WorksheetsThe Data Analytics Journey - D204
Total questions: 75
Worksheet time: 4hrs 45mins
Which activity does an analyst perform in the discovery phase of the data analytics life cycle?
Collecting data
Cleaning data
Identifying outliers
Identifying business needs
In which phase of the data analytics life cycle does an analyst build a histogram?
Data acquisition
Data exploration
Discovery
Predictive modeling
An analyst applies a statistical formula to obtain the average temperature for a city over the last 50 years. Which phase of the data analytics life cycle is represented by this activity?
Data acquisition
Exploratory data analysis
Predictive modeling
Data reporting
An analyst has been tasked with defining data columns that could contain null values. Which activity of the data acquisition phase is represented?
Collecting data
Disqualifying data sources
Detecting missing values
Transforming improperly formatted text
Which activity in the data analytics life cycle occurs during the data acquisition phase and requires the most time and effort from the data analyst?
Selecting the data sources
Importing data into a database
Cleaning data
Defining goals
What might be developed by data analysts when acquiring data from a data warehouse?
The procedures for extracting files from the data warehouse
The procedures for updating tables in the data warehouse
The relational structure of tables
The SQL queries of data within the tables
What can be identified using a box plot?
Frequency
Correlation
Interquartile range
Mean
What will be a consequence of poor attention to detail during the data exploration phase?
Not enough variables will be considered in the analysis.
The outcome of the analysis will be misaligned to business needs.
The analyst will lack insight into the structure of the data set.
The model will be built using the wrong data set.
Which aspect of data exploration occurs when an analyst writes code to compile a bar graph of dog food sales per month?
Performance of a correlation analysis
Analysis of data anomalies
Verification through visualization
Determination of variabilities
An oil company uses robots and sensors to detect how pipeline corrosion changes over time. The collected data is then used in a predictive model that estimates when a pipe should be replaced. How does the predictive model serve this oil company?
To minimize interruptions from maintenance shutdowns
To minimize the need for workforce safety training
To improve compliance with pipeline construction standards
To improve compliance with pipeline disposal standards
During which phase in the data analytics life cycle would a churn analysis be performed?
Data cleaning
Data acquisition
Predictive analysis
Representation and reporting
Which mistake is commonly made during the predictive analytics phase?
The data are separated into different sets.
The variables are separated into response and independent variables.
The data are prepared before the model is developed.
The model is developed before the research question is known.
Why might a data analyst resample a data set with replacement data in a data mining project?
Misidentification of causation due to correlation
Wrong variables chosen for analyzation
Too little data for training and testing data sets
Skewed data resulting from outliers
A data analyst has identified combinations of sales transactions that frequently occur together in data over the past 5 years. Which phase of the data analytics life cycle is represented by this analysis?
Data acquisition
Representation and reporting
Data mining
Predictive modeling
An analyst realizes that the data set has been reduced significantly, resulting in sample sizes that are too small. In which phase of the data analytics life cycle did this likely occur?
Data exploration
Data modeling
Data mining
Data discovery
What strategy will contribute to effective data representation and reporting?
Creating a new training data set
Selecting data for a prediction model
Excluding unrelated data
Extracting data from source repositories
What are two purposes of the reporting phase of the data analytics life cycle?
Provide the conclusions from the analysis in an engaging manner
Provide a tool for decision-makers to import and analyze more data
Provide actionable insights that can inform decision-making
Provide an automated way for decision-makers to test their own models
During which phase of the data analytics life cycle does an analyst create a story to report data?
Data acquisition
Data mining
Data reporting
Data cleaning
What is a common duty of a database administrator?
Set project timelines, milestones, and goals
Acquire funding for data analytics projects
Maintain data on the IT infrastructure
Define business needs at the onset of a project
What is an example of an external stakeholder for a data analytics project?
President/CEO
Project manager
Regulatory body
Data analyst’s supervisor
Which party has the primary vision for a data analytics project and brings resources to complete it?
Project sponsors
Project managers
Customers
Data analysts
What does the critical path represent in data analytics project management?
