The increasing amount of data collected through sensors or computational simulations can take advantage of new techniques for being processed in order to extract new insights out of raw data. The purpose of this course is to present researchers with data science methods and techniques. The course includes theoretical lectures followed by practical sessions on data manipulation, visualisation, machine learning and deep learning. A specific session on how to use HPC resources is included.
Participants will utilize Python on their personal computers as well as Cineca HPC resources for practical projects.
-Introduction to data analytics
-Machine Learning theory (supervised/unsupervised)
-Data Manipulation with Numpy and Pandas
-Data visualization with Matplotlib, Seaborn, Bokeh
-Machine learning with sci-kit learn
-Deep learning with Tensorflow and Keras
-Introduction to Cineca HPC facilities
Students and researchers from academia and industry with different backgrounds who are looking for technologies and methods to analyse large amounts of data.
Participants must have basic knowledge in statistics, fundamentals of computer programming with Python and use of GNU/Linux-based systems.
The number of participants is limited to 20 students. Applicants will be selected according to their experience, qualification and scientific interest BASED ON WHAT WRITTEN IN THE "Reason for participation" FIELD OF THE REGISTRATION FORM.
OCT 29th, 2021.
STUDENTS WILL BE NOTIFIED ON THEIR ADMISSION OR NOT WITH AN EMAIL ON MONDAY NOV 9th.