Truncating dates

It’s common in any business context to understand how quickly product growth might be occurring over a specified time period.

In this exercise, we’ll revisit your understanding of the DATETRUNC() function and how you can apply this to help calculate bike ridership demand!

If you lost progress, load the workbook 1_3_datetrunc.twbx from the Workbooks folder on the Desktop.

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Time Series Analysis in Tableau

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Time Series Analysis in Tableau

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In this chapter, we’ll begin our journey by understanding the different types of time series that exist, as well as the analytical methodologies to analyze this. Once you’ve understood the fundamental concepts, you’ll learn how to reformat time series data in preparation for univariate and multivariate visualizations.

Exercise 1: An introduction to time series Exercise 2: Time series classification Exercise 3: Date time formatting in Tableau Exercise 4: Reformatting with datename Exercise 5: Partitioning with datepart Exercise 6: Date transformations and visualizations Exercise 7: Date time knowledge check Exercise 8: Reformatting dates in Tableau Exercise 9: Truncating dates

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Exercise 10: Calculating durations from date timestamps Exercise 11: Classifying time series data Exercise 12: Analyzing ridership behaviour

Everyone wants to analyze data, but it’s important the data is clean. In this chapter, we’ll learn how to clean up time series data using Tableau’s inbuilt date functions where we’ll reshape, split and rejoin our data based on the different time context we’re interested in. Beyond this, we’ll dive into the concepts of seasonality, trend analysis and anomaly detection!

Exercise 1: Date string manipulation and reformation Exercise 2: Valid or not?Exercise 3: Reforming dates in practice Exercise 4: Parsing the date Exercise 5: Weekly truncations Exercise 6: Seasonality and anomalies in time series data Exercise 7: Anomalous techniques Exercise 8: Visualizing seasonality and percentiles Exercise 9: Changing the seasons Exercise 10: Trending matters Exercise 11: Seasonal variance with box plots Exercise 12: Analyzing avocado pricing discrepencies

In this final chapter, you’ll learn all about the powers of window functions and statistical techniques like Z-Values, where you’ll create your very own anomaly classifiers to identify optimal pricing arbitrage opportunities for a trading company! In the second part of this chapter, we’ll compare multiple anomaly detection techniques, equipping you with a broad range of approaches to treating and analyzing time series!

Exercise 1: Window functions in Tableau Exercise 2: Understanding window functions Exercise 3: Calculations with window functions Exercise 4: Smoothing data with moving averages Exercise 5: Selecting moving average time windows Exercise 6: Identifying relationships with window correlations Exercise 7: Anomaly detection with window functions Exercise 8: Muscle memory with Z-scores and standard deviation Exercise 9: A deep dive into window calculations Exercise 10: Unpacking volatility Exercise 11: Setting upper boundaries Exercise 12: Anomaly detection with Z-scores Exercise 13: Congratulations!