READING LISTS
Essential
Foster, I., Ghani, R., Jarmin, R.S., Kreuter, F., & Lane, J. (Eds.). (2020). Big Data and Social Science:
Data Science Methods and Tools for Research and Practice (2nd ed.). Chapman and Hall/CRC.
Silge, J., & Robinson, D. (2017). Text mining with R: A tidy approach; O'Reilly Media, Inc.".
Tyagi, A.K. (Ed.). (2021). Data Science and Data Analytics: Opportunities and Challenges (1st ed.).
Chapman and Hall/CRC.
Wickham, H., & Grolemund, G. (2017). R for data science: Import. Tidy, transform, visualize, and model
data, 1.
Recommended
Huang, S., & Deng, H. (2021). Data Analytics: A Small Data Approach (1st ed.). Chapman and
Hall/CRC.
Roiger, R.J. (2017). Data Mining: A Tutorial-Based Primer, Second Edition (2nd ed.). Chapman and
Hall/CRC.
Memon, Q.A., & Khoja, S.A. (Eds.). (2019). Data Science: Theory, Analysis and Applications (1st ed.).
CRC Press.
Irizarry, R.A. (2019). Introduction to Data Science: Data Analysis and Prediction Algorithms with R (1st
ed.). Chapman and Hall/CRC.
Ratner, B. (2017). Statistical and Machine-Learning Data Mining: Techniques for Better Predictive
Modeling and Analysis of Big Data, Third Edition (3rd ed.). Chapman and Hall/CRC.
Recommended
Journal Resources:
SAGE Journals
APA Journals
Elsevier
Pub Med
Nature Neuroscience
Neuron
British Journal of Psychology
International Journal of Data Science and Analytics
Neuroscience Informatics