As you know from my previous Tableau Conference sessions and blog posts (TC18, TC17, TC16), I love social media! So thank you for your likes, shares, retweets…
Today marks a significant milestone in my journey with data visualization – the release of my comprehensive Tableau book, Visual Analytics with Tableau (Amazon). This book is more than just a guide; it’s a comprehensive resource for unlocking the power of visual analytics. Also, check out the Visual Analytics with Tableau companion website for further news.
From the Back Cover
A 4-COLOR JOURNEY THROUGH A COMPLETE TABLEAU VISUALIZATION FOR NON-TECHNICAL BUSINESS USERS
Tableau is a popular data visualization and analytics tool favored by financial analysts, marketers, statisticians, business and sales professionals, data scientists, developers, and many others who need to explore insights and present visual, easy-to-understand data. Visual Analytics with Tableau is an accessible, step-by-step introduction to the world of visual analytics. This up-to-date guide is ideal for both beginners and more experienced users seeking a practical introduction to the fields of data analysis and visualization. Through hands-on examples and exercises, readers learn how to analyze their own data and clearly communicate the results.
This guide covers everything you need to get started with Tableau, from the first steps of connecting to data, creating different types of charts, and adding calculation fields to more advanced features such as table calculations, forecasts, clusters, and R, Python, and MATLAB integration for sophisticated statistical modelling. User-friendly instructions for existing options within the Tableau ecosystem–Tableau Desktop, Tableau Prep, Tableau Server, Tableau Online, and Tableau Public–enable you to integrate, clean, and prepare your data and share your work with others.
Why Choose Visual Analytics with Tableau?
Latest Versions Covered: Stay up-to-date with the newest features of Tableau, plus Tableau Prep, Tableau’s cutting-edge data integration application.
No Prior Experience Required: Whether you’re a beginner or an expert, this book caters to all. No background in mathematics or programming is needed.
Exploring Data Visually: This book Focuses on the visual analytics functionality of Tableau rather than complex statistical programming.
Expert Guidance: Gain insights from Alexander Loth, Tableau’s thought leader, and world-renowned visualization expert. Alexander’s hands-on approach ensures a smooth learning curve.
Advanced Functionality: Dive deep into advanced Tableau functionalities and explore various data structures to elevate your analytical capabilities.
Top-notch Presentation: This book provides easy-to-follow instructions, full-color illustrations, learning tools, online resources, and more.
Endorsements
If you’re getting started with visual analytics and Tableau, this book will teach you everything you need to know to build the foundations and understand how and why to explore your data visually. Alexander has created a fantastic resource that guides you step by step through the process of preparing your data, using Tableau Desktop to analyse it and finding insights.
Eva Murray, Head of BI and Tableau Zen Master at Exasol
If you’re keen to go from beginner to expert in Tableau, Alexander’s excellent book gives you everything you need to know. With a crisp and clear style, he talks the reader through all aspects of Tableau, from data cleaning through data analysis and into sharing insight with others.
Andy Cotgreave, Author of Big Book of Dashboards and Technical Evangelist at Tableau Software
Visual Analytics with Tableau – an easy to understand book by Alexander Loth, one of Tableau’s very first employees based in Germany, a recognized speaker on countless conferences, and a Tableau Jedi. It contains both the basics and advanced Tableau features. If you ask me: there is nothing more you need to get started with Tableau!
Klaus Schulte, Professor at Münster School of Business & 2019 Tableau Zen Master
Get Your Copy of Visual Analytics with Tableau and Stay Connected!
Thank you to everyone who contributed to the success of this book! Grab your copy of Visual Analytics with Tableau on Amazon and embark on an enlightening journey through visual analytics. If you’re new to Tableau, don’t miss my blog post on joining a Tableau User Group, where you can learn, connect, and grow your skills.
Stay in touch for more insights on data visualization, analytics, and digital transformation. Follow me on Twitter and LinkedIn, and let’s continue learning together:
China AI: The impressive Alibaba Campus in Hangzhou, a hub of innovation and intrapreneurship.
China’s AI revolution is taking the world by storm. In this journey across cities like Hangzhou, discover how China is leading the AI industry, inspiring innovation, and shaping the future.
Currently, I’m on a 4-week China trip, visiting many cities. In Hangzhou, I met CEIBS peers who work for Alibaba. While the Alibaba campus is quite impressive, I got even more impressed by Alibaba’s leadership culture, which is encouraging its employees to innovate as intrapreneurs.
Intrapreneurship at Alibaba: The China AI Model for Success
At the impressive Alibaba Campus in Hangzhou, I discovered the power of intrapreneurship. If you start your own project (a new mobile app, a patent, a scientific paper, etc.), you’re doing it at your own pace. Employees are encouraged to innovate at their own pace, without being micro-managed. Success is rewarded with bonuses. Truly, this is where we can learn from China in the „China AI“ landscape!
China’s AI Ambitions: Leading the New World Order
Yue and me, Hangzhou West Lake
While traveling in China I was reading AI Superpowers: China Silicon Valley, and the New World Order by Kai-Fu Lee, a book that is a must-read to get an idea of where China’s AI ambitions are heading to. What matters most for AI innovation these days, the author argues, is access to vast quantities of data—where China’s advantage is overwhelming.
Visualizing the Journey: Exploring my Tableau Public Viz
Which other cities in China did I visit? Check out my Tableau Public viz:
A visualization of my 2019 China Visit, exploring the cities and experiences.
Interested in Visual Analytics? Grab a copy of my latest book, Visual Analytics with Tableau (Amazon), for a comprehensive guide to mastering data visualization.
Stay Connected and Explore More on China AI
China’s AI is reshaping the global landscape. From Alibaba to the nation’s strategies, China AI inspires and challenges the world. It’s a blend of technology, culture, and energy driving China’s AI revolution.
The experiences and insights from this trip have been truly enlightening. I invite you to join me as I continue to explore the fascinating world of China AI, digital transformation, and visual analytics. Follow me on Twitter and LinkedIn, and let’s continue learning together.
Sind Sie mit der Geschwindigkeit Ihrer Datenanlyse unzufrieden? Oder haben Ihre Dashboards lange Ladezeiten? Dann können Sie bzw. Ihr Datenbank-Administrator folgenden Hinweisen nachgehen, die sich je nach Datenquelle unterscheiden können.
Allgemeine Empfehlungen zur Performance-Optimierung
Möchten Sie die Geschwindigkeit der Analyse verbessern? Dann beachten Sie folgende Punkte:
Benutzen Sie mehrere »kleinere« Datenquellen für individuelle Fragestellungen anstatt einer einzigen Datenquelle, die alle Fragestellungen abdecken soll.
Verzichten Sie auf nicht notwendige Verknüpfungen.
Aktivieren Sie in Tableau die Option »Referentielle Integrität voraussetzen« im »Daten«-Menü (siehe Abbildung 2.20). Wenn Sie diese Option verwenden, schließt Tableau die verknüpften Tabellen nur dann in die Datenabfrage ein, wenn sie explizit in der Ansicht verwendet wird*. Wenn Ihre Daten nicht über referentielle Integrität verfügen, sind die Abfrageergebnisse möglicherweise ungenau.
Abbildung 2.20: Aktivierte Option »Referentielle Integrität voraussetzen« im »Daten«-Menü
* So wird beispielsweise der Umsatz anstatt mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] S INNER JOIN [Product Catalog] P ON S.ProductID = P.ProductID lediglich mit der SQL-Abfrage SELECT SUM([Sales Amount]) FROM [Sales] ermittelt.
Empfehlungen für Performance-Optimierung bei Dateien und Cloud-Diensten
Achten Sie insbesondere beim Arbeiten mit Dateiformaten, wie Excel-, PDF- oder Textdateien, oder Daten aus Cloud-Diensten wie Google Tabellen zusätzlich auf folgende Punkte:
Verzichten Sie auf Vereinigungen über viele Dateien hinweg, da deren Verarbeitung sehr zeitintensiv ist.
Stellen Sie sicher, dass Sie beim Erstellen des Extrakts die Option »Einzelne Tabelle« wählen, anstatt der Option »Mehrere Tabellen« (siehe Abbildung 2.21). Dadurch wird das erzeugte Extrakt zwar größer und das Erstellen des Extrakts dauert länger, das Abfragen hingegen wird um ein Vielfaches beschleunigt.
Abbildung 2.21: Ausgewählte Option »Einzelne Tabelle« im »Daten extrahieren«-Dialog
Empfehlungen für Performance-Optimierung bei Datenbank-Servern
Arbeiten Sie mit Daten auf einem Datenbank-Server, wie Oracle, PostgreSQL oder Microsoft SQL Server, und möchten die Zugriffszeiten verbessern? Dann achten Sie bzw. der dafür zuständige Datenbankadministrator zusätzlich auf folgende Punkte:
Definieren Sie für Ihre Datenbank-Tabellen sinnvolle Index-Spalten.
Legen Sie für Ihre Datenbank-Tabellen Partitionen an.
How can a Tableau dashboard that displays contacts (name & company) automatically look up LinkedIn profile URLs?
Of course, researching LinkedIn profiles for a huge list of people is a very repetitive task. So let’s find a solution to improve this workflow…
Step by Step: Integrating Azure Cognitive Services in Tableau
1. Python and TabPy
We use Python to build API requests, communicate with Azure Cognitive Services and to verify the returned search results. In order to use Python within Tableau, we need to setup TabPy. If you haven’t done this yet: checkout my TabPy tutorial.
2. Microsoft Azure Cognitive Services
One of many APIs provided by Azure Cognitive Services is the Web Search API. We use this API to search for name + company + „linkedin“. The first three results are then validated by our Python script. One of the results should contain the corresponding LinkedIn profile.
3. Calculated Field in Tableau
Let’s wrap our Python script together and create a Calculated Field in Tableau:
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Adding a URL action with our new Calculated Field will do the trick. Now you can click on the LinkedIn icon and a new browser tab (or the LinkedIn app if installed) opens.
This tutorial is just the tip of the iceberg. If you want to dive deeper into the world of data visualization and analytics, don’t forget to order your copy of my new book, Visual Analytics with Tableau (Amazon). This comprehensive guide offers an in-depth exploration of data visualization techniques and best practices.
I’d love to hear your thoughts. Feel free to leave a comment, share this tweet, and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Azure Cognitive Services in Tableau and other data analytics topics.
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