WGU D204 Data Analytics Study Questions and Answers 2023 (Verified Answers)
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WGU D204 Data Analytics
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WGU D204 Data Analytics
WGU D204 Data Analytics Study Questions and Answers 2023 (Verified Answers)
Which of these is NOT a topic of interest for Discovery/Planning/Business Understanding?
A. Project Scope
B. Identify stakeholders and research questions/KPIs
C. Build a data pipeline (ETL)
D. Identify timeline, budg...
wgu d204 data analytics study questions and answers 2023 verified answers which of these is not a topic of interest for discoveryplanningbusiness understanding a project scope b identify stake
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WGU D204 Data Analytics Study Questions and Answers
2023 (Verified Answers)
Which of these is NOT a topic of interest for Discovery/Planning/Business
Understanding?
A. Project Scope
B. Identify stakeholders and research questions/KPIs
C. Build a data pipeline (ETL)
D. Identify timeline, budget, and participants
C
What is a potential problem to consider in the planning phase?
A. Lack of clear focus on stakeholders, timeline, limitations, and budget
B. Quality and type of data may make access more difficult
C. Some cleaning techniques could dramatically change data/outcomes
D. Outliers not dealt with can cause problems with statistical models due to
excessive variability.
A
In what phase does the analyst identify the stake holders and research
questions?
Business Understanding/Planning/Discovery
In what phase does the analyst deal with the following:
Gather/collect data from a variety of sources
Provide structure to data accessible via relational databases (SQL)
Build data pipeline (ETL)
Use of API to download data from an external source
Data acquisition
In what phase does the analyst deal with the following:
Fixing improperly formatted values
Dealing with duplicates, missing data, and outliers
Data reduction
Data cleaning/wrangling/scrubbing/munging
In what phase does the analyst deal with the following:
Central Tendency/ Measures of center (e.g., mean, median, mode), variability
(e.g., standard deviations and quartiles) and distributions (e.g., normal, skewed,
etc)
Identify basic correlations between variables
Pattern discovery
Data exploration/Exploratory Data Analysis(EDA)/Descriptive Statistics
In what phase does the analyst deal with the following:
Estimate/project future values or likelihood of an event.
Extend correlations found in EDA to mathematical models
, Predict/determine output values based on input values
Cross-validation of predictive models to ensure accuracy.
Predictive Modeling/Data Modeling/Correlation based models/Regression models/Time
Series
In what phase does the analyst deal with the following:
Creating training and testing datasets to build models from
Identify/detect patterns
Determine if groups (clusters) exist in data
Classify data into groups
Create models that "learn" and improve (e.g., machine/deep learning, AI, etc)
Data Mining/Machine Learning/AI/Supervised, Unsupervised Models
In what phase does the analyst deal with the following:
Tell a story with data
Provide a summary of analytic analysis
Provide insights to stakeholders
Create insightful graphs that showcase trends and forecasts
Reporting and visualization/Dashboards
What is a potential problem in the data acquisition/query/collection step?
Quality and type of data may make access more difficult
What are two potential problems in the Data Cleaning/Wrangling step?
Some cleaning techniques could dramatically change data/outcomes
Outliers not dealt with can cause problems with statistical models due to excessive
variability.
What is a potential problem in the data exploration/descriptive statistics step?
Skipping this step could enable faulty perceptions of the data which hurt advanced
analytics.
What are potential problems in the Predictive Modeling step?
Too many input variables (predictors) can cause problems
Correlation does not imply causation.
Time series models often need sufficient time data to offer precise trending.
Predictive model accuracy should be assessed using cross-validation.
What is a potential problem in the data mining/supervised models step?
Running on entire data is problematic; need to subset data into training and testing
datasets to build models.
What are two potential problems in the reporting and visualization/dashboards
step?
Due to potential large audience consumption, mistakes can cause bad business
decisions and loss of revenue
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