Intern
    Data Science Chair

    Data Science (ehemals Data Mining)

    General Information

    Organizer: Prof. Dr. Andreas Hotho, Andrzej Dulny

    Contact: datamining[at]informatik.uni-wuerzburg.de

    The lecture provides an overview of knowledge extraction from (structured) data. This includes, among other things

    • Pre-processing techniques
    • OLAP analysis & data warehousing
    • Clustering (k-means, k-medoids, DBSCAN, OPTICS)
    • Classification (k-Nearest-Neighbor, Bayes, Decision Tree, Support Vector Machine; Bagging, Boosting, e.g. Random Forest, AdaBoost)
    • Regression analysis (linear regression, logistic regression)
    • Association rule learning (Aprioiri, FP-Growth)
    • Introduction to Deep Learning

    Literature

    • {Pattern recognition and machine learning}. Bishop, C.M. Vol. 4. Springer, 2006.
    • Einführung in Data Science. Grus, Joel. O’Reilly, 2019.
    • Data Science from Scratch: First Principles with Python. Grus, Joel. O’Reilly, Beijing, 2015.
    • Data Mining - The Textbook. Aggarwal, Charu C. bll 1–693. Springer, 2015.

    Further literature from the field of data science and machine learning

    • Practical Statistics for Data Scientists. Bruce, Peter; Bruce, Andrew; Gedeck, Peter. 2nd ed. O’Reilly Media, Inc., 2020.
    • Introduction to Machine Learning with Python. Müller, Andreas C.; Guido, Sarah. O’Reilly, 2016.
    • Deep Learning. Goodfellow, Ian; Bengio, Yoshua; Courville, Aaron. 2016.
    • Data Mining for Business Analytics: Concepts, Techniques and Applications in Python. Shmueli, Galit; Bruce, Peter C.; Gedeck, Peter; Patel, Nitin R. 1st ed. Wiley & Sons, Inc., 2020.
    • An introduction to statistical learning. James, Gareth; Witten, Daniela; Hastie, Trevor; Tibshirani, Robert. Springer, 2013.