Welcher Mitarbeiter kündigt als nächstes? - Wie People Analytics den Recruitingbedarf frühzeitig erkennt "Wie können wir vorhersagen, welcher Mitarbeiter als nächstes kündigen wird?" - Eine scheinbar einfache Frage, die meine Studierenden im Mastermodul #DataAnalytics dieses Wintersemester tiefgehend erforscht haben. Durch 📊 Datenexploration, effektive 🖼️ Visualisierungstechniken und den Einsatz vielfältiger 🤖 Vorhersage- und Segmentierungsmodelle haben sie gelernt, dass datenbasierte Analysen oft zuverlässigere Ergebnisse liefern als bloße Intuition. Ein Highlight des Semesters war der heutige #Vortrag von Christian Richter und Alexander Gerhardts , Experten im Bereich 🧑💼 #PeopleAnalytics bei der DHL Group. Sie gaben faszinierende Einblicke, wie dieser spezifische Use Case in der Praxis bei einem der weltweit führenden #Logistikkonzerne umgesetzt wird. Alexander erläuterte eindrucksvoll, wie #DataEngineers Daten für Vorhersagemodelle aufbereiten. Christian verdeutlichte anschließend, wie DHL 🔄 #Turnover Forecasts nutzt, um den #Personalbedarf frühzeitig und präzise zu identifizieren. Ein inspirierender Tag, der die Brücke zwischen akademischem Lernen und realer Anwendung von Data Analytics schlug!
DATA STORIES | JOB MARKET ANALYTICS | KNIME ANALYTICS PLATFORM Job Market Analytics: Findings from the Data Science for Business Challenge at TH Köln My Data Guest — An Interview with TH Köln Challenge Winners
At universities of applied sciences, our teaching focus is coupled with a highly practical business orientation. I’d like to outline how, in my role as professor, this influences how I teach data science to master students in business administration. Tobias Schlüter is Professor at TH Köln in the Faculty of Business, Economics and Law. His teaching disciplines include data science for business, and applied analytics in banking while his research concentrates on analytics an data science for business and pricing for financial institutions. My students will work in a quickly developing business world after graduation. More data will be available, while decisions will have to be made ever faster. In topics like marketing, HR, and product management, there is rarely a single correct course of action. Hence my goal is to teach how to best evaluate different approaches. I’m guided by these questions: How can I prepare my students to make good, meaningful, data-based decisions in their future positions, instead of just going with their guts? How can I enable them to become effective managers in their respective domains — ones who both understand the possibilities of more available data and modern data analytics and can also apply it? My Teaching Objectives and why I chose KNIME In the area of tension between specializations in business and data science, three aspects were important for me. The first mainly impacts the structure and design of my lectures, while the other two have led me to adopt KNIME for my lectures. The Do It Yourself Approach and Real Challenges Students have to learn data science practically. Instead of me flipping through PowerPoint slides, they have to work with data and design algorithmic workflows. They should also preferably learn about challenges in a business context, with real data sets, project structures, deadlines, and ideally with managers from the field. Get Productive Fast Students need easy access to tools. Whether they have previous experience with creating analyses or not, with KNIME, I only need about 90 minutes to introduce the basic concepts of the platform and enable them to create their first predictive models or apply a variety of analytics methods. From there they can choose from the available materials to continue their learning. Using an Industry-Relevant Tool Students should be exposed to software that is used by actual companies, to support their transition to working in the industry. In the case of KNIME, they use exactly the same software (with no artificial limitations) that many companies already use, and which all can use. The Data Science Journey in the Master Program Laying the Foundations To provide students with the skills to discern which technique is best to solve the problem at hand, we lay a solid foundation for assessing and evaluating challenges in the first semester course “ Data Analytics .” The initial weeks are regular front-of-class teaching. What opportunities and potentials does Big Data offer? What classes of algorithms are there? What distinguishes descriptive statistics from modern machine learning algorithms? How does the CRISP-DM work? And so on. Applying Lessons to Business Problems Students are then given a seemingly simple problem: The board of directors at your company is concerned about employees quitting. Can you help the board assess which employees are at risk of quitting? Every student immediately has a sense of why they would quit a job. Over the rest of the term, we work with these perceptions to find insights from HR data, and leverage the benefits modern data analytics can offer.
Die FaRis & DAV Symposien