Book Review: Paul D. Ellis, The Essential Guide to Effect Sizes

Reviewed by Thoralf Mildenberger (ZHAW)

  • Paul. D. Ellis, The Essential Guide to Effect Sizes. Statistical Power, Meta-Analysis and the Interpretation of Research Results. Cambridge University Press, Cambridge 2010. Link to book on publisher’s website.

In the last few years, statistical hypothesis testing – with the p-value still being THE standard for reporting results in many fields of science – has increasingly been criticized. Many researchers have even called for abandoning the “NHST” (Null Hypothesis Significance Testing) approach all together. I think this is going too far as many problems are due to misapplication of the techniques and – perhaps even more importantly – misinterpretation of the results. There is also no consensus on how to replace hypothesis testing with a better methodology – some of the more moderate critics suggest using confidence intervals, but while these are often more informative they are essentially equivalent to hypothesis tests and share some of the problems. This makes it all the more important to highlight difficulties in the correct application and interpretation of statistical methodology. Continue reading

Study on “Quantified Self” Published: Links to Book and Summary

By Kurt Stockinger (ZHAW)

The final results of an interdisciplinary study funded by „TA Swiss“ on „Quantified Self“ with participation of the Datalab have been published. The study was performed by three ZHAW departments (School of Health Professions, School of Management and Law, School of Engineering) in cooperation with the Institute for Futures Studies and Technology Assessment, Berlin. The focus of the Datalab was on legal and Big Data aspects of quantified self.

The results are available in various forms:

Enjoy reading and maybe you get encouraged to “quantify yourself” a bit better 😉

Artificial Intelligence in Industry and Finance

2nd European COST Conference on Mathematics for Industry in Switzerland
September 7, 2017
Zurich University of Applied Sciences,
Technikumstr. 71, 8400 Winterthur

By Jörg Osterrieder (ZHAW)

Below please find a short recap and an outlook for our next conference on September 6, 2018.

Aim of the conference

The aim of this conference was to bring together European academics, young researchers, students and industrial practitioners to discuss the application of Artificial Intelligence to various practical fields. In a broader context, we wanted to promote «Mathematics for Industry» in Switzerland, as part of the European COST (Cooperation in Science and Technology) Action “Mathematics for Industry”, where members of ZHAW are in the management committee for Switzerland. Continue reading

R: Reduce() Part 2 – some pitfalls using Reduce

By Matthias Templ (ZHAW), Thoralf Mildenberger (ZHAW)

By way of example, functionality of Reduce() is shown in in . It’s great to learn about how to use this function on interesting problems. If you are ready (equals if you read the first blog post on Reduce), we want to push you further on writing efficient code. Continue reading

R: Reduce() – apply’s lesser known brother

By Thoralf Mildenberger (ZHAW)

Everybody who knows a bit about R knows that in general loops are said to be evil and should be avoided, both for efficiency reasons and code readability, although one could argue about both.

The usual advice is to use vector operations and apply() and its relatives. sapply(), vapply() and lapply() work by applying a function on each element of a vector or list and return a vector, matrix, array or list of the results. apply() applies a function on one of the dimensions of a matrix or array and returns a vector, matrix or array. These are very useful, but they only work if the function to be applied to the data can be applied to each element independently of each other.

There are cases, however, where we would still use a for loop because the result of applying our operation to an element of the list depends on the results for the previous elements. The R base package provides a function Reduce(), which can come in handy here. Of course it is inspired by functional programming, and actually does something similar to the Reduce step in MapReduce, although it is not inteded for big data applications. Since it seems to be little known even to long-time R users, we will look at a few examples in this post. Continue reading

Conference review of SDS|2017, Kursaal Bern, June 16

by Thilo Stadelmann (ZHAW)

In 2014, ZHAW Datalab started the SDS conference series. It was the year with only one Swiss data scientist identifiable on LinkedIn (at Postfinance…). The year where we talked about “Big Data”, and not “Digitization”. The year where we were unsure if such a thing as a Swiss data science community would exist, and if it actually would come to such an event.

SDS grew from a local workshop to a conference with over 200 participants and international experts as keynote speakers in 2016. This was the year where finally a Swiss-wide network of strong partners form academia and industry emerged to push innovation in data-driven value creation: the Swiss Alliance for Data-Intensive Services ( We as datalabbers have been instrumental in founding this alliance, and then found it to be the perfect partner to take this event to the next level of professionalism.

Continue reading

Who is scared of the big bad robot?

By Lukas Tuggener (ZHAW)

Reposted from

There currently is much talk about the recent developments of AI and how it is going to affect the way we live our lives. While fears of super intelligent robots, which want to end all human live on earth, are mostly held by laymen. There are other concerns, however, which are also very common amongst insiders of the field. Most AI experts agree that the short to midterm impact of AI developments will mostly revolve around automating complex tasks, rather than “artificially intelligent beings”. The well-known AI researcher Andrew Ng put it this way: Continue reading

OpenAI Gym environment for Modelica models

By Gabriel Eyyi (ZHAW)

In this blog post I will show how to combine dynamic models from Modelica with reinforcement learning.

As part of one of my master projects a software environment was developed to examine reinforcement learning algorithms on existing dynamic models from Modelica in order to solve control tasks. Modelica is a non-proprietary, object-oriented, equation based language to conveniently model complex physical systems [1].

The result is the Python library Dymola Reinforcement Learning (dymrl) which allows you to explore reinforcement learning algorithms for dynamical systems.

The code of this project can be found at github.

Continue reading

Review of SDS|2016

By Amrita Prasad (ZHAW)

It’s already been a month since we met as the Swiss Data Science community at our 3rd Swiss Conference on Data Science (SDS|2016), pushed again by ZHAW’s Datalab group and presented by SAP Switzerland.

Several additional organisations sponsored and supported the conference to give it a successful execution – the organising committee thanks IT Logix & Microsoft, PwC, Google, Zühlke, SGAICO, Hasler Stiftung and the Swiss Alliance for Data-Intensive Services for their support in bringing together a successful event! Continue reading

Data scientists type A & B? Nonsense.

By Thilo Stadelmann (ZHAW)

Reposted from

I recently came about the notion of “type A” and “type B” data scientists. While the “type A” is basically a trained statistician that has broadened his field towards modern use cases (“data science for people”), the same is true for “type B” (B for “build”, “data science for software”) that has his roots in programming and contributes stronger to code and systems in the backend.

Frankly, I haven’t come about a practically more useless distinction since the inception of the term “data science”. Data science is the name for a new discipline that is in itself interdisciplinary [see e.g. here – but beware of German text]. The whole point of interdisciplinarity, and by extension of data science, is for proponent to think outside the box of his or her original discipline (which might be be statistics, computer science, physics, economics or something completely different), and acquire skills in the neighboring disciplines in order to tackle problems outside of intellectual silos. Encouraging practitioners to stay in their silos, as this A/B typology suggests, is counterproductive at best, fatal at worst. Continue reading