Download FOUNDATIONS OF ANALYTICS (D491) — GRADE A+ | 2026 and more Exams Computer Science in PDF only on Docsity!
FOUNDATIONS OF ANALYTICS (D491) —
GRADE A+ | 2026
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 - Answer- 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.) Why is quality control/assurance crucial for data engineers in a data analytics project? -It ensures that the data is accurate and reliable. -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 accessible to all stakeholders.
- Answer- 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.) What does a data analyst do in a data analytics project? -Focuses on building machine learning models -Conducts exploratory data analysis to identify trends and patterns -Designs and develops databases and data pipelines -Oversees data governance and data quality assurance - Answer- 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.) What is the function of a data scientist in an organization? -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
-Analyzing and interpreting data to inform business decisions -Developing predictive models using machine learning algorithms - Answer- 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 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 implementing data storage solutions -Designing and developing data visualizations for stakeholders - Answer- 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.) What is the role and function of a decision scientist within an organization? -To manage the company's finances and ensure profitability
-To develop marketing strategies and increase sales revenue -To analyze data and provide insights to support informed decision-making -To oversee the company's human resources and ensure employee satisfaction - Answer- 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 is a primary responsibility of a data analyst? -Developing data visualizations for stakeholders -Conducting statistical analysis to identify patterns and trends -Developing predictive models using machine learning algorithms -Designing and implementing data storage solutions - Answer- 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 component of a data analytics project is typically completed by a data analyst? -To clean and preprocess data to prepare it for analysis -To design and implement machine learning algorithms
-Ensuring that the database remains secure - Answer- 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.) Which job skill is necessary for a researcher in a data analytics project? -Analyzing and interpreting data to inform questions -Ensuring data privacy and security -Designing and implementing data storage solutions -Identifying business needs and requirements - Answer- 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 are the necessary skills for partners in a data analytics project? -Data visualization and dashboard development -Machine learning algorithm development -Business domain knowledge and communication -Cloud infrastructure management and automation - Answer- Business domain knowledge and communication. (Partners in a data analytics project
must have strong business domain knowledge and communication skills.) Which groups make up the key stakeholders in a data analytics project? -Competitors and regulatory agencies -Shareholders and investors -Manufacturers and suppliers -Project team members and senior management - Answer- 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? -Create the project plan and schedule -Execute the project tasks -Provide guidance and feedback throughout the project -Define the project scope and objectives - Answer- Provide guidance and feedback throughout the project. (Stakeholders play a critical role in providing guidance and feedback throughout the project.) Which stakeholder should conduct literature reviews for a data analytics project? -Researcher
-By providing technical details of data analysis methods - Answer- 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.) What role does a project manager play within a data analytics project? -Provide funding and resources for the project
- Collect and analyze data -Interpret the project results and make recommendations for future projects -Oversee the project team and ensure the project is completed on time and within budget - Answer- 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 finances to complete data visualizations -By providing consultations at the start of the project -By providing input throughout the project lifecycle - Answer- 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.) Why are financial operation stakeholders important in a data analytics project? -They help design and implement data analytics projects. -They are responsible for data cleaning and migration within a project. -They provide financial resources for the project. -They interpret data and provide insights to improve financial performance. - Answer- 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.) 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? -Data preparation -Model planning -Model execution -Discovery - Answer- Discovery (This is the stage where the team delves into the problem, gains insights, learns
statistical, mathematical, and computational methods to extract meaningful insights and knowledge from data.) How is data science different from data analytics? -Data science focuses more on data visualization, while data analytics focuses on data cleaning and preprocessing. -Data science focuses more on tracking experimental data, and data analytics is based on statistical methods and hypotheses. -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. - Answer- 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 comparison describes the difference between data analytics and data science? -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. -Data analytics focuses on descriptive analysis, while data science focuses on prescriptive analysis. - Answer- 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 type of data analytics project aims to determine why something happened in the past? -Prescriptive -Descriptive -Predictive -Diagnostic - Answer- Descriptive (Descriptive analytics focuses on summarizing past events and understanding what happened.) What are the different types of data analytics projects? -Regression analysis, time series analysis, text analytics, and network analysis
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 - Answer- 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.) ETLT - Answer- Extract, Transform, Load, Transform 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? -Should the store expand to a new location? -How can the store's social media presence be improved? -What are the store's best-selling products? - Answer- 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 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 identify customer preferences -To predict customer churn risk -To optimize inventory management -To compare the company's churn rate to industry benchmarks - Answer- 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.) 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.
-The company's inventory records - Answer- 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.) 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 -Limited budget for purchasing additional software tools -Limited access to demographic data on customers -Lack of collaboration between departments - Answer- 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.)
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? -Identifying the number of unique customers who visited the website in the past month -Comparing the company's pricing strategy with competitors' -Understanding the most popular products sold by the company -Increasing customer satisfaction and sales through targeted recommendations and improved customer support - Answer- 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.) Which phase of the data analytics lifecycle involves cleaning data, normalizing datasets, and performing transformations? -Data exploration -Data modeling -Data evaluation
