# Computational Statistics (Sommersemester 2024)

Lecturer: Prof. Dr. Bernhard Renard (Data Analytics and Computational Statistics) , Susanne Ibing (Data Analytics and Computational Statistics)

## General Information

• Weekly Hours: 4
• Credits: 6
• Examination time §9 (4) BAMA-O: Rücktritt bis 27.05.2024 möglich
• Teaching Form: Lecture / Exercise
• Enrolment Type: Compulsory Module
• Course Language: English

## Programs, Module Groups & Modules

Data Engineering MA
IT-Systems Engineering MA
Digital Health MA
Cybersecurity MA
Software Systems Engineering MA
• HPI-SSE-A Analytical Foundations

## Description

In almost all areas of life, large amounts of data are generated, requiring dedicated procedures for data analysis to allow predictions and inference for decision making. Computational statistical methods have evolved to cope with challenges arising from large datasets that are not tractable with traditional approaches, e.g. when the number of possible parameters of a model exceeds the number of observations. At the same time, this wealth of data allows replacing distributional assumptions with data-driven analyses.

In this course, we will cover statistical summary of data, hypothesis testing, regression as well as statistical learning approaches with focus on clustering and classification. We will contrast traditional frequentist approaches for these tasks with non-parametric, computational more intensive alternatives and Bayesian approaches.

The lecture will be accompanied by exercises, which focus on applying the covered method to real-life data from different areas of life. In the course we will work with R. Basic programming knowledge is a prerequisite to successfully complete the exercises. For those students who are not familiar with R, an introduction to R will be provided on moodle.

Learning Objectives:

• Understand concepts and methods of computational statistics
• Ability to statistically evaluate real-world data
• Ability to assess the quality and validity of a statistical method for a given analysis
• Ability to select, implement and apply appropriate statistical methods and algorithms for a given use case

## Requirements

• Fundamentals in calculus and vector analysis (at least comparable to the Mathematik I + II lectures in the ITSE Bachelor at HPI, Mathematik III is a plus)
• Basic programming knowledge (Python or R are a plus)
• Knowledge of English (The lecture will be given in English, but you can ask questions in German and submit German solutions etc.)

## Literature

1. Hastie, Trevor ; Tibshirani, Robert ; Friedman, Jerome: The elements of statistical learning: data mining, inference and prediction. 2 : Springer, 2009 (https://web.stanford.edu/~hastie/ElemStatLearn/)
2. James, Gareth ; Witten, Daniela ; Hastie, Trevor ; Tibshirani, Robert: An Introduction to Statistical Learning -- with Applications in R. 103. New York : Springer, 2013 (Springer Texts in Statistics). - ISBN 978-1-4614-7137-0 (http://faculty.marshall.usc.edu/gareth-james/ISL/)

## Learning

Lectures and Exercises.

## Examination

Final exam covering all lecture materials (70% of final grade)