Minimum time to complete independent tasks
Maximum time to complete independent tasks
Minimum time to complete dependent tasks
Maximum time to complete dependent tasks
A data analytics project manager has been asked to complete a project on a very short timeline. Which action is likely to yield positive results?
Outsource the skilled work to an unproven vendor
Expand the team with experienced staff
Require current team to work overtime
Accept lowered quality standards
Which type of project management problem occurs when a data mining task has started but a data acquisition task has not been completed?
Scope
Schedule
Procedure
Cost
How can an organization improve interprofessional communication among team members?
By setting work priorities for team members
By requiring weekly updates on project deadlines
By using tools that provide a team-based collaboration space
By ensuring employees can recite the desired outcomes
A data analyst needs to contact a specific member of the database administration team. Which method should be used to discover the person’s email address?
Ask the project’s customers
Ask the project’s sponsors
Send an email to project stakeholders
Send an email to the team member’s manager
Which feature is commonly found in collaboration tools like Jira, Slack, Teams, and PivotalTracker?
Real-time messaging
Multivariate analysis
Equation editor
Source code management
Which action can the project manager take to keep the team engaged in the analytics project?
At the end of the project, the team publishes an extensive research report and includes it in an email to project stakeholders.
Throughout the project, the project manager communicates insights from the data analytics team and provides ideas of ways to act on those insights.
At the end of the project, the project manager sends an email with the predictive model to the stakeholders so they can use it.
Throughout the project, the project manager holds regular meetings so the entire data analytics team can showcase their work to different departments.
What is an effective method for a data analyst to prepare for a one-on-one meeting with a manager?
Make a written list of all source code comments
Ask other inside employees about the manager’s reputation
Bring a set of questions to draw on to keep the conversation going
Create an essay summarizing steps in the source code
What is a characteristic of active listening?
Actively working on a task while listening to the speaker
Seeking to understand the speaker’s emotions and intent
Focusing intently on the content of the message
Waiting patiently to share one’s own thoughts
Which circumstance could cause a data analyst to have difficulty developing a model to answer a business question?
Project scope creep
Poor project budgeting
Lack of relevant data sources
Lack of stakeholder support
A data analytics project team is preparing to develop a predictive model that will be included within a business intelligence tool for upper management. Which step should be considered for inclusion when creating the project schedule?
Model testing and validation for users
Business intelligence tool interface training
Model training and testing for stakeholders
Business intelligence tool data transformation training
Which task would an analyst consider first during the discovery phase of the data analytics lifecycle?
Seek out necessary data sources.
Formulate a project plan.
Identify project goals.
Develop key metrics.
Numerical measurements of the amount of a toxic chemical substance are recorded in a large database. Which hypothesis can the data analyst answer through exploratory data analytic methods?
The chemical will not cause harm to the habitat’s native species.
The chemical contamination is a result of human activity.
The statistical distribution of the chemical measurements is normal.
The best analytic approach for analyzing the data is linear regression.
A restaurant owner wants to sponsor a data analytics project to provide insights regarding hamburger sales before developing a strategy for increasing sales. Which question is framed appropriately for the data analytics project?
What are the characteristics of customers who buy hamburgers?
What does the supply and demand curve look like for hamburgers?
Which discount coupons should we send to neighborhood residents?
Which varieties of hamburgers are featured by competitors?
Which organizational objective could be accomplished with a descriptive data analytics project using website request logs as a data source?
Explain why web data transfer has increased 25%
Estimate the traffic increase for a new product launch
Improve the speed of server request processing
Recommend a strategy to increase network capacity
A travel website tabulated the results of their latest marketing campaign to understand the relationship of clicks-to-sales conversions. Which area of analytics does this activity represent?
Prescriptive
Proactive
Descriptive
Predictive
An analyst is looking at data that includes the customer’s address, date of purchase, and age. Which question could be answered from this data?
Which customer has spent the highest dollar amount?
Which customer is most likely to respond favorably to the next marketing campaign?
Which state has the highest total customers?
Which product has sold the most in a certain state?
Which outcome should be expected when working with data aggregated from multiple sources? Select two answers.
Consistently named fields
Inconsistently named fields
Data needs cleaning
Data does not need cleaning
Which technique can a project manager use to foster the identification of quality data analytics questions?
