Bachelor
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DevOps
Type:
Elective course (Applied Mathematics and Information Science)
Area of studies:
Applied Mathematics and Information Science
Delivered by:
Big Data and Information Retrieval School
Where:
Faculty of Computer Science
When:
4 year, 3 module
Mode of studies:
distance learning
Online hours:
32
Open to:
students of all HSE University campuses
Instructors:
Gennadii Porosenkov
Language:
English
ECTS credits:
5
Contact hours:
14
Course Syllabus
Abstract
DevOps is a set of software development practices that combine software development (Dev) and information-technology operations (Ops) to shorten the systems development life cycle, while frequently delivering features, fixes, and updates in close alignment with the given business objectives. Graduates often lack practical skills and experience required for professional success in the IT industry. The DevOps course will give you an opportunity to develop and polish relevant skills needed for large-scale complex projects, including system design, system deployment, support, version control systems, virtualization, etc. This valuable hands-on experience will allow you to start working on your own industrial-level projects and effectively collaborate with your team members if you are hired by an IT company. During the course, you will have an opportunity to solve many practical problems focused on various aspects of a product life cycle.
In cooperation with МТС.Тета and МТС Cloud.
Expected Learning Outcomes
- To know how to deploy cloud services
- To understand Git and build automation systems
- To understand the concept of CI / CD
Course Contents
- Introduction
- Virtualization and Cloud
- IaC and Configuration Management
- Containerization
- CI/CD
Assessment Elements
- Test 15 questions with answers based on the materials of the lecture. Held at the 2nd lecture.
- Test 25 questions with answers based on the materials of the lecture. Held at the 3d lecture.
- Test 35 questions with answers based on the materials of the lecture. Held at the 4th lecture.
- Test 45 questions with answers based on the materials of the lecture. Held at the 5th lecture.
- Test 55 questions with answers based on the materials of the lecture. Held at the 6th lecture.
- Test 65 questions with answers based on the materials of the lecture. Held at the 7th lecture.
- HWIssued after the 7th lecture.
- Mini-quiz
- Practical task
Interim Assessment
- 2022/2023 3rd module0.1 * Test 3 + 0.2 * Practical task + 0.1 * Test 5 + 0.2 * Mini-quiz + 0.1 * Test 4 + 0.1 * Test 6 + 0.1 * Test 1 + 0.1 * Test 2
Bibliography
Recommended Core Bibliography
- Christopher M. Bishop. (n.d.). Australian National University Pattern Recognition and Machine Learning. Retrieved from http://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=edsbas&AN=edsbas.EBA0C705
Recommended Additional Bibliography
- M Narasimha Murty, & V Susheela Devi. (2015). Introduction To Pattern Recognition And Machine Learning. World Scientific.