Welcome to the Data & Analytics Tutorials section of my blog, where I share my knowledge and expertise on all things related to data and analytics. As someone who has spent over a decade in the data domain, I’m passionate about sharing what I’ve learned with others and helping them succeed in this exciting field.
In this section, you’ll find a wide range of tutorials on topics such as data visualization, machine learning, data analysis, and more. Whether you’re a beginner just starting out in the field, or an experienced professional looking to expand your knowledge, these tutorials will provide you with the practical skills and insights you need to succeed.
My goal is to make these tutorials as accessible and easy to follow as possible, with step-by-step instructions and real-world examples. I believe that everyone should have the opportunity to learn and grow in the world of data and analytics, and I’m committed to making that a reality through these tutorials.
So, whether you’re looking to improve your skills, expand your knowledge, or just learn something new, join me on this journey as we explore the exciting world of data and analytics together.
Are you ready for Tableau Conference 2018? Don’t miss my Social Media Analytics sessions!
Why do we need Social Media Analytics?
Social Media Analytics transforms raw data from social media platforms into insight, which in turn leads to new business value.
What will your learn in this sessions?
Once you dive into Social Media Analytics, how do you bring it to the next level? Social data can offer powerful insights right away. In this session, you will learn how to build a mature social data program from that foundation and strategies for scaling a social data programme, as well as how to connect directly to your social media data with a web data connector; considerations for building scalable data sources; and tips for using metadata and calculations for more sophisticated analysis.
Where and when are the sessions?
Do you want to learn more about Social Media Analytics with Tableau? Meet me at the 2018 Tableau Conferences in London or New Orleans and join my sessions:
Julia in Tableau: R allows Tableau to execute Julia code on the fly, enhancing your data analytics experience.
Michael, a data scientist, who is working for a German railway and logistics company, recently told me during a FATUG Meetup that he loves Tableau’s R integration and Tableau’s Python integration. As he continued, he raised the question of using functions they have written in Julia. Julia, a high-level dynamic programming language for high-performance numerical analysis, is an integral part of the newly developed data strategy in Michael’s organization.
Tableau, however, does not come with native support for Julia. I didn’t want to keep Michael’s team down and was looking for an alternative way to integrate Julia with Tableau.
This solution is working flawlessly in a production environment for several months. In this tutorial, I’m going to walk you through the installation and connecting Tableau with R and Julia. I will also give you an example of calling a Julia statement from Tableau to calculate the sphere volume.
XRJulia provides an interface from R to Julia. RServe is a TCP/IP server that allows Tableau to use facilities of R.
3. Load libraries and start RServe
After packages are successfully installed, we load them and run RServe:
> library(XRJulia) > library(Rserve) > Rserve()
Make sure to repeat this step every time you restart your R session.
4. Connecting Tableau to RServe
Now let’s open the Help menu in Tableau Desktop and choose Settings and Performance >Manage External Service connection to open the External Service Connection dialog box:
Enter a server name using a domain or an IP address and specify a port. Port 6311 is the default port used by Rserve. Take a look at my R tutorial to learn more about Tableau’s R integration.
5. Adding Julia code to a Calculated Field
You can invoke Calculated Field functions called SCRIPT_STR, SCRIPT_REAL, SCRIPT_BOOL, and SCRIPT_INT to embed your Julia code in Tableau, such as this simple snippet that calculates sphere volume:
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters. Learn more about bidirectional Unicode characters
You can now use your Julia calculation as an alternate Calculated Field in your Tableau worksheet:
Using Julia calculations within Tableau (click to enlarge)
Feel free to download the Tableau Packaged Workbook (twbx) here.
Further Reading: Mastering Julia
If you want to go beyond this tutorial and explore more about Julia in the context of data science, I recommend the book Mastering Julia. You can find it here.
Further Reading: Visual Analytics with Tableau
Join the data science conversation and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Julia in Tableau and other data analytics topics. If you’re looking to master Tableau, don’t forget to preorder your copy of my upcoming book, Visual Analytics with Tableau. (Amazon). It offers an in-depth exploration of data visualization techniques and best practices.
Do you analyze your social media marketing mix? | Photo Credit: via Richard Goodwin
With over 3 billion active social media users, establishing an active presence on social media networks is becoming increasingly essential in getting your business front of your ideal audience. These days, more and more consumers are looking to engage, connect and communicate with their favorite brands on social media.
Adding social media to your customer-centric data strategy will help boost brand awareness, increase followership, drive traffic to your website and generate leads for your sales funnel. In 2017, no organization should be without a plan that actively places their brand on social media, and analyzes their social media data.
Once you’ve started diving into social media analytics, how do you bring it to the next level? This session covers a customer-centric data strategy for scaling a social media data program.
Here are the links (i.e. additional resources) featured during the session to help you formulate your social media data program in order to build a stronger presence and retrieve powerful insights:
Python in Tableau: TabPy allows Tableau to execute Python code on the fly
In 2013, Tableau introduced R Integration, the ability to call R scripts in calculated fields. This opened up possibilities such as K-means clustering, Random Forest models, and sentiment analysis. With the release of Tableau 10.2, we can enjoy a new, fancy addition to this feature: the Python Integration through TabPy, the Tableau Python Server.
Python in Tableau: The Perfect Blend
Python is a widely used general-purpose programming language, popular among academia and industry alike. It provides a wide variety of statistical and machine-learning techniques and is highly extensible. Together, Python and Tableau are the data science dream team to cover any organization’s data analysis needs.
In this tutorial, I’m going to walk you through the installation and connecting Tableau with TabPy. I will also give you an example of calling a Python function from Tableau to calculate correlation coefficients for a trellis chart.
Step by Step: Integrating Python in Tableau
1. Install and start Python and TabPy
Start by clicking on the Clone or download button in the upper right corner of the TabPy repository page, downloading the zip file, and extracting it.
TabPy download via GitHub web page
Protip: If you are familiar with Git, you can download TabPy directly from the repository:
> git clone git://github.com/tableau/TabPy
TabPy download via Git command line interface
Within the TabPy directory, execute setup.sh (or setup.bat if you are on Windows). This script downloads and installs Python, TabPy, and all necessary dependencies. After completion, TabPy is starting up and listens on port 9004.
2. Connecting Tableau to TabPy
In Tableau 10.2 (and later versions), a connection to TabPy can be added in Help > Settings and Performance > Manage External Service Connection:
Tableau main menu
Set port to 9004:
External Service Connection dialogue
3. Adding Python code to a Calculated Field
You can invoke Calculated Field functions called SCRIPT_STR, SCRIPT_REAL, SCRIPT_BOOL, and SCRIPT_INT to embed your Python script in Tableau:
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Now you can use your Python calculation as Calculated Field in your Tableau worksheet:
Tableau workbook featuring a Python calculation
Feel free to download the Tableau Packaged Workbook (twbx) here.
Further Reading: Visual Analytics with Tableau
Join the data science conversation and follow me on Twitter and LinkedIn for more tips, tricks, and tutorials on Python in Tableau and other data analytics topics. If you’re looking to master Tableau, don’t forget to preorder your copy of my upcoming book, Visual Analytics with Tableau (Amazon). It offers an in-depth exploration of data visualization techniques and best practices.
Until now the sentiment package for R only worked with English text. Today, I released version 1.0 of the sentiment package on GitHub that features multi-language support. In order to perform sentiment analysis with German text, just add the parameter language="german" as shown in this example:
Demo showing Multi-Language Sentiment Analysis with English and German text
The new code allows you to add any language. So far, I started to prepare German sentiment files. French and Spanish sentiment files are on my to-do list.
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