Organized project planning
Rigorous data cleaning
Frequent collaboration with the team
Acquisition of abundant project resources
A data analyst notices that the data selected for an analytics project is slightly misaligned with the research question. How can the data analyst resolve this situation?
Halt the data analytics project to pursue a new research question
Dive deeper into the data to identify data quality issues
Adjust the research question to reframe the analysis
Transform the data to a new metric
An analyst has been asked to analyze the open-ended responses from customers on a satisfaction survey. Which type of data is the analyst working with on this project?
Transactional
Secondary
Qualitative
Quantitative
A U.S. company collects and sells information on consumers. Which law prevents the company from collecting information on European Union consumers without their permission?
Electronic Communications Privacy Act
General Data Protection Regulation
Stored Communication Act
Information Nondiscrimination Act
A consumer sues an entertainment streaming company for leaking personal information regarding her viewing habits. Which ASA ethical standard did the streaming company violate?
Conflict of interest
Biases
Privacy
Unfair discrimination
A specific drug is manufactured for the treatment of depression. The company decides to ignore research results on an alternative, less expensive, drug treatment in order to make higher profits. Which ASA ethical standard has the company violated?
Unfair discrimination
Reproducible results
Conflict of interest
Transparent assumptions
What do open-source software tools and widely available analysis tools, such as spreadsheets, help accomplish?
Data schemas
Data democratization
Data security
Data compliance
What is a feature of SQL?
Choose 2 answers.
The basic language is the same across database servers.
It is used with structured data and unstructured data.
It is an object-oriented programming language.
It has built-in chart and graph creation.
What is an example of unstructured data?
Names, dates, and addresses
Credit card numbers that include a credit score
Text messages that include video
Height, weight, and gender
Which tool should a researcher use to conduct a univariate analysis on complex statistical data?
Tableau
Power BI
R
SQL
Which statistical technique should be used to draw conclusions about an entire population based on a representative sample?
Correlation
Bayes theorem
Hypothesis testing
Measures of central tendency
What is an example of random sampling of college students?
Surveying students chosen arbitrarily from around the entire college campus
Surveying every student in the college library
Surveying students chosen arbitrarily in the library of the university
Surveying every student on campus
Which type of analysis would be used to predict a binary outcome based on a set of independent variables?
Hypothesis testing
Descriptive statistics
Regression
Time Series
Which type of data analysis is appropriate if the goal is to minimize the cost of a diet, using a data set consisting of the following variables: protein content, fat content, and cost per unit?
Decision trees
Calculus
Optimization
Bayes’ theorem
Which technique can be used to determine the likelihood that a positive diagnostic test result indicates whether the disease is actually present?
Bayes’ theorem
Central limit theorem
Regression
Optimization
Which concept should be considered when choosing variables for inclusion in a linear regression model?
Feasibility of merging the variables
Feasibility of controlling the variables
Feasibility of testing the variables
Feasibility of classifying the variables
A neural network algorithm in machine learning endeavors to recognize underlying relationships in a set of data. What does this process mimic?
The way a computer processes data
The way the human brain operates
The way architects establish functionality
The way that social media builds networks
Which characteristics are used to group data together in a cluster analysis?
Choose 2 answers.
Distance
Similarity
Shape
Size
Which tool has libraries that expand its visualization capabilities?
Python
Tableau
Adobe Infographics
D3.js
Which tools can be used for performing statistics and creating interactive data visualization for large datasets from various sources?
Choose 2 answers.
Gantt Chart
SQL
Tableau
R
Which type of data representation should a data analyst use to display expense categories as a percentage of total business expenses?
Map visualization
Line chart
Pie chart
Scatter plot
Which is NOT a topic of interest in the business understanding (planning/discovery) phase?
Scope Project
Identify stakeholders and research questions/KPIs
Identify timeline, budget, and participants
Gather/collect data from a variety of sources
Which is NOT a topic of interest in the data acquisition (extraction, data gathering, data query, data collection, ETL, web scraping) phase?
Scope Project
Use of API to download data from an external source
Provide structure to data accessible via relational databases (SQL)
Build data pipeline (ETL)
Gather/collect data from a variety of sources
Which is NOT a topic of interest in the data cleaning (wrangling, scrubbing, munging) phase?
