Data Analytics Journey, D204 Exam Study Questions and Answers with Complete Solutions 2024 Already Passed!!

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 Data Acquisition Problems - Quality and type of data may make access more difficult Data Cleaning Problems - Some cleaning techniques could dramatically change data/outcomes Outliers not dealt with can cause problems with statistical models due to excessive variability. Data Exploration Problems - Skipping this step could enable faulty perceptions of the data which hurt advanced analytics. Predictive Modeling Problems - - 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. Data Mining Problems - Running on entire data is problematic; need to subset data into training and testing datasets to build models. Reporting and visualization Problems - - Due to potential large audience consumption, mistakes can cause bad business decisions and loss of revenue - Improper scales used in graphs could push for interpretations of the story that is inaccurate Descriptive - Key focus: Observation Main question: What happened? Diagnostics - Key focus: Explained reason Main question: Why did it happen? Descriptive Example - In a healthcare setting, an unusually high number of people are admitted to the emergency room in a short period of time. ____ analytics tells you that this is happening and provides real-time data with all the corresponding statistics (date of occurrence, volume, patient details, etc.). Diagnostic Example - In the healthcare example mentioned earlier, ___ analytics would explore the data and make correlations. For instance, it may help you determine that all of the patients' symptoms — high fever, dry cough, and fatigue — point to the same infectious agent. You now have an explanation for the sudden spike in volume at the ER. Predictive Example - Back in our hospital example, ___ analytics may forecast a surge in patients admitted to the ER in the next several weeks. Based on patterns in the data, the illness is spreading at a rapid rate. Prescriptive Example - Back to our hospital example: now that you know the illness is spreading, the ___ analytics tool may suggest that you increase the number of staff on hand to adequately treat the influx of patients. Causation - there is a real-world explanation for why this is logically happening; it implies a cause and effect Show Causality - A/B testing or experiments A/B testing example - you own a website with a red login button. You're thinking that you should change it to blue, since it looks too ugly. To determine which color you should use, you ask your users. You randomly sample 100 users and show 50 users the red button and 50 users the blue button. You measure the ratio of people who login to your website for each group, and see if there's a big difference. This approach randomly assigns subjects to two groups: an A and B group. Planning - 1. Define the goals 2. Organize resources 3. Coordinate people 4. Schedule project Wrangling - 5. Get data 6. Clean data 7. Explore data