“Data science combines mathematical methods, algorithms and programming to extract knowledge and insights from digital data. In this course, students learn useful methods and tools for distilling raw data into useful information. The course also provides an introduction to the field of data science and its many industrial and scientific applications.”
The course will begin on 30th October 2017.
After the course, you can describe how natural data such as images, speech and time series measurements can be represented as data in digital form. You can apply elementary statistical and algorithmic methods to process the digital data to yield insights to the data generating phenomenon. You will understand what processes constitute the data science pipeline in the analysis, starting from natural data and ending with actionable results.
The course serves as an introduction to the topic of data science and related topics such as machine learning. You will be introduced to data science methods and tools to find interesting information from data. Specific topics on the course include processing of digital signals such as speech and images, statistical estimation of parametric distributions, classification, prediction, clustering, pattern mining and network analysis for developing search engines for hypertext collections such as the Web.
Lectures 20h, exercise sessions 20h, independent work 90h, examination 3h
Assessment methods and criteria
Overall grade is determined by the exam grade. Attendance in the exercise sessions will earn the student extra exam points.
Skills needed on the course are taught on introductory courses in mathematics and statistics, programming. Specifically, matrix algebra, derivatives of functions, and statistical distributions will be needed on the course.
Material will be announced on the course pages
CS-C3110 Datasta tietoon (From Data to Knowledge)
Language of Instruction