The optimal stopping is one of the most important topics in pure (theoretical) mathematics with many impacts in real life. It gives you an optimal pattern of how to approach many situations. It includes hiring a new employee, renting a flat, selling a house, finding your next love or just searching for a parking place. Despite the horrendous name, the underpinning logic is relatively simple.

One of the traditional arguments on avoiding using ORM is the lack of performance. Although it is generally speaking correct, there are many ways to write queries in your application. Some of them are better (faster and more efficient) than others. There is almost always a way to significantly increase the performance of an application without avoiding the ORM approach. We anticipate some preliminary knowledge of Django and SQL here (so make sure you understand basic concepts before continuing).

One of the common challenges when dealing with big multidimensional data sets is how effectively store them. There are many problems to deal with, but maybe the most important is choosing the proper dimension order. Although typically, the selection of the optimal dimensional order does not impact the size of the output file (if you use some compression situation that may differ), it has a profound effect on the response time when accessing stored data.

Virtual environments are a beneficial concept that makes your coding much more accessible. There are many tools in Python for creating and managing virtual environments. This article analyses the native virtual environment created using the venv command on Linux and Windows. Another simplified and more user-friendly approach is to use Anaconda software (using command conda) or using IDE like PyCharm directly.

Frameworks for organizing work - you cannot avoid it whenever you decide to manage a project in IT. What is the most effective way for organizing the work of your team? Probably none of the issues related to project management is covered by so many posts, videos on YouTube, articles in scientific journals. This article is about one concrete human perspective on project organization. It can help you decide how to run your project, consider available frameworks (particularly Waterfall, Agile and SCRUM frameworks), and present management tools.

One of the common challenges of on-demand large datasets processing is the time required for data processing. The naive approach computes operation results on the same component as serves results (and horizontally scale the system when overloaded). The more complex (and generally speaking the only correct) approach is to use some asynchronous task queue based on distributed message passing (message broker). Another option is to stream data continuously as they come.

Code profiling is one of the most important parts of the code optimisation process. Generally speaking, it is the program analysis where we are mainly interested in memory usage and time complexity of the program (or each of its parts). We can also be interested in how often we use some function (or how often we call it, what is the latency). There are a lot of principles and tools to perform these tasks. This article is mainly focused on stack tracing, line-by-line code analysis tools, and function call graphs.