What is Statistical Thinking?
Statistical thinking is a thought process that enables an objective perception of a subject matter. It provides our minds with a thinking methodology that is founded on the key statistical concepts. It empowers the process of turning data in to reliable and objective information.
A ‘human mind data analysis’ versus a ‘statistical data analysis’
Human minds work according to the evidence-based principle – information and data our minds are exposed to are the foundation for our minds’ decision-making process. Our minds collect information, analyse it and form conclusions. A statistical data analysis does it similarly, but with one important difference: the process of collecting data, analysing and forming conclusions is done according to the methodology that assures objectivity and supports the notion of finding the truth. Statistical thinking is the crucial part of this methodology.
How do we help you to develop Statistical Thinking skills?
We introduce you the processes and the thinking that is used in statistical data analysis. We provide you with an experience that enables you to develop not only statistical thinking, but also a profound understanding of the key data analytics concepts.
What are the key objectives of the Statistical Thinking courses for Data Scientists & Data Analysts?
- To be introduced to the thinking methodology behind statistical thinking and to become familiar with its applications at different levels of data analysis processes and conclusion making
- To develop a profound understanding of the key and more advanced statistical concepts
- To develop an understanding about the impacts that different types of data have on selection of data analysis techniques and methodologies
- To be introduced to causal thinking concepts and to explore the impacts that causal thinking has on data analysis procedures – from the perspective of data selection, analysis techniques and formation of concluding insights
- To polish the skills of ‘objective critical thinking’ by consolidating statistical and causal thinking, and by introducing the impact that ‘objective critical thinking’ has on the design of data analysis and formation of conclusions
Short outline of statistical and data analysis concepts covered in courses
- The ‘thinking mechanism’ behind statistical thinking
- Understanding how different types of data and data analysis designs impact reliability and objectivity of data analysis results
- Statistical thinking for data quality evaluation – understanding origins of data and the framework of available sample data
- Missing data – its implications and solutions in the data analysis process
- Exploratory data analysis – pitfalls and advantages in deriving objective insights
- Inferential statistics and its challenges in deriving trustworthy insights
- Causal effect analysis and causal thinking
- Understanding experiments – design, analysis and reliability of obtained results
- Understanding surveys – design, analysis and reliability of obtained results
- Different types of regression analysis – its applications, statistical thinking, advantages and pitfalls
- Different types of inferential statistics – its applications, statistical thinking, advantages and pitfalls
Detailed descriptions of the courses are provided in the above tabs: ‘Course 1’, ‘Course 2’.
Who should attend?
- Customer data analysts, Human behaviour analysts, Human resource analysts, Quality control analysts, etc.
- Data scientists with little or no official statistical training (no degree in statistics, self-educated in statistics, statistics as a minor in an undergraduate degree) or those interested in exploring the art of statistical and causal thinking
- Those interested in expanding their understanding and knowledge about design and analysis of experiments and becoming familiar with advanced approaches in experimental studies
- Those interested in implications of regression analysis approaches
The course is organised in an interactive way and comprises a mixture of presentations, discussions and team work. A deep-learning and an experiential teaching approaches are used. Participants’ thinking is challenged by a number of real world data analysis examples. The interaction among participants and instructor is important, thus, each course is limited to 25 participants.
About the instructor
Dr. Ana Kolar has significant experience in teaching at university level. She is able to introduce and explain complex topics in a simple manner and she strives towards an educational approach that is based on experiential learning. Ana is the creator of this new approach for developing statistical thinking skills. Read more about Ana here.