In most of the data science applications, it comes very handy to be able to run code on the cloud. Be it a simple demonstration of a functionality that we want to make accessible for a potential client or an end-to-end implementation of let’s say a predictive model, the accessibility of cloud-based solutions is a definitive asset. However, running code on the cloud does have its pitfalls, which can discourage many from taking advantage of it.
This is why I have decided to share our experience with working on the cloud. In this post, I will specifically give a summary of functionalities that can help to run a python script on the Ubuntu cloud.
Running a python script on the cloud, can become much more bothersome than the development on our local computer, especially if we are using a standard SSH connection. Fortunately, to make our lives easier, there are a couple of functionalities that we can use.
1. argparse (python) – to run the script with various input arguments
2. tmux (unix) – to run sessions without the need to have a permanent SSH connection
3. cron (unix) – to run the scripts with a predefined frequency
4. SimpleHTTPs (python) – lightweight webserver for providing access to files to users that don’t have access to our cloud
Driving marketing budget sometimes seems to be a mysterious art where decisions are based on ideas of few enlightened people, who know what’s right. But you should not fool yourself, the times are changing and so is the way successful marketing is managed. The same as in other fields, experienced marketing managers use information hidden in data to help them. With the amount of data and methods available, it is however often tricky not to get lost and be able to distinguish the signal from the noise. Typical examples are the marketing attribution models – a tool that is widely used, but in my experience rarely maximizes the leveraged value of data.
Typically, in marketing attribution, marketers want to know, which part of the business KPI (typically site visits, sales, new customers, new revenues etc.) result from which marketing activity. Mainstream approach is to use attribution models that are often very simplistic – like single source attribution (last click, first click) or fractional attribution (where the contribution is distributed among multiple touch points given some simple rule). These methods provide marketers with the importance of each marketing channel or campaign in respect to their KPI. Based on this historical information the marketing managers make a decision about how to allocate the marketing budget. This approach however puts a great deal of pressure to tedious and demanding data detective work to make sure all client touch points are measured correctly. More importantly, there is no way of knowing that this work has been done correctly, which of course has significant impact on credibility of the attribution models.
Knowing these difficulties, we decided for an alternative approach. We thought: Why should we dig into the individual touch points? Shouldn’t we rather focus on marketing investments and model the ultimate business output? And that is exactly what we did. We took investments into individual marketing channels in time and used time series analysis to predict our client’s business goal (number of sales). On top of it, we also added seasonality, marketing investment of competitors and some other simple parameters.
“Even though we are using data to drive marketing decisions on a daily basis, most of the tools that we have used up until now focus on describing the past. Recently we decided to work together with aLook Analytics to change that. Thanks to their modelling approach to marketing investments we now have accurate information about the expected future developments as well.
Using the interactive Shiny application that is built in Keboola Connection, we want to make informed decisions on the fly, which will help us to reach our sales goals in the most cost efficient way.”
Daniel Gorol, BNP Paribas Personal Finance SA / Cetelem
If there is one thing you learn soon as a data scientist, it is that problem solving gets an extra dimension as the data volume grows. One typical example is building recommendation engines.
A very basic form of a recommendation engine can be built using just a simple matrix algebra. But the situation quickly changes when analyzing data about many thousands customers, who are buying or rating several hundreds of products, which generates large and so called sparse data sets (where a lot of customer-item combinations do not have any value assigned). There are two main problems to overcome – how to store these large sparse matrices and how to run quick calculations over them.
In the following post, I will describe how to approach this problem in R using the package Matrix. The package allows to store large matrices in R’s virtual memory, supports standard matrix operations (transpose, matrix multiplication, element-wise multiplication etc.) and also provides a nice toolkit to develop new custom functions needed for recommendation engines as well as for other applications (here or here) where sparse matrices are used.
Every student of statistics I know has at least once thought about making easy money by predicting the stock market or by predicting sports results. To be honest, I certainly was not an exception. Intimidated by uncapped randomness of the stock market, I always tended more to the second option – the sports betting. Nevertheless, it has not been until recently, that I asked the guys from the team and we decided to actually really try if we can make an easy living from predicting football results. [For impatient readers: it looks promising but it is not so easy]
In the beginning we knew absolutely nothing about how the betting works, where to find the data, neither what some standard prediction methods are. So let me walk you through what we have learned.