Introduction to Analytics - D491

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?

Accuracy (Accuracy of the data is most important, as this is a necessary first step in calculating profit margins. Without accurate data, any conclusion drawn would be potentially wrong.)

Which job skill is necessary for a researcher in a data analytics project?

Analyzing and interpreting data to inform questions. (Collecting data is vital for researchers as it allows them to analyze and interpret the data to inform research questions.)

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

Assess benefits, implications, and business impact. (Business users focus on the benefits and implications of findings, while project sponsors focus on the business impact, risks, and return on investment.)

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

Bounce rate (The bounce rate measures the percentage of visitors who leave a website after viewing one page.)

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

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

Business domain knowledge and communication. (Partners in a data analytics project must have strong business domain knowledge and communication skills.)

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. (For instance, they might find that customers who buy bread and milk are also likely to buy eggs, butter, and cheese. These can be grouped in a promotion.)

How does a data analyst interact with stakeholders during a data analytics project?

By presenting data analysis results in an easily understandable format. (During a data analytics project, a data analyst interacts with stakeholders by presenting the data analysis results in an easily understandable format.)

How do stakeholders interact with data analytics projects?

By providing input throughout the project lifecycle. (Stakeholders provide input throughout the project lifecycle and may make key decisions. They may provide input on project requirements, goals, and priorities and make key decisions throughout the project lifecycle.)

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?

Census and economic data, hourly weather readings, and demographic data (Census data, economic data, hourly weather readings, and demographic data can provide valuable insights into geographic areas, financial capacity, and environmental factors that can influence the adoption of solar power.)

Which technique is the most appropriate for analyzing customer demographics?

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.)

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

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.)

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

Communicate project benefits, set up the pilot project, and deploy in production. (In the operationalize phase, the team shares the advantages of the project, conducts a trial run, and puts the developed solution into practical use within the organization.)

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

Communicate results (This is the stage where insights and recommendations are shared, keeping in mind any constraints or limitations of the analysis.)

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 (This would contain data relevant to a merger because external data on competitors would be most relevant for a possible merger.)

Which task is the data analyst responsible for within a data analysis project?

Conducting statistical analyses and generating reports. (Data analysts are responsible for analyzing and interpreting large datasets to identify trends, patterns, and insights. They use statistical methods to draw conclusions from the data and generate reports to communicate their findings to stakeholders.)

What is a primary responsibility of a data analyst?

Conducting statistical analysis to identify patterns and trends. (Data analysts are responsible for analyzing large and complex datasets to extract insights and information that can inform decision-making.)

What does a data analyst do in a data analytics project?

Conducts exploratory data analysis to identify trends and patterns. (Data analysts are responsible for analyzing data to identify trends and patterns that can inform business decisions. This typically involves conducting exploratory data analysis, which involves visually exploring and summarizing data to identify patterns and relationships.)

Which type of data is necessary to perform cluster analysis?

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 information tool is a possible source of data in a data analytics project?

Corporate information system (A corporate information system, or data warehouse, is a central repository that stores and manages an organization's data, making it a valuable source of information for a data analytics project.)

Which project is considered a data analytics project?

Creating a dashboard to visualize sales data and monitor inventory levels for a grocery store chain. (A data analytics project typically involves analyzing data to identify trends and patterns and then using this information to make data-driven decisions.)

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

Data analytics is the process of analyzing data to extract insights, while data science involves building and testing models to make predictions. (Data analytics involves using statistical and quantitative methods to analyze data to extract insights and solve problems, while data science involves using machine learning and statistical models to build predictive models and make decisions based on data.)

Which phase of the data analytics lifecycle involves cleaning data, normalizing datasets, and performing transformations?

Data preparation. (This stage focuses on addressing data quality issues, standardizing the data, and carrying out any necessary adjustments. These activities are essential to ensure the data is suitable and accurate for further analysis and model development.)

How is data science different from data analytics?

Data science focuses on developing new algorithms and models, while data analytics focuses on using existing models to analyze data. (Data science is more research-based, while data analytics is more focused on the practical applications of data analytics.)

