Statistical Methods for Data Science
About
The course gives an introduction to the theory of probability and statistics, data analysis using descriptive statistics and data visualization, and applications of probabilistic modeling in data science.
In the course, the following broad areas will be covered: data analysis including descriptive statistics and data visualization probability theory including basic probability calculations, random variables, distributions statistical methods including point and interval estimates, hypothesis testing, regression probabilistic models in data science applications, for instance, Naive Bayes classifiers and topic models for text or Hidden Markov Models for sequences
Prerequisites and selection
Entry requirements
To be eligible to the course, the student should have a Bachelor?s degree in any subject, or have successfully completed 90 credits of studies in computer science, software engineering, or equivalent. Specifically, the course requires the following: at least 15 credits of successfully completed courses in programming, one of the courses DIT852 Introduction to Data Science or equivalent, or DIT856 Applied Mathematical Thinking or equivalent. Alternatively at least 15 credits of mathematics or mathematical statistics. Applicants must prove knowledge of English: English 6/English B or the equivalent level of an internationally recognized test, for example TOEFL, IELTS.
Selection
Selection is based upon the number of credits from previous university studies, maximum 165 credits.
