Introduction to Analytics - D491
Which phase is the immediate predecessor to the operationalize phase of the data analytics life cycle?
Communicate Results
Which type of data is necessary to perform cluster analysis? -Categorical -Nominal -Continuous -Time series
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? -Marketing slogans -Company logo designs -Corporate information system -Consumer perception survey questions
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.)
What is a skill required of a data engineer?
Maintaining Databases
Which classification model is based on the concept of probability and assigns class labels to instances based on the possibility of belonging to a particular class?
Naive Bayes
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
Oversee the project team and ensure the project is completed on time and within budget.
Which measure assesses the validity of a correlation between two variables during the communicate results phase?
P-Value
External stakeholders collaborating with the company on the project. In this scenario, the partners might be software vendors or consultants providing specialized expertise or tools to help with the data analysis.
Partners
Which programming language is primarily used for statistical analysis and data manipulation in the model planning phase?
R
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? -Mode -Median -Mean -Range
Range (The range gives data about the spread of all possible data points.)
A car manufacturing company is looking to improve its production by analyzing how factors such as temperature, humidity, and machine performance affect production efficiency. To identify the key factor that affects efficiency the most, an analyst uses the regression analysis technique.What justifies the use of regression analysis for the given task?
Regression analysis is used to analyze the relationship between one or more independent variables and a dependent variable.
Are responsible for interpreting the results of specific data. In this scenario, they would be responsible for using statistical and computational techniques to identify patterns and trends in the data and make recommendations for how the company can optimize its inventory management system.
Researchers
Which step is typically performed after executing the model in the model execution phase?
Result analysis
Which activity is performed during the model planning phase of a data analysis project? -Building the final predictive model -Selecting relevant features for modeling -Generating synthetic data for model training -Conducting hypothesis testing on the modeling data
Selecting relevant features for modeling
A retail company wants to improve customer satisfaction by understanding the factors that influence customer loyalty. The company has collected customer feedback data from different sources, such as online surveys, social media platforms, and customer support calls.Which data analytic technique should be used to identify the factors influencing customer loyalty based on the collected customer feedback data?
Text analysis
A pharmaceutical company wants to understand if a new drug reduces fevers. The data suggest a fever reduction when using the drug. The company's data analysts study the data to determine whether the effect observed could have occurred by chance.Which data analytic calculation should the analysts perform?
The P-Value
Who should be included as stakeholders in an analytics project?
Anyone who will benefit from the project.
A data analyst is working on a project to identify the main reasons for customer complaints and is planning to review the data.Which question should the analyst consider in this scenario? -Are all the relevant variables present? -Do all variables have a known distribution? -What is the correlation between variables? -What are the independent variables?
Are all the relevant variables present
A data analyst working for a retail company analyzes the purchasing behavior of customers to identify patterns and recommend products.Which data analytic technique is most appropriate for analyzing the transaction data?
Association Rules
Which group of stakeholders comprises the professionals, such as line managers?
Business Users
A data analyst working for a retail company has a team that analyzes customer purchasing behavior and identifies a segment of high-value customers who have a high propensity to churn. Now the analyst needs to communicate the results to the customer service department to operationalize the insights and reduce customer churn.How does the communication of results tie to the operationalize phase of data analytics? -By conducting further data analysis and exploration -By implementing personalized outreach to customers -By refining data collection processes -By training customer service representatives
By implementing personalized outreach to customers
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
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.)
Which task is commonly performed to identify and address data quality issues during the data preparation phase? -Performing data deduplication -Developing data visualization -Conducting data profiling -Extracting data integration
Conducting data profiling
Which testing procedure is used for evaluating the performance of a model in the data analytics life cycle?
Cross-Validation
A financial institution is seeking to reduce the risk of fraudulent transactions. The institution has customer data that includes account information, transaction history, demographics, and device usage. The data analytics project aims to answer the question: "What patterns of behavior suggest a higher risk of fraudulent transactions?"Which data is required to meet the needs of the project and address the risk of fraudulent transactions? -Transaction time data -Customer age data -Customer device usage data -Account opening date data
Customer device usage data
Which data visualization tool in the communicate results phase is used to create web-based visualization?
D3.js
Which job position is primarily responsible for designing and constructing data pipelines within the field of data analytics?
