TStat’s “Maximising the Potential of Stata’s New Python Capabilities” course offers participants an excellent opportunity to acquire the introductory programming skills required to integrate Python’s capability into Stata 17. The course opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa. In the closing session a series of practical applications will be discussed in order to highlight WHEN and HOW one should exploit the connectivity between Python and Stata for one’s research.
At the end of the course, participants are expected to be able, with the aid of the Stata routines implemented during the sessions, to independently implement the methodologies and techniques illustrated during the course by adopting the Stata routines to their own particular research needs.
In common with TStat’s training philosophy, each session is composed of both a theoretical component (in which the techniques and underlying principles behind them are explained) and an applied (hands-on) segment, during which participants have the opportunity to implement the techniques using real data under the watchful eye of the course tutor. Throughout the course, theoretical sessions are reinforced using applied case studies, in which the course tutor discusses and highlights potential pitfalls and the advantages of individual techniques.
This course is of particular interest to sociologists, mathematicians, economists, ethnologists, epidemiologists and political scientists wishing to acquire the basic tools necessary to use Python routines within Stata.
Participants should having a working knowledge of Stata. No prior knowledge of Python is necessary, although it will be an advantage.
The potential of Stata/Python connectivity: an overview
Python programming basics
Alternative ways to implement Python in Stata: the PyStata Module
Calling Python from within Stata
Calling Stata from within Python
Stata integration of Python Scikit-learn for Machine Learning
Least squares regression in Mata/Python
Stata/Python data visualization
Due to the ongoing Public Health situation, the 2022 edition of this training course will be offered ONLINE on a part-time basis on the 7th-8th of November 10:00 am to 1:30 pm Central European Time (CET).
Full-Time Students*: € 355.00
Ph.D. Students: € 455.00
Academic: € 535.00
Commercial: € 630.00
*To be eligible for student prices, participants must provide proof of their full-time student status for the current academic year. Our standard policy is to provide all full-time students, be they Undergraduates or Masters students, access to student participation rates. Part-time master and doctoral students who are also currently employed will however, be allocated academic status.
Fees are subject to VAT (applied at the current Italian rate of 22%). Under current EU fiscal regulations, VAT will not however applied to companies, Institutions or Universities providing a valid tax registration number.
The number of participants is limited to 8. Places will be allocated on a first come, first serve basis. The course will be officially confirmed, when at least 5 individuals are enrolled.
Course fees cover: teaching materials (handouts, Stata do-files, program templates and datasets to use during the course), a temporary course licence of Stata valid for 30 days from the beginning of the course.
Individuals interested in attending this training course must return their completed registration forms by email (firstname.lastname@example.org) to TStat by 28th October 2022.
The course opens with an introductory session focusing on the Python programming basics required by users wishing to exploit the Stata – Python connectivity, before moving on to illustrate how to use Python in a Stata environment and the vice versa.
Due to the current pandemic situation, the 2022 edition of this training course will now be offered ONLINE, on a part-time basis on the 7th-8th of November.