This article describes how to write data from Python into an Excel file and format them. Through automation the content and appearance of the report only need to be programmed once; after that, the report can be created again and again with minimal effort, for example for partial data sets or daily updated data.

In this second part we show an interesting way to combine existing python packages and concepts to tackle some problems of the python programming language. You learn how to create a simple, yet flexible and powerful way to do complex DataFrame validation with Pydantic. With this unit tests for functions that return DataFrames can be reduced, and the data quality of production pipelines can be ensured.

In our introductory article, we explained how discrete choice models can generate insights into customers' decision-making behaviour. In this article we show how to estimate an MNL model using RStan, the R interface of the statistical software Stan.

This is the third part of our series about code performance in R. In the first part, I introduced methods to measure which part of a given code is slow. The second part lists general techniques to make R code faster. In this part you are going to see how to take advantage from parallelization in R.

This is the second part of our series about code performance in R. It contains a lot of approaches to reduce the time your code needs to run. It's useful to know those ideas before starting to write new code, but it also helps to optimize existing code.

In this article, we would like to discuss discrete choice models used in marketing analysis and modeling. Discrete choice models allow to develop a better understanding of the decision-making behavior of customers. This understanding can be used, among other things, to make precise predictions about purchase decisions and to evaluate how customers perceive promotional offers, product messages or brand strategies and assess new products or improved product features.

The map shows the local 7-day-incidence rate of the officially reported Covid-19 infections in Germany over time. The project originated from a master thesis in the Joint Master Studiengang Statistics, in cooperation with the Freie Universität Berlin and INWT Statistics. An advanced algorithm is used to plot national-wide infection cases, revealing more visible patterns and providing higher accuracy compared to district-level incidence data.

Data-driven approaches to maximizing customer relationships are more important than ever in today’s highly saturated and competitive markets. There are many steps a proactive business can take to positively position themselves to keep high-value customers and prevent customer churn.

How to protect your data from one of the most common (and potentially damaging) web security risks. 

Jenkins is currently the leading open source automation server and programmed in Java. It is distributed under the MIT license. Jenkins is absolutely free and very flexible because it allows the use of a wide range of version control systems and offers more than 1,500 plugins. In this blog article we would like to introduce the CI tool Jenkins and the essential aspects of its user interface.

Missing or incomplete data can have a huge negative impact on any data science project. In this blog we explore what kinds of missing data exist, and how we can go about overcoming the challenges they present. 

Business is changing as a result of the increasing quantity and variety of data available. Significant new opportunities can be realized by harnessing the knowledge contained in these data - if you know where to look. A data science team can help to bring raw data through the analysis process and derive insights that are critical in today’s technologically-competitive environment.

When you write code, you’re sure to run into problems from time to time. Here are some advanced tips and tricks for handling these errors, explained accessibly.

Multi-Armed Bandit algorithms are a modern alternative to traditional A/B testing. Similar to Reinforcement Learning, these algorithms can optimize what is shown to the client to maximize rewards while simultaneously determining the most successful option for your business. 

This article describes best practice approaches for developing shiny dashboards. The creation of the dashboard in package form, as well as the use of unit tests should enable the development of robust solutions and guarantee high quality.

In this article we look at how to build a shiny app with clear code, reusable and automatically tested modules. For that, we first go into the package structure and testing a shiny app before we focus on the actual modules.

In this article we present our R package rsync, which serves as an interface between R and the popular Linux command line tool rsync. Rsync allows users of Unix systems to synchronize local and remote files between two locations.

ggCorpIdent is a package for customizing ggplot2 graphics in R easily and without touching the plot code itself. It lets you use custom colors in the plot, which are interpolated if you have not specified as much colors as needed. You can add custom fonts for the text elements within the plot and embed your corporate logo.

In the first part of this blog series, we examined the theoretical foundations of cluster analysis. Now we put the theory into practice using R and find a cluster solution for the mtcars data set. Then the cluster solution is evaluated and interpreted.

This article describes how you can apply a programming technique, called Memoization, to speed up your R code and solve performance bottlenecks.

The speed or run-time of models in R can be a critical factor, especially considering the size and complexity of modern datasets. The number of data points as well as the number of features can easily be in the millions. Even relatively trivial modeling procedures can consume a lot of time, which is critical both for optimization and update of models. An easy way to speed up computations is to use an optimized BLAS (Basic Linear Algebra Subprograms). Especially since R’s default BLAS is well regarded for its stability and portability, not necessarily its speed, this has potential.

All over the world, at the newsstand, in public transport and above all in countless betting communities, football fans are currently asking themselves the question: Who will be the World Champion of the 2018 Football World Cup? Using statistical data science models, we simulated the 2018 FIFA World Cup 10,000 times to determine the probabilities for the next World Cup winner and thus the World Cup favourites. In the following days of the FIFA World Cup, you will find the answer to the question who are the top favourites for the FIFA World Cup here in our blog - daily updated and based on a lot of data and up-to-date statistical analyses.

The motivation for this plot is the function:graphics::smoothScatter, basically a plot of a two dimensional density estimator. In the following I want to reproduce the features with ggplot2.

Sticking to a styleguide helps writing cleaner code and makes working in a team more comfortable. In this article, we present the styleguide we use at INWT – and how you can check your code for deviations from certain style rules.

Which layout of an advertisement leads to more clicks? Would a different color or position of the purchase button lead to a higher conversion rate? Does a special offer really attract more customers – and which of two phrasings would be better? For a long time, people have trusted their gut feeling to answer these questions. Today all these questions could be answered by conducting an A/B test.

This last part is about visualizing the crash location and the flight route with help of the R package leaflet. 

Have you ever tried to find your way around in the file structure of an already existing project? To separate relevant from obsolete files in a historically grown directory? To find out in which order existing scripts should be executed? To make all this easier, it helps to have a consistent file and folder structure across your projects. In this article we present our file structure for R projects to help you get started. 

This article presents the election forecast of INWT for the 2017 elections to the Bundestag. A statistical forecasting model based on the survey results of leading German survey institutes is presented. Unlike the survey institutes we can also use our election forecast to predict the probability of possible coalitions after the election.