Once you have the data, you need to analyze it and visualize it in a way that is meaningful and understandable for your audience. Data analysis and visualization can help you explore and summarize the data, test and compare the data, as well as model and forecast the data. For example, descriptive statistics such as mean, median, mode, or standard deviation can be used to describe the basic features of the data, while graphical methods like histograms, box plots, or scatter plots can be used to display the data visually. Inferential statistics such as hypothesis testing, confidence intervals, or correlation can be used to draw conclusions or make predictions based on the data. Comparative methods like t-tests, ANOVA, or chi-square can be used to compare the data across groups, categories, or time periods. Advanced techniques like regression, classification, or clustering can create mathematical models that explain or predict the behavior of the data. Time series methods like ARIMA, exponential smoothing, or neural networks can be used to forecast the data based on historical trends. Depending on the method used for analysis and visualization, different tools and techniques may be needed to perform and present the analysis such as statistical software or packages like R, Python, or SPSS; visualization software or libraries such as Tableau, Power BI, or Matplotlib; etc.