Foundation of Data Science and Algorithm Design
The data-science life cycle: collection, pre-processing, exploration, modelling and evaluation — plus core algorithm-design techniques.
Understand the evolution of data science.
Learn data collection and preprocessing strategies.
Build and assess data-based models.
Design algorithms for linear and non-linear data structures.
Apply data science to real-world problems and communicate solutions.
Modules
References
W. McKinney, Python for Data Analysis, 2e, O'Reilly, 2017.
Tan, Steinbach, Karpatne & Kumar, Introduction to Data Mining, 2e, Pearson, 2018.
James, Witten, Hastie & Tibshirani, An Introduction to Statistical Learning with Applications in R, Springer, 2013.
O'Neil & Schutt, Doing Data Science, O'Reilly, 2015.
Dietrich, Heller & Yang, Data Science and Big Data Analytics, EMC, 2013.
Horowitz, Sahni & Rajasekaran, Fundamentals of Computer Algorithms, 2e, Universities Press, 2007.
Aho, Hopcroft & Ullman, The Design and Analysis of Computer Algorithms, Addison-Wesley, 1974.