Introduction to Data Science with Python

online course

Students will learn how to use the popular pandas data science library and jupyter notebooks
as a working environment for data analysis, as well as how to effectively use data handling
functions. At the end of the course, students will be able to load data from a file or retrieve it
from an online service, cleanse and manipulate it, perform basic inferential statistical
analysis, and create comprehensive data visualisations. They will also know how to use
powerful libraries such as scikit-learn and statsmodels to apply machine learning techniques
such as clustering, classification and regression, and to perform time series forecasting. They
will learn how to use the Python data science ecosystem in several practical case studies, such
as market basket analysis, portfolio optimization, and online advertising in social networks.
The course consists of five sessions. Each session lasts about four hours.
Session 1: Introduction to Python data science ecosystem
Session 2: Data wrangling and data exploring
Session 3: Data visualizations and time series analysis
Session 4: Machine learning and time series forecasting
Session 5: Practical case studies from finance and marketing

Course leader

Matej GUID & Martin MOZINA, University of Ljubljana, Faculty of Computer and Information Science, Slovenia

Target group

Doctoral Winter School is an online programme with courses intended for PhD students, post-docs, academics and professionals from different areas and all around the world.

Course aim

The course Introduction to Data Science with Python gives an overview of some of the basic
topics in data science, such as data analysis, data visualisation, machine learning, and time
series forecasting. The course is designed for students who want to learn about Python's
powerful data science ecosystem to apply data analysis techniques, information
visualisation, and inferential statistical analysis to gain new insights into data. The course is
taught in a tutorial format. The emphasis is not on computer programming, but mainly on
the use of various practical tools and libraries in the Python programming language. They will
be introduced through various case studies and practical examples from different fields of
economics.

Credits info

4 EC
Participants who need ECTS credits for their PhD studies, can obtain an official Transcript of records upon completion of all course obligations with passing final examination/assesment.

Fee info

EUR 600: For more information please see our website.