This course introduces students to the principles and applications of data analytics in business contexts, with a strong emphasis on marketing use cases. Students will learn how to collect, prepare, analyze, and interpret data using industry-relevant tools such as Excel, Tableau, and KNIME. The course aims to bridge technical analysis with managerial decision-making, enabling students to translate data insights into actionable business strategies.

By the end of the course, students will be able to: Technical & Analytical Skills • Explain foundational concepts of descriptive, predictive, and prescriptive analytics • Apply statistical and computational techniques to business and marketing problems • Analyze datasets using tools such as Excel, Tableau, and KNIME • Perform segmentation, regression, and RFM analysis • Understand and apply basic text analytics techniques Business Application • Interpret analytical results in the context of real business problems • Generate actionable insights that impact business performance • Connect analytical techniques to marketing and broader business strategies Communication Skills • Present analytical findings clearly to non-technical stakeholders • Prepare professional written reports based on data analysis

The course consists of three modules: Module 1 – Descriptive Analytics • Data collection and preparation • Data cleaning and transformation • Data visualization (Excel, Google Sheets, Tableau) • Exploratory data analysis • Basic statistics and A/B testing • Introduction to DataCamp exercises Module 2 – Marketing Models and Predictive Analytics • Customer segmentation (clustering concepts) • Regression analysis • RFM (Recency, Frequency, Monetary) analysis • Introduction to predictive modeling • Dashboarding and storytelling with Tableau • Applications in real business cases Module 3 – Introduction to Text Analytics • Overview of text mining • Sentiment analysis basics • Applications in marketing (reviews, social media) • Tools overview (KNIME, Python-based tools conceptually)

A. Marketing Analytics: Data-Driven Techniques with Microsoft Excel (1st Edition) By Wayne L. Winston. This book is your primary resource for this course. It covers statistical concepts fairly well, with extensively detailed instructions for Excel. • Tableau → Used by companies like Airbnb and Netflix • KNIME → Used by BMW and Siemens • DataCamp → Used for hands-on learning

The course combines: • Lectures (conceptual foundations) • Hands-on labs (Excel, Tableau, KNIME) • DataCamp exercises (guided practice) • Case studies (real-world business problems) • Group project (segmentation + presentation) Emphasis is placed on learning by doing and connecting analysis to business decisions.

Attendant students: • Online Quizzes (2) → 5% • DataCamp Course Completion → 8% • Group Project (Segmentation + Presentation) → 15% • Final Exam (69%) o 50% Computer-based (data analysis tasks) o 50% Written (concepts and interpretation) o Duration: 2 hours Note: The final grade that was computed on a /100 scale will be converted according to a /30 scale. Students cannot reject the grade obtained. Students can only withdraw from the exam before or during the exam. Students must obtain a minimum grade of 60% (100/100, 18/30) on the final exam, regardless of the rest of the grades. Attendance is mandatory: Students must attend at least 70% of sessions to be considered attendant students. Not-complaint/ Exempt students: • Final Exam (100%) o 50% Computer-based (data analysis tasks) o 50% Written (concepts and interpretation) o Duration: 2 hours