Python/R for Data Science

Lecture notes for Spring 2023 at Arkansas Tech University

Author

Weijia Jia & Xinli Xiao

Published

April 21, 2023

Preface

This is the lecture notes for STAT 2304 Programming languages for Data Science Spring 2023 at ATU. If you have any comments/suggetions/concerns about the notes please contact us at or .

References

[1]
Klosterman, S. (2021). Data science projects with python: A case study approach to gaining valuable insights from real data with machine learning. Packt Publishing, Limited.
[2]
McKinney, W. (2017). Python for data analysis: Data wrangling with pandas, NumPy, and IPython. O’Reilly Media.
[3]
Shaw, Z. A. (2017). Learn python 3 the hard way. Addison Wesley.
[4]
Sweigart, A. (2020). Automate the boring stuff with python, 2nd edition practical programming for total beginners: Practical programming for total beginners. No Starch Press.
[5]
Prabhakaran, S. (2018). 101 NumPy exercises for data analysis (python).
[6]
Grolemund, G. (2014). Hands-on programming with r: Write your own functions and simulations. O’Reilly Media.
[7]
Prabhakaran, S. (2018). 101 pandas exercises for data analysis.
[8]
Beuzen, T. and Timbers, T. (2022). Python packages. Taylor & Francis Group.
[9]
Wickham, H. and Grolemund, G. (2017). R for data science: Import, tidy, transform, visualize, and model data. O’Reilly Media.
[10]
Youens-Clark, K. (2020). Tiny python projects. Manning Publications.