Foundations of Data Science in Marketing (GRAV035)

Where are we, and where are we going? These are potentially the most fundamental questions we can ask ourselves, we can ask regarding our company or professional projects, or even what politicians would wonder at the country level. In order to answer those questions, an obvious solution is to measure and track progress, in other words translate the reality numerically. Even though numbers help to bring a sense of objectivity and facilitate comparison they can be equally misleading.
This class will guide you through the fascinating world of data science and will provide you with applied knowledge and tools to make better decisions, prevent misinformation and optimize data communication. This class is composed of four parts. First, we will explore how to define a Key Performance Indicator (KPI) robust to manipulation and bias. Second, we will dive into the analysis of the KPIs by learning and applying a standard approach for Exploratory Data Analysis in order to reduce the risk of statistical bias. Third, we will discover how to assess causality, an essential aspect to track progress and make decisions. Finally, we will cover data story telling and communication.

• Demonstrate knowledge and understanding of the different statistical method for exploratory data analysis, causal inference, statistical and behavioural bias.
• Apply causal inference method, exploratory data analysis method to marketing related questions.
• Determine correct statistical methods for decision-making.
• Interpret the results of statistical analysis and present them in a rigorous, understandable, clear and concise manner.
• Argue the choice of a key performance indicator: strength and weaknesses.