Data Engineer
Which role in a data analytics project helps data scientists shape data for analysis?
Data Engineer
Which stakeholder extracts and transforms data during the discovery phase?
Data Engineer
Which project-related activity typically takes up the majority of a data analyst's time?
Data Preparation
Which skill must a business intelligence analyst possess to collect and organize data?
Data Preparation
Which role in a data analytics project provides expertise for analytical techniques?
Data Scientist
Who offers suggestions on ideas to test as the team formulates hypotheses during the discovery phase of a data analytics project?
Data Scientists
Which skills are required by data scientists for converting unstructured data to structured data in data analytics projects?
Data Wrangling
A person has been assigned to manage a project to implement a company-wide customer relationship management (CRM) system. The CRM system aims to centralize customer details, automate sales processes, and improve customer service.What skills are crucial for the project team members working on the CRM system implementation? -Data analysis, system integration, and training -Graphic design, social media marketing, and content creation -Financial forecasting, budgeting, and cost analysis -Network troubleshooting, hardware maintenance, and software installation
Data analysis, system integration, and training
Is responsible for managing the data infrastructure and ensuring that the data is stored securely and efficiently. In this scenario, they would be responsible for setting up the data analytics tools and ensuring the data is stored and well-organized.
Database Administrator
Which stakeholder has access to essential tables or storage systems and guarantees the highest levels of security in the data repository?
Database Administrator
The ultimate beneficiaries of the project, as they will benefit from a more efficient and optimized inventory management system. In this scenario, the end user might be retail store managers or buyers responsible for managing inventory levels and making purchasing decisions.
End Users
A data analyst at a retail company is provided with a large dataset containing sales transactions, customer information, and product details. The analyst is tasked with preparing the data for analysis and modeling.Which activity would the analyst perform during the data preparation phase? -Exploring available data to understand its characteristics and suitability -Identifying the business problem or research question that needs to be addressed -Developing initial hypotheses about the relationship between data variables -Allocating computing resources for the data analysis
Exploring available data to understand its characteristics and suitability
ETLT
Extract, Transform, Load, Transform
Which statement is an example of a common pitfall in the communication of model results? -overemphasizing simplicity to explain the model -Providing detailed explanations of model assumptions -Focusing only on the accuracy of the model -Presenting multiple visualizations to illustrate the model
Focusing only on the accuracy of the model
What is the primary objective of the operationalize phase in the data analytics life cycle?
Implementing and maintaining the analytics solution in a production environment
Which common data cleaning task is used to address the missing data in a data set? -Normalization -Handling outliers -Data transformation -Imputation
Imputation
A food delivery company would like to study economic conditions' effects on sales. The analyst in charge of the project is planning on gathering economic data from a well-known blog.Which question should the analyst consider in the scenario?
Is the data correct
Why is formulating an initial hypothesis an integral part of the discovery phase of the data analytics lifecycle?
It guides the subsequent data collection, processing, and analysis activities.
Why is it significant to establish failure criteria for a data analytics project in the discovery phase?
It helps the team determine when it is best to accept the conclusions.
How does the communication of results tie to the operationalize phase of data analytics?
It implements data-driven insights into business functions.
What is the role of the SPSS modeler in the model execution phase of the data analytics life cycle?
It is used for applying the trained model to new data predictions.
Which data visualization is most suitable for understanding the trend and progression of a variable over time in the data preparation phase? -Histograms -scatter plots -Box plots -Line Charts
Line Charts
Which regression model is commonly used for predicting a continuous numerical outcome based on a set of input features?
Linear Regression
A data analyst working for a digital marketing agency wants to analyze customer data to identify factors that are most strongly associated with customer churn. The analyst has access to a database of customer information, which includes data such as age, gender, location, income, purchasing behavior, engagement with the agency's services, and customer satisfaction ratings.Which data analytic technique should be used to identify factors that are strongly associated with customer churn for the agency?
Logistic regression analysis
Which software do business intelligence analysts use to perform their responsibilities?
Microsoft Excel
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 (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.)
Which phase of the data analytics life cycle involves running analytical software packages on small datasets to test and refine models?
Model execution phase
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
Which stakeholder is primarily responsible for ensuring the desired quality of the project?
