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Time Series Analysis 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.

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4 Hours13 Videos37 Exercises

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Course Description

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.

Discover the power of time series

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.

Data Preparation & Time series

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!

Analyzing Time series Data

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!
  1. 1

    Introduction to Time Series Analysis in Tableau

    Free

    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.

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    An introduction to time series
    50 xp
    Time series classification
    100 xp
    Date time formatting in Tableau
    50 xp
    Reformatting with datename
    100 xp
    Partitioning with datepart
    100 xp
    Date transformations and visualizations
    50 xp
    Date time knowledge check
    50 xp
    Reformatting dates in Tableau
    50 xp
    Truncating dates
    100 xp
    Calculating durations from date timestamps
    100 xp
    Classifying time series data
    100 xp
    Analyzing ridership behaviour
    100 xp
  2. 2

    Data Preparation for Time Series Analysis

    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!

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  3. 3

    Windowing Functions and Moving Averages in Tableau

    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!

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Datasets

Workbooks and Datasources

Collaborators

Collaborator's avatar
Maarten Van den Broeck
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Carl Rosseel
Collaborator's avatar
Jess Ahmet
Chris Hui HeadshotChris Hui

VP at Tracked

Chris currently works as the VP of Product & Analytics at Tracked, an analytics edtech startup. He oversees data science and data engineering teams spanning the US, Australia, and Southeast Asia. Prior to Tracked, Chris created and spearheaded the course creation of the Microsoft, Amazon, and Walmart Data Analytics Career Tracks; educational initiatives aimed at those coming from non-technical backgrounds. Prior to this, Chris excelled in various data scientist roles specializing in time series analysis where he leveraged machine learning to predict asset failure and commodity movements across energy markets. Beyond his professional commitments, Chris mentors aspiring data scientists in the Tracked and Springboard analytics programs.
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