### General Information

Course Code | E_EOR1_IDS |
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Credits | 6 EC |

Period | P1+2 |

Course Level | 100 |

Language of Tuition | English |

Faculty | School of Business and Economics |

Course Coordinator | dr. H. Karabiyik |

Examiner | dr. H. Karabiyik |

Teaching Staff |
dr. H. Karabiyik |

### Practical Information

You need to register for this course yourself

Last-minute registration is available for this course.

Teaching Methods | Lecture, Study Group |
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Target audiences

This course is also available as:

### Course Objective

For the data to tell a story, data scientists need to make use ofstatistics. Probability theory provides a foundation and the necessary

language for statistics. The knowledge of concepts of probability theory

can be used as a backbone of many important concepts in data science.

This is why an introduction to data science should essentially contain

elements of probability theory. This course is an elementary

introduction to probability theory for data scientists. The aim is to

gain understanding of the theoretical knowledge with an emphasis on the

mathematical foundation of modeling and gain experience with

applications of this theory.

By the end of this course, participants will:

(1) have detailed knowledge of mathematics of probability theory;

(2) become familiar with the concepts like axioms of probability, random

variables, limit theorems;

(3) understand the bridge between probability theory and practice;

(4) demonstrate a thorough knowledge of the core areas of probability

theory and data science.

### Course Content

This course covers the topics of introductory and elementary probabilitytheory for data scientists and it promises a comprehensive understanding

of theoretical and practical applications of probability theory by

bridging the theory and practice.

In particular upon a brief discussion of combinatorial analysis, the

students will be introduced to axioms of probability and the concepts of

conditional probability and independence. Then, the concept of random

variables including will be discussed. This part will mainly cover

discrete and continuous random variables and jointly distributed random

variables. Next, the concept of expectation in probability theory will

be discussed. This part will include expectations of sums of random

variables, moments, moments generating functions. Finally, students will

be briefly introduced to limit theorems such as central limit theorems

and laws of large numbers.

### Teaching Methods

Lectures and tutorials### Method of Assessment

Intermediate exam – Individual assessmentFinal exam – Individual assessment

Individual assignments - Individual assessment

### Literature

Ross (2013), A first course in probability. Pearson New InternationalEdition, Ninth Edition, Pearson

### Recommended background knowledge

This course presumes that students are familiar with basic mathematicalmethods.