Statistical Online Courses for Data Science and Analytics
Develop foundational statistical knowledge to analyse data in scientifically objective way.
Modern Statistical Thinking for Data Science and Analytics
A foundation to analyse data in a scientifically objective way
Causal Inference for Data Science and Analytics
Foundations, assumptions and applications
Data Strategy, Analytics and AI
Which questions to ask and how to think to get to objective information
This course is about understanding trustworthiness of information produced by AI systems and the impact that quality of data has on credibility of advanced AI systems.
Causal Inference with Observational Data for Impact Evaluations
Data requirements, methods and techniques
Statistical Thinking for Journalists
How to read data and analyse it in scientifically objective way
In-Person Courses and Workshops
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Decision making 8.0 for Management
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Decision making 7.0 for Leaders
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Decision making 7.0 for Analytics
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Statistical Thinking for Journalists
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Statistical Thinking for Scholars and Educators
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Causal Effect Analysis
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Impact Evaluations
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Missing Data
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READING CORNER
Do you wonder how statistics lies?
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
Data science without causal inference is like a fish without water
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
A New Approach To Statistical Thinking
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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