Foundations of Data Science
Overview
Data Science is the study of the generalizable extraction of knowledge from data. Unprecedented advances in digital technology during the second half of the 20th century and the data explosion that ensued in the 21st century is transforming the way we do science, social science, and engineering. Application of data science cut across all verticals. The whole idea of this course is to introduce the students with the sole foundations/mathematics of data science. The course will cover topics such as Probability Distributions, Mathematical Optimization, Big Data, Machine Learning, etc.
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Prerequisites
This course has no prerequisites.
Textbooks
| Title | Author(s) | Edition |
|---|---|---|
| Foundations of Data Science | Avrim Blum, John Hopcroft, Ravi Kannan | (2018) |
| Machine Learning | Tom M. Mitchell | 1st (1997) |
| Pattern Recognition & Machine Learning | Christopher M. Bhisop | 1st (2006) |
| Machine Learning – An Algorithmic Perspective | Marsland Stephen | 2nd (2015) |
| Introduction to Machine Learning | Alpaydin Ethem | 3rd (2014) |
| Essentials of Statistics | Mario Triola | 5th (2015) |
Videos
- StatQuest with Josh Starmer
- Data Science Specialization, coursera
- Machine Learning by Andrew NG, coursera
Websites
- Towards Data Science
- Machine Learning, Medium
- UC Berkeley's Data 8 Foundations of Data Science
- Computational Structures in Data Science
- Data Science Central