principal component analysis (PCA)

Logistic regression

Logistic regression is a suitable technique for binary classification problems, such as predicting the likelihood of a customer responding to a marketing campaign when the dataset contains numerical and categorical variables.

An e-commerce company is interested in improving the conversion rate of its website.
In which scenario should the company's analyst use an A/B test?

- When they want to discover whether the company should move workers offshore to decrease costs
- When they want to see whether the strategy of unique customer pricing should be used
- When they want to evaluate the market to see whether an acquisition of a smaller company will increase market share
- When they want to find out whether changing the color of the "Add to Cart" button will have a significant impact on sales

When they want to find out whether changing the color of the "Add to Cart" button will have a significant impact on sales

A team working for a social media company needs to analyze customer feedback on a newly launched product using sentiment analysis.
What is the most appropriate approach for sentiment analysis in this scenario?

Regression analysis
Text mining
Time series analysis
Clustering analysis

Text mining

Text mining is a process of analyzing text data to extract useful information. It is the most appropriate approach for sentiment analysis, as it deals with text data and can identify and extract the sentiment behind the words.

A data analyst for a retail company has collected data on customer demographics, purchase history, and marketing campaigns.
Which data analytic technique should be used to predict demand for the upcoming holiday season?

- Use an experiment to see whether consumers prefer music in the store while they shop.
- Use text mining to extract which product descriptions have the most positive sentiments.
- Use a machine learning algorithm to predict future demand and determine the reorder quantity for each product.
- Use clustering to divide customers into high-spending and low-spending groups.

Use a machine learning algorithm to predict future demand and determine the reorder quantity for each product.

A marketing company has a client who wants to know their social media engagement for the past month. They have accounts on several social media platforms and want to compare their engagement across these platforms.
Which visualization metric should be used to find the social media engagement for the client?

Box plot
Pie chart
Bar graph
Heat map

Heat map

A manufacturing company wants to compare the productivity of different teams in its factory over time.
Which visualization technique should be used to present the findings of the comparison?

Box plot
Line chart
Bubble chart
Scatterplot

Line chart

A line chart is the best visualization technique to show data changes over time.

Which technique is the most effective for identifying patterns in large datasets?

Naive bayes
Linear regression
Decision trees
Clustering

Clustering

Clustering is the most effective when dealing with large datasets, as it allows for identifying groups of similar data points without prior knowledge of the data structure.

Which data analytic technique is best suited for identifying outliers in a dataset?

Principal component analysis (PCA)
Box plot
Linear regression
K-means clustering

Box plot

Box plot is the most effective technique for identifying outliers in a dataset. It provides a visual representation of the distribution of data and identifies any data points located outside the range of typical values.

What is a data requirement for logistic regression?

- The dependent variable has to be numeric.
- The independent variable has to be positive.
- The dependent variable has to be binary.
- The independent variable has to be nominal.

The dependent variable has to be binary.

Which type of data is necessary to perform cluster analysis?

Nominal
Time series
Continuous
Categorical

Continuous

Cluster analysis is a data analytics technique that groups similar objects or data points into clusters based on their similarity. Continuous data is necessary for performing cluster analysis because it allows for the calculation of distance or similarity between data points.

Which type of data is necessary for performing machine learning analysis?

- Nonstandardized data
- Health data collected from one hospital
- Survey response data
- Preprocessed data

Preprocessed data

A data analyst is analyzing the employees' salaries at a company to find a representative value that summarizes the central tendency of the data.
Which metric should be used to summarize the central tendency of the data?

Mode
Range
Standard deviation
Median

Median

An organization is building a theme park where the temperature can vary wildly. All rides should be built to handle the extremes of the temperature spectrum.
Which metric should be used in this scenario?

Median
Range
Mode
Mean

Range

Which metric should be used to measure the percentage of website visitors who leave after viewing only one page?

Conversion rate
Bounce rate
Churn rate
Click-through rate

Bounce rate

Descriptive Analytics

tells you what happened in the past.

Diagnostic Analytics

helps you understand why something happened in the past.

Predictive Analytics

predicts what is most likely to happen in the future.