Project Manager
Which role is responsible for project initiation and providing the requirements for a project?
Project Sponsor
Which tool is used to connect users to relational databases and data warehouse appliances in the model planning phase?
SAS/ACCESS
A business seeks to increase profitability and optimize inventory management. They decide to carry out data analytics research to determine which merchandise is selling quickly and which is selling slowly. The research seeks to determine whether there is a relationship between the kind of merchandise and sales velocity.Which data is required to answer the question for the data analytics project in the scenario? -Sales data categorized by customer segment -Sales data categorized by region -Sales data categorized by time period -Sales data categorized by product
Sales data categorized by product
What is a data requirement for logistic regression? -The independent variable has to be positive. -The independent variable has to be nominal. -The dependent variable has to be numeric. -The dependent variable has to be binary.
The dependent variable has to be binary. (Logistic regression requires a binary dependent variable to make probabilistic assessments throughout any scenario.)
An energy company wants to predict future energy demand to optimize production. The company has an extensive data set on historical energy usage.Which data analytic technique is best suited for this scenario? -Time series -Principal component analysis -Multiple Regression -Logistic Regression
Time Series
What is the purpose of communicating data analytics results to stakeholders?
To demonstrate the value and impact f data analytics on business outcomes.
What is the primary responsibility of the business intelligence analyst during the operationalize phase of the data analytics life cycle?
To make sure their reports and dashboards are up to date.
Which task is typically performed to handle outliers during the data preparation phase?
Truncating extreme values
Which activity occurs during the data preparation phase of the data analytics lifecycle?
Understanding of data
An e-commerce company has collected various types of data about their customers, products, and sales transactions. The available data includes customer demographics, product attributes, purchase history, website clickstream data, and customer feedback.Which question can be answered using data analytics based on the available data? -What are the most popular products among customers aged 18-25? -How many sales transactions were made in the last month? -What is the total revenue generated by the company since its inception? -How many employees does the company have?
What are the most popular products among customers aged 18-25?
A retail company collects data on its sales for the last quarter. The data includes information on the sale of the products sold, the price, the quantity, and the sale location.Which type of question can the data analytics project answer based on the available data? -How many employees work in the retail company? -What are the top-selling products in each location? -What is the average age of customers who purchased products? -What is the best time to launch a new product based on customer purchase behavior?
What are the top-selling products in each location?
A data analyst plans to explore possible indicators of fraud in bank transactions. The analyst is considering different tools that can be used to collect the data needed.Which question should the analyst consider when identifying the tool to use?
What is the format and structure of the data?
A data analyst is planning the data preparation phase of a data analysis project. During planning, they consider what to do if the data contains a lot of outliers.Which question should the analyst consider in this scenario?
What is the impact of outliers on the analysis?
A company will survey its customers to understand the potential demand for a new product. A data analyst will review the data.Which question should the analysis consider to validate the representativity of the data?
What is the response rate of the survey?
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
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 -To design and implement machine learning algorithms -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 (This involves collecting data from various sources, cleaning it, and transforming it into a format that can be used for analysis.)
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
Provide guidance and feedback throughout the project. (Stakeholders play a critical role in providing guidance and feedback throughout the project.)
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? -Completeness -Consistency -Timeliness -Accuracy
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 -Ensuring data privacy and security -Designing and implementing data storage solutions -Identifying business needs and requirements
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? -Develop and refine data models -Evaluate project completion and goals -Produce detailed reports and visuals -Assess benefits, implications, and business impact
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? -Churn rate -Conversion rate -Click-through rate -Bounce rate
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? -Principal component analysis (PCA) -Box plot -K-means clustering -Linear regression
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? -Data visualization and dashboard development -Machine learning algorithm development -Business domain knowledge and communication -Cloud infrastructure management and automation
Business domain knowledge and communication. (Partners in a data analytics project must have strong business domain knowledge and communication skills.)
What is the role of a business intelligence analyst? -Designing and maintaining data visualizations and dashboards -Conducting statistical analysis and machine learning modeling -Developing and implementing data processing pipelines -Overseeing data governance and compliance
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 -Analyzing and interpreting data to inform business decisions -Designing and implementing data storage solutions -Designing and developing data visualizations for stakeholders
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.)
