The Data Analytics Journey, D204 Questions and Answers with complete solution

Business Understanding (order) - 1st in Data Analytics Lifecycle Data Acquisition (order) - 2nd in Data Analytics Lifecycle Data Cleaning (order) - 3rd in Data Analytics Lifecycle Data Exploration (order) - 4th in Data Analytics Lifecycle Predictive Modeling (order) - 5th in Data Analytics Lifecycle Data Mining/Machine Learning (order) - 6th in Data Analytics Lifecycle Reporting and Visualization (order) - 7th in Data Analytics Lifecycle Business Understanding - - 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 - - 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 - - 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 - - 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 - - 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 - - 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 - - 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 - Lack of clear focus on stakeholders, timeline, limitations and budget could potentially derail an analysis