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

Data visualization tools and presentation software (Data visualization tools help convey insights clearly, and presentation software assists in sharing information with stakeholders.)

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

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 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 (Demographic data is helpful for advertisers since it describes the age, socioeconomic status, and more of the user base.)

Which type of data analytics project aims to determine why something happened in the past?

Descriptive (Descriptive analytics focuses on summarizing past events and understanding what happened.)

What are the different types of data analytics projects?

Descriptive, diagnostic, predictive, and prescriptive analytics

What is a primary responsibility of a data engineer?

Designing and implementing data storage solutions. (Data engineers are responsible for designing and implementing data storage solutions that enable efficient and effective processing, storage, and retrieval.)

What is the role of a business intelligence analyst?

Designing and maintaining data visualizations and dashboards. (Business intelligence analysts are responsible for designing and maintaining data visualizations and dashboards to communicate business insights to stakeholders.)

What is a primary responsibility of a machine learning engineer?

Developing predictive models using machine learning algorithms. (Machine learning engineers are responsible for developing predictive models using machine learning algorithms that can be used to make predictions or inform business decisions.)

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?

Discovery (This is the stage where the team delves into the problem, gains insights, learns about the data that can be used, and comes up with initial ideas to be tested with the data.)

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 data analytics projects are typically used when little is known about the data or when researchers look for patterns or trends that may not have been previously identified.)

ETLT

Extract, Transform, Load, Transform

Is responsible for managing the budget and finances of the project. In this scenario, they would ensure that the project stays within budget and provide regular financial updates to the project sponsor and other stakeholders.

Financial Operations

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

Generating training and test sets and refining models to enhance performance (In this stage, the team focuses on using the prepared data to create subsets for training and testing the model, improving the model's accuracy, and optimizing its performance to ensure it meets the project goals.)

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?

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.)

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

Heat map (A heat map is a graphical representation of data that uses color coding to visualize the magnitude or frequency of a variable across two dimensions. Heat maps display large amounts of data in a way that is easy to interpret and identify patterns.)

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?

Heat map (A heat map would be best to visualize the interactions between posts and customer engagement due to its ability to communicate complex information through color gradients.)

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

Identifying methods and aligning techniques with objectives. (The model planning phase aims to determine the most suitable method for the given problem and ensure that the chosen analytical techniques align with the business objectives.)

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?

Increasing customer satisfaction and sales through targeted recommendations and improved customer support. (By using data analytics to provide personalized recommendations and enhance customer support, the company can create a better shopping experience for its customers, ultimately leading to increased satisfaction and sales.)

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? -Insufficient time for comprehensive data analysis

Insufficient time for comprehensive data analysis (Insufficient time for comprehensive data analysis could lead to incomplete or superficial insights, affecting the project's overall effectiveness.)

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

Is the data from a self-reported survey? (Survey responses can be very subjective based on how the questions are asked and even who is asking the question.)

Why is quality control/assurance crucial for data engineers in a data analytics project?

It ensures that the data is accurate and reliable. (Quality control is crucial for data engineers in a data analytics project because it ensures that the data used for analysis is accurate and reliable.)

Which tool is commonly used during the model planning phase?

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

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?

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

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?

Lists of at-risk customers (Providing a list of at-risk customers for targeted marketing campaigns can help retain their business and prevent revenue loss.)

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?

Logistic regression (Logistic regression is a model that predicts the probability of an event occurring. It is suitable for predicting which patients are at risk of developing a certain medical condition.)

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 (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.)

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? -Standard deviation

Median (The median is the middle value in a dataset when the data is arranged in order. It is an appropriate metric for summarizing the central tendency of the data in this scenario, as it provides information on the typical salary of employees. The median is less sensitive to outliers than the mean and provides a better representation of the central tendency of the data when there are extreme values.)

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 (In this phase, the data analyst divides the available data into subsets for training and testing purposes and fine-tuning the chosen models. Additionally, the analyst evaluates how well these models can predict outcomes and checks their reliability.)

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?

