In this course, we systematically cover fundamentals of statistical inference and testing, and give an introduction to statistical modelling. The first half of the course will be focused on the fundamentals of statistical inference such as sampling procedures, probability theory, and random variables. In the beginning, we also provide a gentle introduction to the [R] language for statistical computing, which will be used throughout the course to show how theoretical concepts can be applied in practice. In the second half of the course, we will deal with the estimation and testing of population characteristics based on sample data. Furthermore, we provide an introduction to statistical modelling via introductory lectures on (generalized) linear regression models and briefly discuss the Bayesian approach to statistics. Throughout the course, real-data examples will be used in lecture discussions and exercises. This course lays the foundation, preparing students for other courses in machine learning, data mining, and visualization.|
At the end of this course, the student
- understands important statistical concepts that form the basis of data analytic methods commonly used in the area of data science,
- is able to apply these statistical concepts to solve real-world problems,
- is able to carry out the necessary calculations in the [R] language for statistical computing.
The course is based on the book Statistics for Data Scientists by Kaptein and van den Heuvel. A PDF version of the book will be provided on Canvas. The book (and hence the course) covers the following topics:
- Descriptive statistics and visualizing data
- Sampling methods and evaluation of sampling plans
- Probability theory
- Random variables and distributions
- Estimation of population characteristics
- Multiple variables and joint distributions
- Bootstrapping and null-hypothesis testing
- Linear regression and logistic regression
- Bayesian statistics
This course is open for students enrolled in the pre-master's program Data Science and Entrepreneurship only.
The courses from the pre-master's program Data Science and Entrepreneurship require specific prior knowledge. It is only possible to participate in this course if approved by the admission committee and if you are enrolled for the pre-master's program.
Please note that this course will be taught in Mariënburg, 's-Hertogenbosch (JADS).