D204 the Data Analytics Journey Questions and Answers 100% Solved

D204 the Data Analytics Journey Questions and Answers 100% Solved Look at the data. 1) Visualize the data - graphs, histograms, bell curve 2) Compute Univariate Descriptive Statistics: mean (average), mode (most comm ...

D204 the Data Analytics Journey Questions and Answers 100% Solved Look at the data. 1) Visualize the data - graphs, histograms, bell curve 2) Compute Univariate Descriptive Statistics: mean (average), mode (most common), median (splits into two equal halves). So ONE Value. 3) Measures of association: connection between the variables in your data. Range: high and low, Quartiles, Variance, Standard Deviation, Correlation coefficients, regression analysis. (this was the click to sales conversions question) Must be attentive to outliers, open-ended and undefined scores (screen data!) Use your words: explain describe Data Analytics Lifecyle per course requirements ✔✔Discovery Phase (Business understanding) Data Acquisition Data Exploration Predictive Modeling Data Mining Data Reporting & Representation Data Analytics Lifecycle: Discovery ✔✔This is where you identify business needs. Defining the business reason that any analysis is needed. Working with stakeholders to help them ask better questions so that both they and you understand the outcome. Do you have the data you need to answer this question? If not, is there another way to accomplish it with what you do have? Data Analytics Lifecycle: Data Acquisition ✔✔Collect/Get Data from various sources and cleaning it. Getting Data may include creating SQL queries of data within the tables; or data warehouses. Cleaning is the most labor-intensive phase (both in time and effort). You may have to identify outliers here or detect missing values (null values in columns). An analyst will use SQL, Python, R, or Excel to perform various data modifications and transformations. When cleaning is skipped, or ignored, the results from the analysis may become irrelevant Data Analytics Lifecycle: Data Exploration ✔✔Now that the data is cleaned, you can begin to familiarize yourself with it. You are beginning to understand the basic nature of data and the relationships. Making histograms and generally understanding what is included. Creating bar graphs will give you verification through visualization. This may include applying a statistical formula to obtain the avg. temp of a city over the last 50 years. Poor attention to detail in this phase will give you a lack of insight into the structure of the data set. Data Analytics Lifecycle: Predictive Modeling ✔✔Is where you are taking those insights and operationalizing them to predict future outcomes (oil company uses robots to detect corrosion over time - reducing shutdown/interruptions). You go beyond describing to actual modeling. Churn Analysis could be performed (evaluation of a company's customer loss repeat in order to reduce it (analyses your product and how people use it). Common mistake is to develop a model before the research question is known. Again, Python and R play key roles here. Data Analytics Lifecycle: Data Mining ✔✔Find patterns and insights. Find correlations and test hypothesis. Example: data analyst has identified combinations of sales transactions that frequently occur together in data over the past 5 years. It is possible in this phase to reduce significantly the data which results in a sample size that is too small. Some call this machine learning. Python and R also play key roles here. Data Analytics Lifecycle: Data Reporting & Representation ✔✔Analysts creates a story to report on data findings. Provide actionable insights that can inform decision makers; provide conclusion from the analysis in engaging manner. Effective data reporting is to exclude unrelated data. Here you may use Tableau or PowerBi. Data Science Pathway (per video) ✔✔Planning