Introduction to Data Science

2019-2020

Course Objective

For the data to tell a story, data scientists need to make use of
statistics. 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 probability
theory 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 assessment
Final exam – Individual assessment
Individual assignments - Individual assessment

Literature

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

Recommended background knowledge

This course presumes that students are familiar with basic mathematical
methods.

General Information

Course Code E_EOR1_IDS
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
Target audiences

This course is also available as: