Marketing Strategy and Data Analysis with AI

Course overview

Structure of the training program This training combines two courses to provide comprehensive knowledge in the field of AI-supported marketing and data analysis. The modules are designed to build on each other, enabling participants to strategically use AI technologies in marketing and make data-driven decisions. Course 1: Marketing: Marketing Strategy with AI Course Objectives The course provides the fundamentals for the use of Artificial Intelligence (AI) in marketing. It focuses on data-driven navigation of marketing activities and the development of intelligent marketing strategies. Content Introduction to AI in Marketing Basics of data-driven decision-making Target group segmentation with AI Customer Journey and AI-supported approaches Overview of AI tools and technologies in marketing Content planning and personalization through AI Social Media Marketing and AI Campaign planning with AI support Performance marketing and AI analysis Conversion optimization through artificial intelligence Ethics, data protection, and legal aspects in AI marketing Development of an AI-based marketing strategy Success measurement and KPIs in AI marketing Future trends and innovations in AI marketing Learning Objectives Understanding the role of AI in modern marketing Application of AI-based methods for target group analysis and addressing Implementation of AI tools to optimize marketing campaigns Development and evaluation of AI-supported marketing strategies Course 2: AI-supported Data Analysis Course Objectives The course imparts the ability to recognize hidden patterns in data through AI-supported analyses and to gain actionable insights for business strategies. Content My AI Analysis Cockpit with Co-Pilot Data preparation and visualization The AI Code Accelerator: Automating with GitHub Copilot The AI Analysis Pipeline: From raw data to business strategy Basics of machine learning Supervised learning - concepts and applications Your own AI engine: Local AI with Knime & final project Unsupervised learning and clustering Data cleaning Deep learning in data analysis Feature engineering and dimensionality reduction Model evaluation and performance metrics Interpretability and explainability of AI models Ethics and data protection in AI-supported data analysis Use of AI-supported analysis tools and software solutions Practical project: Implementation of an AI-supported data analysis Learning Objectives Application of AI tools for data preparation and visualization Understanding and applying concepts of machine learning Building and evaluating AI models for data analysis Implementation of AI-supported analyses in practical projects Consideration of ethical and data protection aspects in AI analysis Career Prospects The acquired competencies are relevant for activities in marketing, data analysis, and strategic management. Graduates are qualified for tasks that involve the analysis of market data, the design and implementation of AI-supported marketing campaigns,…

Upcoming dates

• 2026-10-11T22:00:00.000Z · 2026-12-03T23:00:00.000Z · Bielefeld · Combined Learning
• 2026-10-11T22:00:00.000Z · 2026-12-03T23:00:00.000Z · Berlin · Combined Learning
• 2026-10-11T22:00:00.000Z · 2027-01-28T23:00:00.000Z · Berlin · Combined Learning
• 2026-10-11T22:00:00.000Z · 2026-12-03T23:00:00.000Z · Dortmund · Combined Learning
• 2026-10-11T22:00:00.000Z · 2027-01-28T23:00:00.000Z · Dortmund · Combined Learning