D204 - MW Data Analytics Life Cycle Questions and Answers

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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?

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