Statistics is the grammar of science and a foundation of advanced AI.
Learn from our holistic, concept-focused statistical-methodological courses designed to build a strong foundation on what it takes to analyse data in a scientifically objective way.
ONLINE COURSES
Causal Inference is the method of choice for impact evaluations, policy evaluations, evaluations of marketing interventions, clinical trials and many more.
Causal Inference is the foundation for analysing cause-and-effect relationships, designing experiments and analysing experimental data.
The courses are designed by following philosophy of experiential learning. The focus is to provide data practitioners (data analysts, data scientists, AI engineers and researchers) with strong foundations on what it takes to analyse data in a scientifically objective way, in particular cause-and-effect relationships. We provide practical advice and scientific references for further independent learning.
Different parts of the material were previously used for lectures, seminars and workshops at scientific conferences, business seminars and universities, such as University of Helsinki, Sigmund Freud University, Uppsala University and Tsinghua University. Some of the courses have been taught to master’s and doctoral students at University of Helsinki.
Statistics lies in presence of ignorance. By definition, ignorance means lack of knowledge, understanding, or information about something. Statistics lies when a person presenting statistical data lacks knowledge, understanding, or information about statistical-methodological techniques which enable one to analyse data in scientifically objective way. Although we live
Learning about foundational concepts of causal inference is crucial for data science because most questions of interest are causal in their nature. Whether we perform impact evaluations, A/B testing, quality control or clinical trials, causal inference is the method of choice. Causal inference is one of the
Statistical thinking is in need of a new approach due to recent developments of the modern statistical science. This new approach puts causal thinking at the heart of the key statistical thinking concepts, which reflects recent developments of modern statistical science in the field of causal inference
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