Instructor(s):

Roland Molontay
Weeks
1-7
Contact hours
2x2 hours
Credit
2 credits

Short Description of the Course:
Data Scientist is called "the sexiest job of the Century" by Harvard Business Review. In the first part of the course, we learn the basics of understanding data and predicting its unknown properties.

We give a general introduction to data analysis, modeling, and algorithms of data mining.The course provides a good base for its follow-up course, Data Mining Applications.Lectures are supplemented by computer exercises and student projects in small teams.

Aim of the Course:
The aim of the course is to provide a basic but comprehensive introduction to data mining. By the end of the course students will be able to choose the right algorithms for data science problems to build, implement and evaluate data mining models.

Prerequisites:
The course requires basic knowledge in calculus, probability theory, and linear algebra. Knowledge of graphs and basic algorithms is an advantage. Basic programming skills are also required.

Detailed Program and Class Schedule:

  • Motivations for data mining. Examples of application domains.
  • Analyzing data: preparation and exploration.
  • Models and algorithms for classification.
  • Introduction to the IPython Notebook and python based data mining software packages. Classification with scikit-learn.
  • Basics of classification. Concepts of training and prediction. Measuring quality and comparison of classification models.
  • Type of variables, measuring similarity and distances. The k-nearest neighbor classifier.
  • Decision trees, naive Bayes. The concept of model over and underfitting. Midterm test.
  • Basics of cluster analysis. Partitioning clustering algorithms, k-means, k-medoids.
  • Hierarchical clustering algorithms.
  • Introduction to frequent itemset mining. Applications for finding association rules. Level-wise algorithms, APRIORI.
  • Final test.

Method of instruction:
Handouts, presentations, IPython Notebooks, relevant research papers, web page, course mailing list and Wiki. Weekly regular office hour for consultations..

Textbooks:
Pang-Ning Tan, Michael Steinbach, Vipin Kumar: Introduction to Data Mining, Addison-Wesley, 2006.

Jure Leskovec, Anand Rajaraman, Jeff Ullman: Mining of Massive Datasets

http://www.mmds.org/.

Instructors' bio:

Roland Molontay (born 1991) obtained his PhD degree in network and data science from Budapest University of Technology and Economics (BME). He was a visiting PhD student at Brown University in 2016. Currently he holds a research position at MTA-BME Stochastics Research Group and he also teaches mathematics and data science at BME for undergraduate and graduate students. He has been participating in many successful data intensive R&D projects with renowned companies (such as NOKIA-Bell Labs) throughout the years. He has been awarded the Gyula Farkas Memorial Prize in 2020 for his outstanding work in applied mathematics. He is the founder and leader of the Human and Social Data Science Lab at BME.

Students' Review About This Course

"The Data Science course solidified my decision to pursue a career in the field. Professor Molontay engaged us with the material really well as we discussed topics from gradient boosting to artificial neural networks. Professor Molontay even mentored my class project group in transforming our final project into a research paper which has been accepted into the journal Applied Network Science."

Tiernon Riesenmy

Tiernon Riesenmy

The University of Kansas

"Data Science was a great introduction to how to gather and manage big datasets! Professor Molontay gave a great overview of all the algorithms one can use to extract information from these datasets and clearly explained how these algorithms manage to do so. It was a super rewarding class and inspired me to explore topics in Machine Learning further!"

Kiersten Campbell

Kiersten Campbell

Williams College

"Data Science is a great class. It is taught very well. Prof. Molontay was constantly checking in on our progress and how we were doing. I think that was really helpful. He obviously cared about how much the students were learning and that we were actually grasping the concepts and not just getting by. He was always in very close contact with the students which was good."

Kate Barnes

Kate Barnes

Colorado College