Model planning (This is the phase where the most suitable models are chosen based on the business goals and the types of relationships that need to be discovered in the data.)

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

Modify reports and dashboards (Analysts focus on understanding how the findings impact the reports and dashboards they manage, and they modify them accordingly.)

Which tool is commonly used for data preparation?

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

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.

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

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 type of data is necessary for performing machine learning analysis?

Preprocessed data (Machine learning analysis is a data analytics technique used to develop predictive models by training algorithms to identify patterns and relationships in data. Preprocessed data is necessary for performing machine learning analysis because the data must be cleaned, transformed, and standardized to ensure the accuracy and reliability of the models.)

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

Presenting findings and outcomes to stakeholders (The purpose of the communicate results phase is to convey project outcomes, findings, and other relevant information 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 (The main goal of the communicate results phase is to convey the project outcomes and insights to stakeholders while evaluating the project's success and discussing possible improvements.)

Oversees the project's day-to-day operations, including coordinating with stakeholders and ensuring that the project stays on track. In this scenario, the project manager would manage the data analytics project and ensure the team meets its goals and deadlines.

Project Manager

Is the executive who has authorized the project and is responsible for ensuring the project aligns with the company's strategic goals. In this scenario, the project sponsor would be a high-level executive within the retail company interested in improving inventory management and reducing waste.

Project Sponsor

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

Project team members and senior management. (Key stakeholders in a project are those who have a direct interest in its success or failure.)

What role do stakeholders play in the project cycle?

Provide guidance and feedback throughout the project. (Stakeholders play a critical role in providing guidance and feedback throughout the project.)

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?

Range (The range gives data about the spread of all possible data points.)

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

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

Which stakeholder should conduct literature reviews for a data analytics project?

Researcher (Researchers are responsible for thoroughly reviewing existing literature to identify relevant research and data that can inform the project's objectives and research questions.)

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

SAS Enterprise Miner (SAS Enterprise Miner is a commercial tool specifically designed for model building and execution, making it suitable for the model execution phase of the project.)

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

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.)

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 (Data scientists, data engineers, and database administrators share their code and create technical documents on how to implement it.)

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

Social media activity of the company's competitors (Social media activity of a competitor would have to come from an external data source.)

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?

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 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 customer database (Access to the customer database is crucial to analyze customer data, including transactions, demographics, and feedback, which will help the team create actionable insights to improve sales and satisfaction.)

What is a data requirement for logistic regression?

The dependent variable has to be binary. (Logistic regression requires a binary dependent variable to make probabilistic assessments throughout any scenario.)

What is data science?

The practice of using statistical methods to extract insights from data. (Data science is a multidisciplinary field involving various statistical, mathematical, and computational methods to extract meaningful insights and knowledge from data.)

What is Data analytics?

The process of analyzing data to extract insights. (Data analytics involves analyzing data to extract insights and inform decision-making. This includes using various techniques and tools to explore, clean, transform, and model data and visualize and communicate findings.)

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

They ensure that the project aligns with business goals and objectives. (A project sponsor is a person or group that provides direction and support to a project. In a data analytics project, the project sponsor is critical in ensuring the project aligns with the business goals and objectives.)

Why are financial operation stakeholders important in a data analytics project?

They interpret data and provide insights to improve financial performance. (Financial operation stakeholders have a deep understanding of financial performance and provide insights on how to interpret and improve financial data, trends, and patterns.)

What is the role and function of a decision scientist within an organization?

To analyze data and provide insights to support informed decision-making. (Decision scientists use data analysis and statistical methods to identify patterns, trends, and relationships in data.)

What component of a data analytics project is typically completed by a data analyst?

To clean and preprocess data to prepare it for analysis (This involves collecting data from various sources, cleaning it, and transforming it into a format that can be used for analysis.)

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

To clean, normalize, and transform data (The primary purpose of the data preparation phase is to ensure that the data is accurate, standardized, and adjusted as needed, which includes tasks like cleaning, normalizing, and transforming data.)

