Develop foundational knowledge to analyse data in 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 a design and analysis of experiments.
The courses are designed by following philosophy of experiential learning. The focus is to provide data practitioners (data analysts, data scientists and researchers) with foundations on designing studies and analysing data in scientifically objective way. 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.
Dr. Ana Kolar holds a PhD in Statistics with expertise in Causal inference and she is the creator of a new approach for developing modern statistical thinking skills. Ana is able to introduce complex topics in a simple manner and she is dedicated to an experiential educational approach. She holds a postgraduate qualification in tertiary teaching and learning and has over 20 years of international professional experience in the fields of academia, applied statistics and research. Read more about Ana here.
We warmly invite data scientists, data analysts, researchers, journalists and business leaders to learn about methods and techniques of modern statistical science that enable us to analyse data in scientifically objective way.
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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