D204 The Data Analytics Journey

Analyses for Data Science: Descriptive: - Humans are good at finding patterns, but limited bandwidth - so we need to narrow the data. 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,