D204 - MW Data Analytics Life Cycle Questions and Answers

D204 - MW Data Analytics Life Cycle Questions and Answers 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 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. 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. 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. 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. 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. 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. 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. 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 What is the Data Science Pathway? 1. Define the goals 2. Organize resources 3. Coordinate people 4. Schedule project Summarize the Planning phase of the Data Science Pathway. 5. Get data 6. Clean data 7. Explore data 8. Refine data Summarize the Wrangling phase of the Data Science Pathway. 9. Create model 10. Validate model 11. Evaluate model 12. Refine model Summarize the Modeling phase of the Data Science Pathway. 13. Present model 14. Deploy model 15. Revisit model 16. Archive assets 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. Describe "Define the goals" from the Data Science Pathway. Lives in the planning phase. Identify what you will need for the project from computers, software, people, and time available. Describe "Organize resources" from the Data Science Pathway. Lives in the planning phase. Projects are a team effort. Important to layout the flow of the project so people know when they will be needed. Describe "Coordinate people" from the Data Science Pathway. Lives in the planning phase. Use time boxing by task to avoid project time exploding into too much time. Describe "Schedule project" from the Data Science Pathway. Lives in the wrangling phase. Secure the data from in house, open sources, public APIs, etc Describe "Get data" from the Data Science Pathway. Lives in the wrangling phase. Get the data ready so it fits in analysis tools. Allows you to process and get insights out of the data. Describe "Clean data" from the Data Science Pathway. Lives in the wrangling phase. Create visualizations, numerical summaries, etc to get a feel for the data. Describe "Explore data" from the Data Science Pathway. Lives in the wrangling phase. Recategorize variables, combine variables to create new ones. Includes anything needed to prepare the data for new insights. Describe "Refine data" from the Data Science Pathway. Lives in the modeling phase. Create regressions, decision trees, and/or deep learning (neural networks). Describe "Create the model" from the Data Science Pathway. Lives in the modeling phase. Ensure that the data will genera