INF2420 - Introduction to Data Science

It is a capital mistake to theorize before one has data.
Sherlock Holmes in “A Study in Scarlett” (Arthur Conan Doyle).

Suggested References: 

- Laura Igual, and Santi Seguí. "Introduction to Data Science”, Springer, Cham, 2017.

- Larry Wasserman. All of statistics: a concise course in statistical inference. Springer Science & Business Media, 2013.

- Nina Zumel and John Mount. Practical Data Science with R, Manning, 2014.

- Crhistopher Gandrud, Reproducible Research with R and RStudio, R Series, CRC Press, 2014.

- Yihui Xie, Dynamic Documents with R and Knitr, R Series, CRC Press, 2014.

- Oscar P. Lamigueiro, Displaying Time Series, Spatial and Space-Time Data with R, R Series, CRC Press, 2014.

- Gareth James et al., An Introduction to Statistical Learning with Application in R, Springer, 2013.

- Eric D. Kolasky and Gabor Csárdi, Statistical Analysis of Network Data with R, Springer, 2014.


Software:

http://www.anaconda.com/

http://www.r-project.org/

http://www.rstudio.com/


DataCamp courses:

DataCamp_Icon_RGB-a3afe004da4a462151b8e87a89937603ea50534f338d4600cb11043a9cc434c9

This class is supported by DataCamp, the most intuitive learning platform for data science. Learn R, Python and SQL the way you learn best through a combination of short expert videos and hands-on-the-keyboard exercises. Take over 100+ courses by expert instructors on topics such as importing data, data visualization or machine learning and learn faster through immediate and personalised feedback on every exercise.

Class material:

http://www.ideias.inf.puc-rio.br/Aulas

e-mail: lopes at inf dot puc-rio dot br
Tel: 55+21+3527-1500 R:4350  Fax: 55+21+3527-1530
Pontifícia Universidade Católica do Rio de Janeiro (PUC-Rio) - Departamento de Informática - Sala 408 RDC
Rua Marquês de São Vicente 225, Gávea, Rio de Janeiro, RJ, Brazil, CEP 22451-900