Muscle memory with Z-scores and standard deviation

We've covered a lot of new terminologies and concepts in this lesson that are critical to your understanding of time series analysis, specifically Z-scores and standard deviations.

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Time Series Analysis in Tableau

ดูคอร์ส

แบบฝึกหัดเชิงโต้ตอบแบบลงมือทำจริง

เปลี่ยนทฤษฎีให้เป็นการลงมือทำด้วยแบบฝึกหัดเชิงโต้ตอบหนึ่งในของเรา

เริ่มแบบฝึกหัด

แบบฝึกหัดนี้เป็นส่วนหนึ่งของหลักสูตร

Time Series Analysis in Tableau

SkillTag.level.intermediateSkillTag.label

4.8+

เริ่มคอร์สฟรี

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 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

แบบฝึกหัดปัจจุบัน

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!