Data science is a combination of statistics, software engineering, and domain expertise to gain insight and understanding. Besides that, it is a human activity and should not be confused with artificial intelligence. We define three types of insights that it can provide:
Data science is a robust set of tools to discover new and sometimes surprising insights in large volumes of data. Also, you wouldn’t find them by looking at a straight forward visualization; only the tools in this toolkit can surface these hidden insights.
We use data science in a lot of situations. An everyday use case is the analysis of customer churn. It helps our customers to understand why customers stop doing business with them. With these insights on the table, processes will improve, and customer loyalty will get a boost.
Another use case is the analysis of sick leave. For example, every organization wants to be a place where employees grow and thrive. Understanding why sick leave occurs and fixing the causes that lie within your organization is crucial, especially for labor-intensive organizations.
A third use case is customer segmenting, because we use it to discover new customer segments in large data sets. These new customer segments are typically very focused and can be used to serve customer needs better and to grow your business.
We use a best in class portfolio of toolsets. We evaluate our portfolio continuously, and we add new and promising solutions to the portfolio where we can. Our current portfolio includes:
Data science empowers professionals by delivering insights that traditional analysis will not be able to surface. It drives improvements in customer churn, sick leave, and customer-segmenting.
Changing business through data science and analytics, Alteryx lets everyone in an organization feel the thrill of getting to the answer faster. The new, end-to-end analytics platform empowers analysts and data scientists alike to create, share, and prep data, perform analysis – statistical, predictive, prescriptive, and spatial – and deploy and run analytic models.
Azure Machine Learning can be applied for any type of machine learning, from standard ml to deep learning, supervised, and unsupervised learning. Moreover, whether you prefer to write Python or R code or zero-code/low-code options such as the designer, you can create, shape, and track highly accurate machine learning and deep-learning models.
This language is open-source, interpreted, and high-level language that provides an excellent approach for object-oriented programming. It is one of the best tools used by data scientists for various data science projects/applications. It provides great functionalities to deal with mathematics, statistics, and scientific functions.
ISS-Facility Services want to become the best service provider in the world. To become the best, they want to work with a number of its key-accounts based on a new business model, in which cooperation between partners is central.
Season tickets are one of the most important revenue streams for top-level football clubs. It is therefore in a club’s interest to keep the number of cancellations as low as possible. However, for many clubs it is difficult to estimate who exactly is leaving. In this case study, we show how we used a predictive model to gain insight into potential cancellations at Eredivisie club ADO Den Haag.