WGUD204 - MW Data Analytics Life Cycle Already Passed!!

Many versions but one proposed in this class: 1) Business Understanding 2) Data Acquisition 3) Data Cleaning 4) Data Exploration 5) Predictive Modeling 6) Data Mining/Machine Learning 7) Reporting and Visualization and loop back at this point - ANSWER What are the phases of the data analytics life cycle proposed by WGU? 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, understand the needs of the stakeholders, and assess the resource constraints of the project. - ANSWER Summarize the Business Understanding phase of the data analytics life cycle. 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 and the analyst will use tools such as web scraping or surveys to acquire it. - ANSWER Summarize the Data Acquisition phase of the data analytics life cycle. 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. - ANSWER Summarize the Data Cleaning phase of the data analytics life cycle. 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. - ANSWER Summarize the Data Exploration phase of the data analytics life cycle. These tools enable an analyst to move beyond describing the data to creating models that enable predicting outcomes of interest. Tools such as Python and R play an important role in automating the training and using of models. - ANSWER Summarize the Predictive Modeling phase of the data analytics life cycle. These tools became popular with the ability of computers to look for patterns in large amounts of data. In the industry, you will sometimes find terms like machine learning used in place of data mining. Tools such as Python and R play an important role in this stage. - ANSWER Summarize the Data Mining/Machine Learning phase of the data analytics life cycle. In this phase, 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, allow even the novice user the ability to interact with the data and spot trends and patterns. - ANSWER Summarize the Reporting and Visualization phase of the data analytics life cycle. This is another way to show the data analytics life cycle. It includes the following steps: 1) Planning 2) Wrangling 3) Modeling 4) Applying - ANSWER What is the Data Science Pathway? 1. Define the goals 2. Organize resources 3. Coordinate people 4. Schedule project - ANSWER Summarize the Planning phase of the Data Science Pathway. 5. Get data 6. Clean data 7. Explore data 8. Refine data - ANSWER Summarize the Wrangling phase of the Data Science Pathway. 9. Create model 10. Validate model 11. Evaluate model 12. Refine model - ANSWER Summarize the Modeling phase of the Data Science Pathway. 13. Present model 14. Deploy model 15. Revisit model 16. Archive assets - ANSWER Summarize the Applying phase of the Data Science Pathway. Lives in the planning phase. Determine what you want to know. Helps keep you on target. Helps in every step of the Data Science Pathway. - ANSWER Describe "Define the goals" from the Data Science Pathway. Lives in the planning phase. Identify what you will need for the project