WGU D204 MASTERS OF DATA ANALYTICS JOURNEY UPDATED ACTUAL QUESTIONS AND CORRECT ANSWERS

WGU D204 MASTERS OF DATA ANALYTICS JOURNEY
UPDATED ACTUAL QUESTIONS AND CORRECT
ANSWERS

Question:
Business Understanding (order)
Answer:
1st in Data Analytics Lifecycle

Question:
Data Acquisition (order)
Answer:
2nd in Data Analytics Lifecycle

Question:
Data Cleaning (order)
Answer:
3rd in Data Analytics Lifecycle

Question:
Data Exploration (order)
Answer:
4th in Data Analytics Lifecycle

Question:
Predictive Modeling (order)
Answer:
5th in Data Analytics Lifecycle

Question:
Data Mining/Machine Learning (order)
Answer:
6th in Data Analytics Lifecycle

Question:
Reporting and Visualization (order)
Answer:
7th in Data Analytics Lifecycle

Question:
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

Question:
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

Question:
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.

Question:
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.

Question:
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.

Question:
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.

Question:
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.

Question:
Business Understanding Problems
Answer:
Lack of clear focus on stakeholders, timeline, limitations and budget could potentially derail an analysis

Question:
Data Acquisition Problems
Answer:
Quality and type of data may make access more difficult

Question:
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.

Question:
Data Exploration Problems
Answer:
Skipping this step could enable faulty perceptions of the data which hurt advanced analytics.

Question:
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.

Question:
Data Mining Problems
Answer:
Running on entire data is problematic; need to subset data into training and testing datasets to build
models.

Question:
Reporting and visualization Problems