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 tool is commonly used during the model planning phase? -OpenRefine -KNIME -Hadoop -Data Wrangler
KNIME (KNIME is an open-source data analytics platform for visually creating data workflows.)
Which tool is commonly used for data preparation? -SAS Enterprise Miner -R -Tableau -OpenRefine
OpenRefine (OpenRefine is a free, open-source tool for working with messy data, making it suitable for data preparation tasks.)
Which type of data is necessary for performing machine learning analysis? -Preprocessed data -Nonstandardized data -Health data collected from one hospital -Survey response data
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.)
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
What is the most appropriate analytics technique for predicting sales for the next quarter? -Regression analysis -Heat map -Tree map -Bar chart
Regression analysis (Regression analysis is a statistical technique used to determine the relationship between a dependent variable and one or more independent variables.)
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 (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 type of data is needed to assess whether a new type of web content is increasing user engagement? -Web log -Competitor analysis -Advertising cost -Demographic
Web log (Web log data contains the time spent on each web page.)
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? -Is the data in a .csv format or a .xls format? -Will the data support the hypothesis? -Is the data being obtained from a third party, a public company, or a private company? -What is the time frame of the data, and how often is it updated?
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? -How can energy consumption be reduced during production processes without affecting product quality? -What was the cause of the production process inefficiency that resulted in a six-hour delay yesterday? -What is the cost per unit of production? -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? (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.)
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? -Can the data be improved? -When was the data collected? -Is the data backed up? -Has the data been copied into multiple languages?
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 tools are commonly used for communicating results in data analytics projects? -Data visualization tools and presentation software -Database management systems and data warehouses -Text editors and spreadsheet software -Predictive modeling software and programming languages
Data visualization tools and presentation software (Data visualization tools help convey insights clearly, and presentation software assists in sharing information with stakeholders.)
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 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 stockpiling behavior, which can be used for coupons. -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.
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 making decisions on behalf of stakeholders -By presenting data analysis results in an easily understandable format -By delegating tasks to stakeholders -By providing technical details of data analysis methods
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.)
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? -Medical insurance claims data, survey response data, and warranty claims data -Web log data, e-commerce server application logs, and call-center records -Point-of-sale data, credit card charge records, and telephone call detail records -Census and economic data, hourly weather readings, and demographic data
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 -Neural network -Linear regression -Decision trees
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 -Naive bayes -Decision trees -Linear regression
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? -Translate business problems into data mining problems and locate appropriate data -Communicate project benefits, set up the pilot project, and deploy in production -Apply data transformations to fix problems with data and surface information -Explore data, create model sets, and partition them into training, validation, and test sets
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? -Operationalize -Model development -Data preparation -Communicate results
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 -Demographic -Competitor analysis -Web log
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? -Creating the project's overall goals and objectives -Collecting, cleaning, and loading customer data into a data warehouse -Developing and implementing software applications -Conducting statistical analyses and generating reports
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? -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
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? -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
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 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. (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 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.
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 exploration -Data modeling -Data evaluation -Data preparation
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 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.
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.)
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 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 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? -Web log -Competitor analysis -Demographic -Advertiser cost
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? -Prescriptive -Descriptive -Predictive -Diagnostic
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 -Data warehousing, data mining, data visualization, and business intelligence -Descriptive, diagnostic, predictive, and prescriptive analytics -Data collection, data cleaning, data transformation, and data visualization
Descriptive, diagnostic, predictive, and prescriptive analytics
What is a primary responsibility of a data engineer? -Designing and developing data visualizations for stakeholders -Designing and implementing data storage solutions -Analyzing and interpreting data to inform business decisions -Developing predictive models using machine learning algorithms
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.)
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
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 projects involve analyzing data from a single source, while confirmatory projects involve integrating data from multiple sources. -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 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.)
