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Looking for more information? Would you like to get in touch? We’d love to hear from you. For those who are eager to learn more, some of the most frequent questions we receive are listed below.

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FAQ

Do you only use Python?

No, the name Python Predictions originates from the Greek mythology, where the Greek god Python resided at the Oracle at Delphi. Hence, it was the first Greek god capable of predicting the future. We chose this name in 2005 for that reason only.

Python, as a coding language, has become increasingly popular recently, also in our team. But we equally use R, or commercial software packages. In short, we use the solution that makes most sense for a specific client.

So, while we also love Python, the Python in our brand name does not refer to the programming language.

How can we assess the opportunities?

Most clients address us with a specific problem. For example, some companies aim to improve customer retention, others aim to improve new client acquisition, others aim to reduce fraudulent behaviour and others aim to optimise stock levels. However, we have also helped clients with more general questions, such as: ‘Is my company mature to benefit from predictive analytics?’. The time needed to perform such assessments may vary from as little as one meeting to as much as a week. Most often, such questions result in a strategic roadmap for data science projects.

How much internal expertise is required?

While some of our clients have a large team of internal data science experts, we have equally worked with companies that have no previous internal experience. In different settings, we work towards the same goal: offering powerful predictions that can be interpreted and trusted by the business users. This means that we always strive for an optimal solution, but we aim to adapt our communication and presentation to the interests of the user, balancing the focus between managerial and/or technical aspects.

How can we collaborate practically?

Depending on the availability of hardware, software and office space, we can perform our activities at our client’s location or at our offices. Our primary goal is to perform projects from A to Z, i.e. starting from the problem definition to the solution and recommendations. Furthermore, we also offer support and guidance during the implementation of the solution in the customer’s IT infrastructure. An initial data assessment may be useful to identify and quantify opportunities.

We also offer company-specific training and coaching in the areas of data science, prediction and machine learning, and customer segmentation. These courses are adapted towards the (desired) level of expertise and interests of the client.

Do you invest in analytical communities?

Absolutely. Since the launch of Python Predictions, we have invested both time and resources in developing and growing analytical communities. Today, we are most actively collaborating with the AI & Data Science Community of Belgium and DigitYser where we offer training for managers as well as Data Scientists.

Is data science useful for my industry?

Data science is useful across a wide variety of industries and applications. Although retailing, e-tailing, banking, telecommunications and utilities provide some of the most obvious players that currently profit from data science, the methodologies and technologies used are generic and industry-independent. However, we consider an accurate definition of the business question and an understanding of a company’s strategy and tactics crucial to deliver valuable results.

Are you connected to universities & business schools?

Absolutely. We regularly provide introductory sessions about our work at respected Belgian universities such as Ghent University and KU Leuven. And we are contributing to business school programs and workshops at Solvay Business School (Belgium), Vlerick Business School (Belgium), and IÉSEG School of Management (France).

Data science, Machine Learning or Artificial Intelligence?

Data science is all about using algorithms to solve problems. An important number of algorithms in the field are Machine Learning algorithms, a type of algorithms that originated in computer science rather than statistics. Such algorithms provide the intelligence that is key to artificial intelligence – through algorithms we make better, more accurate decisions. And we indeed often outperform human decision making.

How can we stay in touch?

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