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

To conduct statistical analysis and machine learning modeling. (Data scientists analyze complex datasets using statistical analysis and machine learning techniques. This typically involves cleaning and preprocessing data, conducting exploratory data analysis, building and testing models, and communicating insights to business stakeholders.)

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

To develop datasets, refine models, and assess validity (In this stage, analysts focus on creating separate data subsets for training and testing, fine-tuning the selected models to improve their performance, and evaluating how well these models predict outcomes based on their validity and predictive strength.)

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

To pilot the model, refine it, and fully deploy it. (The operationalize phase tests the model in a controlled environment, making necessary adjustments and integrating it into the organization's processes.)

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 predict customer churn risk (The project aims to predict which customers are likely to leave the company so that targeted promotions can be offered to retain their business.)

What is the primary purpose of the discovery phase in the data science process?

To understand the business problem and develop initial hypotheses. (This phase focuses on investigating the issue, gaining a deeper understanding of the context, learning about available data sources, and formulating initial ideas that will be tested using data.)

Which data migration skill is necessary for database administrators?

Transferring data between different systems or formats. (Database administrators need to have a deep understanding of the data and its structure and the systems and formats involved in the migration process to ensure a smooth transfer of data.)

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 a machine learning algorithm to predict future demand and determine the reorder quantity for each product. (This approach considers historical sales data and other relevant external factors such as seasonality, trends, and economic indicators to predict future demand accurately. The predicted demand can then determine the optimal reorder quantity for each product, thereby optimizing inventory management.)

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?

Was the data collected from electronic health records (EHRs) of patients using the drug? (Whether the data came from the EHRs of patients who have used the drug is an appropriate question to ask, as it can provide insights into real-world drug effectiveness and safety.)

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 sent to a random sample of customers?

Was the survey sent to a random sample of customers? (Sending surveys to a random sample of customers is the best method for collecting data, as it allows for a large and diverse sample size that can provide valuable insights into customer behavior.)

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

Web log (Web log data contains the time spent on each web page.)

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 (Web log data, call-center records, and survey responses provide valuable insights into patient behavior and satisfaction, which are important factors for analyzing patient satisfaction.)

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 are the most popular products at each store's location? (Descriptive analytics can use customer purchase data to quickly identify and summarize the most popular products at each store's location. This information can inform inventory management, product placement, and marketing strategies to increase revenue.)

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? (Prescriptive analytics can use data on customer demographics, purchase history, and marketing campaigns to recommend the best action to achieve a specific outcome, such as increasing sales to a specific customer segment. This information can be used to implement targeted marketing campaigns that are more likely to be successful and increase revenue for the company.)

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?

What is the time frame of the data, and how often is it updated? (The time frame of the data and its updating frequency can impact whether the data is suitable for analysis for a particular project.)

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

What was the cause of the production process inefficiency that resulted in a six-hour delay yesterday? (Diagnostic analytics can use data on production processes, equipment downtime, and maintenance logs to identify the root causes of problems, such as machine breakdowns, operator errors, or maintenance issues. This information can be used to implement corrective actions to improve efficiency. It goes beyond describing what has happened (descriptive analytics) or predicting what might happen (predictive analytics) and focuses on answering the "why" questions.)

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 find out whether changing the color of the "Add to Cart" button will have a significant impact on sales (Randomly assigning visitors to either the control or variant version of the home page ensures that the two groups are statistically similar and that any differences in conversion rates can be attributed to the change in the "Add to Cart" button color.)

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?

When was the data collected? (If a dataset was collected five years ago, that was a very long time ago and could not relate to human behavior today.)

Which question of interest is appropriate for a data analytics project to increase a store's sales?

Which customer segments will most likely respond to a marketing campaign? (This question focuses on identifying customer segments most likely to respond positively to a marketing campaign, directly addressing the goal of increasing sales. By targeting the right customer segments, the store can optimize its marketing efforts and increase its overall sales.)

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? (Predictive analytics can use data on patient demographics, medical history, treatment outcomes, and hospital readmissions to predict which treatment will result in a decrease in being readmitted to the hospital. This information can be used to implement interventions or adjustments to treatment plans to reduce readmissions and improve patient outcomes.)