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.


An introduction to time series

Time series classification

Date time formatting in Tableau

Reformatting with datename

Partitioning with datepart

Date transformations and visualizations

Date time knowledge check

Reformatting dates in Tableau

Truncating dates

Calculating durations from date timestamps

Classifying time series data

Analyzing ridership behaviour

Introduction to Time Series Analysis in Tableau

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!

Date string manipulation and reformation

Valid or not?

Reforming dates in practice

Parsing the date

Weekly truncations

Seasonality and anomalies in time series data

Anomalous techniques

Visualizing seasonality and percentiles

Changing the seasons

Trending matters

Seasonal variance with box plots

Analyzing avocado pricing discrepencies

Data Preparation for Time Series Analysis

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! 

Window functions in Tableau

Understanding window functions

Calculations with window functions

Smoothing data with moving averages

Selecting moving average time windows

Identifying relationships with window correlations

Anomaly detection with window functions

Options

True

False

Muscle memory with Z-scores and standard deviation

A deep dive into window calculations

Unpacking volatility

Setting upper boundaries

Anomaly detection with Z-scores

Congratulations!

Windowing Functions and Moving Averages in Tableau

Workbooks and Datasources

Time series is all around us; from the post you’ve made on Twitter, to the daily fluctuations in the financial markets - these are all examples of time-series data points that need to be analyzed. In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.

Time series is all around us; from the post you’ve made on Twitter, to the daily fluctuations in the financial markets - these are all examples of time-series data points that need to be analyzed. In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.

<h2>Discover the power of time series</h2>
You’ll start with the basics where you’ll learn the different types of time series that exist, as well as the analytical methodologies to analyze this. Once you’ve got the fundamentals down, you’ll learn how to reformat time series data in preparation for univariate and multivariate visualizations.

<h2>Data Preparation & Time series</h2>
Everyone wants to analyze data, but it’s important we clean this up as well. We’ll learn how to clean up time-series data using Tableau’s date functions where we’ll reshape our data based on the different time context we’re interested in. Additionally, we’ll learn the ins and outs of using LODs to automate these calculations for us!

<h2>Analyzing Time series Data</h2>
In the final parts of this course, you’ll learn about statistical techniques like Z-Values, where you’ll your very own Anomaly detection fields in Tableau to identify optimal arbitrage opportunities for a trading company! You’d have learnt what time series is, how to treat it, and more importantly, how to use statistical measures to tell an impactful story. Let’s get started!

Calculations in Tableau

In this course, you’ll learn to classify, treat and analyze time series; an absolute must, if you’re serious about stepping up as an analytics professional.


Time Series Analysis in Tableau

Z-score measures the relative position of a data point as a number of standard deviations, the standard deviation quantifies the dataset's spread

Z-score measures the dispersion of data points from the mean, while the standard deviation represents the average distance from the median

Z-score calculates the average distance between data points, while the standard deviation determines the likelihood of extreme values occurring

Z-score represents the proportion of data points within a certain range, while the standard deviation determines the skewness of the dataset

Muscle memory with Z-scores and standard deviation

Time Series Analysis in Tableau

実践的なインタラクティブ演習