WGU D204 Masters of Data Analytics Journey Exam Questions and Answers 100% Pass

WGU D204 Masters of Data Analytics Journey Exam Questions and Answers 100% Pass Business Understanding (order) - Answer- 1st in Data Analytics Lifecycle Data Acquisition (order) - Answer- 2nd in Data Analytics Lifecycle Data Cleaning (order) - Answer- 3rd in Data Analytics Lifecycle Data Exploration (order) - Answer- 4th in Data Analytics Lifecycle Predictive Modeling (order) - Answer- 5th in Data Analytics Lifecycle Data Mining/Machine Learning (order) - Answer- 6th in Data Analytics Lifecycle Reporting and Visualization (order) - Answer- 7th in Data Analytics Lifecycle Business Understanding - Answer- - Also known as the discovery phase or planning phase - Analyst defines the major questions of interest - Assesses the resource constraints of the project - Determines the needs of stakeholders Data Acquisition - Answer- - Collecting Data - Data retrieved from DB - Use SQL to obtain from Data Warehouse - If data is not available, web scraping and surveys are used to acquire it Data Cleaning - Answer- - Referred to as 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 Exploration - Answer- - 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. Predictive Modeling - Answer- - These tools allow an analyst to move beyond describing the data to creating models that enable predictions of outcomes of interest. - Tools such as Python and R play an important role in automating the training and use of models. Data Mining - Answer- - 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. Reporting and Visualization - Answer- - an analyst tells the story of the data and uses graphs or interactive dashboards to inform others of the findings from the analyses. - Interactive dashboard tools, such as Tableau, give even the novice user the ability to interact with the data and spot trends and patterns. - Often, the goal of this phase is to provide actionable insights for various stakeholders. Business Understanding Problems - Answer- Lack of clear focus on stakeholders, timeline, limitations and budget could potentially derail an analysis Data Acquisition Problems - Answer- Quality and type of data may make access more difficult Data Cleaning Problems - Answer- Some cleaning techniques could dramatically change data/outcomes Outliers not dealt with can cause problems with statistical models due to excessive variability. Data Exploration Problems - Answer- Skipping this step could enable faulty perceptions of the data which hurt advanced analytics. Predictive Modeling Problems - Answer- - 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. Data Mining Problems - Answer- Running on entire data is problematic; need to subset data into training and testing datasets to build models. Reporting and visualization Problems - Answer- - Due to potential large audience consumption, mistakes can cause bad business decisions and loss of revenue - Improper scales used in graphs could push for interpretations of the story that is inaccurate Descriptive - Answer- Key focus: Observation Main question: What happened? Diagnostics - Answer- Key focus: Explained reaso