Statistical software

PS IMAGO PRO (IBM SPSS Statistics)

The Nicolaus Copernicus University has a site license for PS IMAGO PRO Academic software based on the IBM SPSS Statistics analytical engine. The software includes numerous modules for advanced statistical analysis and data mining. It can be used by every employee and student of the Nicolaus Copernicus University, on university and home computers, for purposes related to teaching and non-commercial scientific research. The condition for using the software is to fill in the Confirmation of having read the terms of use of the software and deliver it to an authorized person in a given unit.

The software is distributed by designated department tutors and the University Centre for IT Services.

NCU participates in the ARIADNA Program of SPSS cooperation with Academic Units. This program offers various benefits for employees and students, including discounts on analytical workshops and training and the possibility of accreditation of the conducted classes.

Recommended literature:

Górniak Jarosław, Wachnicki Janusz: Pierwsze kroki w analizie danych. SPSS Polska, Kraków, 2010 (available in the NCU University Library).
Malarska Anna: Statystyczna analiza danych wspomagana programem SPSS. SPSS Polska, Kraków, 2005 (available in the NCU University Library).
Bedyńska Sylwia, Cypryańska Marzena (Eds.): Statystyczny drogowskaz 1. Praktyczne wprowadzenie do wnioskowania statystycznego. Wydawnictwo Akademickie SEDNO, Warszawa, 2013 (e-book available through the NCU University Library).
Field Andy: Discovering Statistics Using IBM SPSS Statistics. Sage, Los Angeles, 2018 (available in the NCU University Library).
https://www.discoveringstatistics.com/

IBM SPSS Amos

IBM SPSS Amos is advanced structural equation modeling (SEM) software. Only for Windows. NCU has a university-wide license for this software - the rules of use and the possibility of obtaining access are the same as in the case of PS IMAGO PRO.

Recommended literature:

Bedyńska Sylwia, Książek Monika: Statystyczny drogowskaz 3. Praktyczny przewodnik wykorzystania modeli regresji oraz równań strukturalnych. Wydawnictwo Akademickie SEDNO, Warszawa, 2012 (e-book available through the NCU University Library).

Statistica

NCU Collegium Medicum has purchased a license for the Statistica program for all employees and students of the NCU CM in Bydgoszcz. Statistica is a comprehensive analytical tool that allows users to access data, prepare and analyze data, report and implement analytical models in various environments. Access details are provided in the information about this software.

Recommended literature:

Stanisz Andrzej: Przystępny kurs statystyki z zastosowaniem STATISTICA PL na przykładach z medycyny. Tomy 1-3. StatSoft Polska, Kraków, 2006-2007.
Internetowy Podręcznik Statystyki.

GRETL

The GRETL program includes basic econometric procedures and methods. It is available under the GNU General Public License, which means free access for all users.

Recommended literature:

Kufel Tadeusz: Ekonometria. Rozwiązywanie problemów z wykorzystaniem programu GRETL. Wydawnictwo Naukowe PWN, Warszawa, 2013 (e-book available through the NCU University Library).
Adkins Lee: Using gretl for Principles of Econometrics. 2018 (e-book available on the author's website).

R

R is a language and environment for statistical computing and graphics. It is available under the GNU General Public License - this means that R is completely free for both educational and business use. There are several graphical user interfaces, e.g., RStudio,
an integrated development environment.

Recommended literature:

Biecek Przemysław: Przewodnik po pakiecie R. Oficyna Wydawnicza GiS, Wrocław, 2014 (e-book available on the author's website).
Emmanuel Paradis: R for Beginners (website).
Winston Chang: Cookbook for R (website).
James Gareth, Witten Daniela, Hastie Trevor, Tibshirani Robert: An Introduction to Statistical Learning with Applications in R. Springer, 2017 (pdf available on the book's website).
Hadley Wickham: Advanced R. Chapman & Hall, 2019 (website).
Hadley Wickham, Garrett Grolemund: R for Data Science. O'Reilly, 2017 (website).
Garrett Grolemund: Hands-On Programming with R. O'Reilly, 2014 (website).

Python

Python is one of the most popular high-level programming languages. Its simplicity and readability enable it to be used by even people with little programming experience. It is particularly appreciated by specialists in data analysis and machine learning. Thanks to the huge number of specialized libraries, even complex analytical tasks can be completed with just a few lines of code. Libraries such as NumPy, pandas, Matplotlib, or scikit-learn have become a standard in the world of data science specialists.

Recommended literature:

Kenneth Reitz, Tanya Schlusser: The Hitchhiker's Guide to Python. O'Reilly, 2016 (website).
Al Sweigart: Automate the Boring Stuff with Python. No Starch Press, 2019 (website).
Charles R. Severance: Python For Everybody. Exploring Data in Python 3. (website).
Jack VanderPlas: Python Data Science Handbook. O'Reilly, 2016 (website).
Chris Albon: Notes On Using Data Science & Machine Learning To Fight For Something That Matters (website).