Which activities should the data analytics team perform during the model execution phase of this project? -Grouping categorical variables and standardizing numeric values -Generating training and test sets and refining models to enhance performance -Deploying the model and measuring its return on investment -Creating data visualizations and capturing essential predictors
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? -Power BI -Tableau -Microsoft Excel -Hadoop
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? -Line chart -Heat map -Regression analysis -Scatterplot
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? -Pie chart -Bar graph -Box plot -Heat map
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 -Assessing resources and framing the business problem -Transforming data to bring information to the surface -Cleaning and conditioning data for analysis
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? -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
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 -Limited budget for purchasing additional software tools -Limited access to demographic data on customers -Lack of collaboration between departments
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 there too much data? -Is the financial data objective? -Is the market research data too comprehensive? -Is the data from a self-reported survey?
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. -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.
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.)
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 -Bubble chart -Scatterplot -Line chart
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? -Updated website design -Supply chain improvements -Lists of at-risk customers -Monthly sales reports
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? -K-means clustering -Logistic regression -Decision tree -Association rules
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? -Random forest -Principal component analysis (PCA) -K-means clustering -Logistic regression
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 -Mode -Range -Median
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.)
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 preparation -Discovery -Model planning -Data transformation
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? -Assess project risks and return on investment (ROI) -Modify reports and dashboards -Evaluate the project's success -Create technical specifications
Modify reports and dashboards (Analysts focus on understanding how the findings impact the reports and dashboards they manage, and they modify them accordingly.)
Which activities should be the focus of the model planning phase? -Partitioning the data into training, validation, and test sets -Transforming data to bring information to the surface -Cleaning and conditioning data for analysis -Visualizing and exploring data patterns
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.)
What is the purpose of the communicate results phase in a data analytics project? -Operationalize -Model development -Data preparation -Communicate results
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 findings and outcomes to stakeholders -Creating and refining analytical models -Evaluating the project's financial and technical results -Preparing and managing data for analysis
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.)
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
Project team members and senior management. (Key stakeholders in a project are those who have a direct interest in its success or failure.)
Which stakeholder should conduct literature reviews for a data analytics project? -Researcher -End use -Database administrator -Project sponsor
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 sequence of steps should you follow during the data preparation phase? -Obtain data, store data, create charts, finalize report -Generate visuals, modify data, analyze patterns, cooperate with IT department -Formulate hypothesis, gather data, examine findings, conclude analysis -Set up sandbox, extract and transform data, condition data, explore visually
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 -Assess the benefits and implications of findings -Create high-level presentations -Manage project timelines and budgets
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? -Employee surveys -Customer demographic data from the loyalty program -Social media activity of the company's competitors -Sales data from the company's website
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? -Time series analysis -Clustering analysis -Text mining -Regression 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 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 financial statements -The employee records -The customer database -The company's inventory records
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 data science? -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
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 encrypting data to keep it secure -The process of storing data in a secure location for future use -The process of analyzing data to extract insights -The process of collecting data from various sources
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 are the primary users of the project's outputs. -They provide funding for the project. -They are responsible for implementing the project. -They ensure that the project aligns with business goals and objectives.
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 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.
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 primary purpose of the data preparation phase in a data analytics project? -To build and refine predictive models -To evaluate the performance of models -To visualize and explore data patterns -To clean, normalize, and transform data
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 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 design and maintain data visualizations and dashboards
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 clean, transform, and aggregate data for analysis -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
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 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
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 identify customer preferences -To predict customer churn risk -To optimize inventory management -To compare the company's churn rate to industry benchmarks
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 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. (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? -Developing and implementing database software -Transferring data between different systems or formats -Troubleshooting network issues within the system -Ensuring that the database remains secure
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. -Use an experiment to see whether consumers prefer music in the store while they shop. -Use clustering to divide customers into high-spending and low-spending groups. -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. (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? -Can data be excluded to decrease the impact of side effects on the analysis? -Did the data come from a completely unbiased source? -Was the data collected from electronic health records (EHRs) of patients using the drug? -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? (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? -What font was used in the survey? -Was the survey completed on a device connected to Wi-Fi? -Was the survey collected on a site or filled out via email?
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 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 (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? -Are there segments of customers whose purchase habits differ during the week compared to the weekend? -Can future customer purchases be predicted based on past data? -What is the optimal inventory level for each product? -What are the most popular products at each store's location?
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? -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? (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.)
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 see whether the strategy of unique customer pricing should be used -When they want to discover whether the company should move workers offshore to decrease costs -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 (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.)
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?
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? -What caused the surge in readmissions last week? -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?
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.)
