It’s My 10 Year Blogging Anniversary!

Photo from an early blog post: 2007 Hampi, a temple town in South India recognised as UNESCO World Heritage Site
Photo from an early blog post: 2007 Hampi, a temple town in South India recognized as UNESCO World Heritage Site (Flickr)

Woohoo, it’s already ten years since I started this blog. Can’t believe it! Thanks to all of those who read my posts, and who encouraged and inspired me. Without you blogging would be only half the fun! Now, let’s have a little recap…

2007-2009 SAP and India:

It all started in 2007. I was studying Computer Science, and decided to go for an internship abroad. China and India were on my short list. I decided for India, applied for a scholarship and asked some companies for interesting project work. Before starting the adventure, I published my very first blog post to keep family and friends in loop.

For the next seven month, I lived in Bangalore, and worked for SAP Labs India to develop prototypes for mobile BI apps. I spent plenty of weekends to explore India and surrounding countries. After returning from India, I continued to work for SAP at their headquarters while finishing my degree in Karlsruhe.

2009-2012 CERN:

CERN, surrounded by snow-capped mountains and Lake Geneva, grabbed my attention during the end of my studies. CERN has tons of data: some petabytes! Challange accepted. CERN is known for its particle accelerator Large Hadron Collider (LHC). We applied machine learning to identify new correlations between variables (LHC data and external data) that were not previously connected.

2012-2015 Capgemini and MBA:

Back in Germany, I wanted to bring Big Data Analytics to companies. To one company? No, to many companies! So instead of getting hired as Head of BI for an SME, I started to work for Capgemini. I had fantastic projects, designed data-driven usecases for the financial sector, and gave advice for digital transformation inititives.

In order to keep in balance with all the project work, I dedicated many of my weekend for studies and got enrolled in Frankfurt School’s Executive MBA programme. During my studies, I focused on Emerging Markets and visited a module at CEIBS in Shanghai.

2015-201? Tableau and Futura:

I knew Tableau from my time as consultant. It is an awesome company with a great product and a mission: help people see and understand their data. That’s me! I joined Tableau to help organizations through the transition from classic BI factories to modern self-service analytics by developing data strategies, so that data can be treated as a corporate asset. This includes education, evangelism and establishing a data-driven culture.

In the evenings I’m working for Futura Analytics, a fintech startup, which I co-founded in 2017. Futura Analytics offers real-time information discovery, and transforms data from social media and other public sources into actionable signals.

What’s next?

Currently I’m looking forward to give my Data Strategy talk on TC17 accompanied by a TensorFlow demo scenario. I’m also learning Mandarin, the predominant language of business, politics, and media in China and Taiwan, for quite a while. Let’s see if that is going to influence my next steps… 🙂

Tableau Conference TC17 Sneak Peek: Integrating Julia for Advanced Analytics

Using Julia within calculations in Tableau (click to enlarge)
Using Julia calculations within Tableau (click to enlarge)

We have already seen some love from Tableau for R and Python, boosting Tableau’s Advanced Analytics capabilities.

So what is the next big thing for our Data Science Rockstars? Julia!

Who is Julia?

JuliaJulia logo is a high-level dynamic programming language introduced in 2012. Designed to address the needs of high-performance numerical analysis its syntax is very similar to MATLAB. If you are used to MATLAB, you will be very quick to get on track with Julia.

Compared to R and Python, Julia is significantly faster (close to C and FORTRAN, see benchmark). Based on Tableau’s R integration, Julia is a fantastic addition to Tableau’s Advanced Analytics stack and to your data science toolbox.

Where can I learn more?

Do you want to learn more about Advanced Analytics and how to leverage Tableau with R, Python and Julia? Meet me at the 2017 Tableau Conferences in London, Berlin or Las Vegas and join my Advanced Analytics sessions:

Will there be an online tutorial?

Yes, of course! I published tutorials for R and Python on this blog. And I will also publish a Julia tutorial soon. Feel free to follow me on Twitter @xlth, and leave me your feedback/suggestions in the comment section below.

Further reading: Mastering Julia

A German translation of this post is published on the official Tableau blog: Tableau Conference On Tour Sneak Peek: Julia-Integration für Advanced Analytics

Price and Sentiment Analysis: Why is Bitcoin Going Down?

Bitcoin Price and Sentiment Analysis with variable Moving Average: click to open interactive Tableau dashboard with annotations
Bitcoin Price and Sentiment Analysis with variable Moving Average: click to open interactive Tableau dashboard with annotations

Bitcoin has become one of the trendy investment assets in the recent years. Whenever bitcoin prices approach historical highs, every investor should watch the currency closely. Bitcoin rallied by more than 20% in the first days of 2017, crossing the $1000 mark for the first time since November 2013.

