Fundamentals of Data Science
Description: The course introduces master students to the basic concepts and methods of data analysis and machine learning. Data Science (DS) is a new, exponentially-growing field, which consists of a set of tools and techniques used to extract useful information from data. Data Science is an interdisciplinary, problem-solving oriented subject that learns to apply scientific techniques to practical problems. The course orients on practical classes and self-study during preparation of datasets and programming of data analysis tasks. The course content includes methods and tools for collecting, storing, processing and visualizing data, methods and techniques for extracting and processing data from the Internet and various file.
Amount of credits: 5
Пререквизиты:
- Database Design
Course Workload:
| Types of classes | hours |
|---|---|
| Lectures | 15 |
| Practical works | |
| Laboratory works | 30 |
| SAWTG (Student Autonomous Work under Teacher Guidance) | 30 |
| SAW (Student autonomous work) | 75 |
| Form of final control | Exam |
| Final assessment method |
Component: Component by selection
Cycle: Base disciplines
Goal
- The main result of this discipline is the preparation of undergraduates to perform practical work on the Data Science.
Objective
- To develop applied experience with data science software, programming, applications and processes
- To develop practical data analysis skills, which can be applied to practical problems
- To develop practical skills needed in modern analytics
Learning outcome: knowledge and understanding
- to know the basic concepts and methods of Data Science, including the stages of working with data, the basics of statistics, machine learning and data visualization
Learning outcome: applying knowledge and understanding
- Be able to translate a real-world problem into mathematical terms.
Learning outcome: formation of judgments
- Be able to formulate the problem of knowledge extraction as combinations of data filtration, analysis and exploration methods
Learning outcome: learning skills or learning abilities
- Learn to develop complex analytical reasoning
Teaching methods
Technology of research activities
Communication technologies (discussions, press conference, brainstorming, educational debates, etc.)
Information and communication (including remote) technologies
Key reading
- D.Cielen, A.. Meysman, Mohamed Ali Introducing Data Science. Big Data, Machine Learning, and more, using Python tools. Manning Publications Co, 2016, p.322
Further reading
- Han, J., Kamber, M., Pei, J. Data mining concepts and techniques. Morgan Kaufmann, 2011.
