D204 WGU Exam Study Guide | 100% Correct | Verified | 2024 Version

D204 WGU Exam Study Guide | 100% Correct | Verified | 2024 Version 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