# Exam Fundamentals of Big Data Analytics

## Literature

- Alfonso S. Bandeira, Ten Lectures and Forty-Two Open Problems in the Mathematics of Data Science.
- J. Leskovec, A. Rajaraman, and J. D. Ullman, Mining of Massive Datasets Second edition. Cambridge: Cambridge University Press, 2014.
- C. D. Meyer, Matrix analysis and applied linear algebra. Philadelphia: Society for Industrial and Applied Mathematics, 2000.
- K. P. Murphy, Machine learning: a probabilistic perspective. Cambridge, MA: MIT Press, 2012.
- C. Aggarwal, Data Mining. Cham: Springer International Publishing, 2015.
- T. Hastie, R. Tibshirani, and J. H. Friedman, The elements of statistical learning: data mining, inference, and prediction, 2nd ed. New York, NY: Springer, 2009.
- K. V. Mardia, J. T. Kent, and J. M. Bibby, Multivariate analysis. Londonâ€Ż; New York: Academic Press, 1979.
- Complete list of the bibliography.

## Dates

Exam Fundamentals of Big Data Analytics

## News

There will be no lecture " Fundamentals of Big Data Analytics" this semester. The lectures and previous exercises are available at here.

## Exercise

## Help-sheet

- Help-sheet for Fundamentals of Big Data Analytics (english version only)

## Pocket Calculator

While taking the written exam, you are allowed to use a pocket calculator from this list.

## Information about the Exam

- The reexamination of Fundamentals of Big Data Analytics begins on Monday, August 28, 2017, 10:30 in the room 1010|201. During the exam you are allowed to use a pocket-calculator that is enlisted in the list above. We will distribute the official help sheet (English version) at the beginning of the exam.
- The results of the exam are now online. The access codes are found on the information sheets distributed during the exam.
- Exam inspection (Klausur-Einsicht) (Monday, 04.09.17, 14:00h, ICT-Cubes, room 333)

## Preparation

There will be no specific preparation to these exams.

## Consultation hour

Dr. Arash Behboodi upon agreement