As many experienced bitcoin traders will remember, the first $1000 peak was a case of obvious over exuberance. Bitcoin was hot, plenty of money was pouring into it. Bitcoin investors got too excited, causing a price surge. Prices then rebounded and suffered a long-term collapse shortly after.

Moving Average Convergence/Divergence Indicator

Many traders rely on a Moving Average Convergence/Divergence (MACD) indicator. The MACD is a measure of the convergence and divergence between two EMAs (usually 12 and 26 days) and is calculated by subtracting the two of them. The signal line is constructed by creating an EMA (usually 10 days) of the signal line.

The signal line crossing the MACD from above is a buy signal. The signal line crossing the MACD from below is a sell signal. Relying only on momentum-based indicators (such as the MACD) and optimization-based models, however, will most certainly fail to indicate heavy price drops, as the drop in late 2016.

Predicting Fear with Sentiment Analysis

In late 2016 a lot of people began to pour money into bitcoin again. This time because they were worried that stock markets and other assets were due for a drop. For investors, it is essential to figure out whether or not these fears are actually founded. However, such “safe assets” are prone to suffering from bubbles. People get scared, get invested into gold, or bitcoin, then realize that their fears were unfounded. As a result bitcoin prices could plummet.

So how to catch emotions such as fear in advance? Twitter is a valuable source of information and emotion. It certainly influences the stock market and can help to predict the market. Sentiment analysis can lead price movements by up to two days. Negative sentiment, however, is reflected in the market much more than positive sentiment. This is probably because most people tweet positive things about bitcoins most of the time. Even more positive news occurred after breaking the $1000 barrier.

This content is part of the session “Price and Sentiment Analysis: Why is Bitcoin Going Down?” that I deliver at the Frankfurt Bitcoin Colloquium. Have a look on my upcoming sessions!

[Update 14 Jun 2017]: Axis for Moving Average adjusted. Relative Date selector added with last 6 month as default. Screenshot updated.

Feel free to share the Bitcoin Price and Sentiment Analysis dashboard, which is also featured as Viz of the Day on Tableau Public:

Monkey Business: Always be Ready to Demo

The famous Tableau Superstore demo data set
The famous Tableau Superstore demo data set

Usually, I really don’t like looking on the screens of other passengers. On this early morning train from Frankfurt to Cologne, however, the screen of my seatmate caught my attention. Where have I seen the logo on his slide deck before? Two coffee sips later, it came to me: it was the logo of Monkey 47, a very delicious gin, distilled in the heart of the Black Forest. So I asked my neighbor: “Is that the Monkey 47 logo?”

He was happy that I recognized his brand and we had a small chat about gin and the Black Forest. Turns out his name is Thomas, and he is the head of Sales and Marketing for Monkey 47. Thomas mentioned that his team is planning a tour to promote Monkey 47 in a number of cities. That sounds similar to what we are doing with the Tableau Cinema Tour, so I showed him our Cinema Tour landing page and explained briefly who we are and what our mission is.

I asked him how he is organizing his data. Thomas revealed that he lives in Excel hell: “spreadsheets with thousands of rows and way too many columns”. This also sounded familiar. I opened up our Superstore.xlsx in Excel and asked: “Do your Excel sheets look like this?” Thomas replied: “Yes!”

Here we go! I drag’n’dropped the file on my Tableau desktop icon and paced through a 7-minute-demo ending with an interactive dashboard. Thomas was flabbergasted. To polish things off, I showed him the interactive Twitter Sentiment Dashboard embedded in my blog. Thomas grabbed his jacket and gave me a business card, saying: “We need Tableau!”

Monkey 47 business card (back side)
Monkey 47 business card (back side)

This story was originally written for Tableau’s EMEA Sales Newsletter. I think it’s a good read for the holidays, and wish you all Merry Christmas!

TabPy Tutorial: Integrating Python with Tableau for Advanced Analytics

TabPy allows Tableau to execute Python code on the fly
TabPy allows Tableau to execute Python code on the fly

In 2013 Tableau introduced the 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 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 is 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.

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 familar 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, a connection to TabPy can be added in Help > Settings and Performance > Manage External Service Connection:

Tableau main menu
Tableau main menu

Set port to 9004:

External Service Connection dialogue
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:

Python script within Tableau
Python script within Tableau

4. Use Calculated Field in Tableau

Now you can use your Python calculation as Calculated Field in your Tableau worksheet:

Tableau workbook featuring a Python calculation
Tableau workbook featuring a Python calculation

Feel free to download the Tableau Packaged Workbook (twbx) here.

[Update 3 Jan 2017]: Translated to Japanese by Tomohiro Iwahashi: Tableau + Python 連携 (Tabpy) を使ってみよう!

[Update 30 Mar 2017]: A German translation of this post is published on the official Tableau blog: TabPy Tutorial: Integration von Python mit Tableau für Advanced Analytics