Fixing improperly formatted values
Dealing with duplicates, missing data, and outliers
Provide structure to data accessible via relational databases (SQL)
Data reduction
Which is NOT a topic of interest in the data exploration (exploratory data analysis (EDA) & descriptive statistics) phase?
Pattern discovery
Identify basic correlations between variables
Central Tendency/ Measures of center (e.g., mean, median, mode), variability (e.g., standard deviations and quartiles) and distributions (e.g., normal, skewed, etc.)
Data reduction
Which is NOT a topic of interest in the Predictive Modeling (Data Modeling, Correlation based models, Regression models, Time series) phase?
Estimate/project future values or likelihood of an event.
Extend correlations found in EDA to mathematical models
Central Tendency/ Measures of center (e.g., mean, median, mode), variability (e.g., standard deviations and quartiles) and distributions (e.g., normal, skewed, etc.)
Predict/determine output values based on input values
Cross-validation of predictive models to ensure accuracy.
Which is NOT a topic of interest in the data mining (Machine Learning, Deep Learning, AI or artificial intelligence, Supervised/Unsupervised Models) phase?
Creating training and testing datasets to build models from
Extend correlations found in EDA to mathematical models
Identify/detect patterns
Determine if groups (clusters) exist in data and Classify data into groups
Create models that “learn” and improve (e.g., machine/deep learning, AI, etc.)
What is a potential problem during the Business Understanding/
Planning/Discovery phase?
Outliers not dealt with can cause problems with statistical models due to excessive variability.
Lack of clear focus on stakeholders, timeline, limitations, and budget could potentially derail an analysis
Quality and type of data may make access more difficult
Some cleaning techniques could dramatically change data/outcomes
What is a potential problem during the Data cleaning/Wrangling/Scrubbing/Munging phase? Choose two
Outliers not dealt with can cause problems with statistical models due to excessive variability.
Lack of clear focus on stakeholders, timeline, limitations, and budget could potentially derail an analysis
Quality and type of data may make access more difficult
Some cleaning techniques could dramatically change data/outcomes
This phase is also known as the discovery phase. During this phase, an analyst defines the major questions of interest that need to be answered, determines the needs of the stakeholders, and assesses the resource constraints of the project.
Business understanding
Data acquisition
Data cleaning
Data exploration
This is the phase of collecting data. Frequently, data will be retrieved from a database, perhaps a component of a data warehouse, by using a language like SQL. Sometimes, data might not be available. In these cases, the analyst will use tools such as web scraping or surveys to acquire it.
Data Mining
Data acquisition
Data cleaning
Data exploration
This phase is referred to by a variety of names. Common alternative terms include data cleansing, data wrangling, data munging, and feature engineering. When this phase is ignored or skipped, the results from the analysis may become irrelevant. There is no one common tool supporting this phase. An analyst will use SQL, Python, R, or Excel to perform various data modifications and transformations. Data quality is measured in terms of uniqueness and relevance.
Data Mining
Data acquisition
Data cleaning
Data exploration
In this phase, the analyst begins to understand the basic nature of data and the relationships within it. This phase often relies on the use of data visualization tools and numerical summaries, such as measures of central tendency and variability.
Data Mining
Data acquisition
Data cleaning
Data exploration
These tools became popular with the ability of computers to look for patterns in large amounts of data. Tools such as Python and R play an important role in this phase. At times you may find that "machine learning" is used as a synonym for "data mining." However, some in the industry might refer to "machine learning" as a specialized segment of data mining techniques that continually update (i.e., "learn") to improve its modeling over time.
Data Mining
Data acquisition
Data cleaning
Data exploration
A technique that allows us to predict an outcome (either numerical or categorical) based on a set of predictor variables. One might think of this process as providing an output given a set of input variables. For example, an analyst might predict the churn of customers based upon various customer demographic data.
Classification
Clustering
Regression
Time Series
A a technique in which the analyst wants to assign an item to a specific category based on various conditions and attempts to identify an unknown object among known groups.
Classification
Clustering
Regression
Time Series
