KDDC 2EN22 - Data analysis: a technical and strategic handbook

Students will learn the various steps of data analysis (descriptive, diagnostic, predictive and prescriptive), rationally choose charts and visuals, generate data points and metrics that contribute to a strong article, memo, report, conference speech or decision making. The main goals of the course are to clarify how data can best support a structured decision-making process and to sharpen the critical reading of data-based lines of argument.

Analytical objectives will include: how to avoid biases and misleading interpretations; how to make a complex reality easier to understand.

Technical aspects will cover : data sourcing, cleaning and preparation; choice of visualization softwares; selection of the best visuals; geographical data and mapping; use of AI (opportunities and risks).

The course will be based on case studies from various regions in the world. Sectors studied will cover Climate Change, Economic Forecasting, Financial Markets, Epidemics. Examples will be taken from use cases in journalism, financial analysis, policy making, academic research.

No pre-requesite except curiosity and analytical mindset. This course is not about how to manipulate data software and do the fundamental “technical” data science work. It will leverage on and complement technical courses by adding a practitioner and critical thinking on all the steps involved in the data analysis. Knowledge of basic statistics and data software (Power BI, Tableau etc.) will be a plus, but reminders will be provided.

Mix of presentations, case studies (some done in classroom and some prepared by students as home work; all groups prepare on different subjects of their choice from a list). Analysis and comments of documents (written and video). Active class participation is strongly encouraged.

• Quick Multi-Choice Questionnaires on readings and previous session in each class 20%
• 1 focused case study memo/project (groups of 4 students, 40%),
• Final MCQ 40%

6 seminars of two hours each (12 hours). Some preparation is required before each class: brief reading, short literature review notes and preparation for the case studies. Most of the theoretical work will be done by short reading before the class. Class time will be prominently spent on case studies and examples.