Lectures

Lectures

Lectures Held on a Regular Basis

Please click here to find the lectures that are held on a regular basis. This information can be used to create a study plan. However, this information is tentative, i.e. always check the lectures of the current and next semester to keep the study plan up to date.

Lectures of the Current Semester

Module details on 'Basic Topics of Data Science - Basic Topics of Data Science'

CategoryData & Information
TypeLecture
SiteHannover
LecturerProf. Dr. Nejdl, Wolfgang (Hannover)
Module Exam ID2093
ECTS-Credits5
Weekly CompositionLecture 2LE + 1/2E
Required Hours of Work (presence / self-study)125 (42 / 83)
Semesterperiodically, according to student demand and staff specialisms
Teaching MethodsLectures and exercises.
Module DescriptionLearning from data in order to gain useful predictions and insights is an important task, covered under the data science umbrella. This involves skills and knowledge from a wide variety of fields such as statistics, artificial intelligence, effective visualization, as well as efficient (big) data engineering, processing and storage. Given data arising from some real-world phenomenon, how does one analyze that data so as to understand that phenomenon?
Module OutcomesThis course teaches critical concepts and practical skills in computer programming and statistical inference, in conjunction with hands-on analysis of datasets, involving issues such as data cleaning; sampling; data management to be able to access big data quickly and reliably; exploratory data analysis to generate hypotheses and intuition; prediction based on statistical methods such as regression and classification; and communication of results through visualization.
Recommended Literature- R for Data Science (by Garrett Grolemund and Hadley Wickham) O’Reilly Media - Statistics in a Nutshell, 2nd Edition, A Desktop Quick Reference, Sarah Boslaugh, O’Reilly Media - Doing Data Science – Straight Talk from the Frontline, Cathy O’Neil, Rachel Schutt, O’Reilly Media - Statistical inference for data science (https://leanpub.com/LittleInferenceBook)
PrerequisitesNone
ExamWritten exam, graded (90 Minutes)

Available Course Modes

In the following document you can get an overview about the available course modes that are offered in the ITIS Master's program: Course Modes