How to Start Learning Statistics for Computer Science Students

Statistics is not as daunting as it may seem, especially for computer science students. The core concepts you need to know are relatively straightforward, and with the right resources, you can build a strong foundation quickly. This article outlines a step-by-step approach to learning statistics that aligns with your CS curriculum and provides practical applications in the field.

Introduction to Statistics

To get started, familiarize yourself with the basics of statistics. Start with free introductory materials, such as OpenStax or OpenIntro. OpenIntro also offers video lectures to complement your learning.

Books for Programming-Based Statistics

Once you have a foundational understanding, delve into programming-based statistical books:

Think Stats 2e by Green Tea Press ~hastie/CASI_files/PDF/casi.pdf (An online resource with practical applications)

For a more in-depth Bayesian approach, consider the following books:

Bayesian Methods for Hackers by CamDavidsonPilon Think Bayes by Green Tea Press

If you want to invest in a more comprehensive resource and buy a book, consider:

Doing Bayesian Data Analysis by Alberto Alocovadia All of Statistics: A Concise Course in Statistical Inference by Larry Wasserman, published by Springer

Coursework in Mathematical Statistics

Depending on your computer science program, mathematical statistics is likely a core component of your coursework. If you are a Bachelor of Science, Bachelor of Technology, or Bachelor of Applied Science student, you will likely be exposed to both theoretical and practical aspects of statistics.

To practice and apply statistical methods, consider using:

R Pandas in Python

Practice is key. Apply your statistical knowledge to solve problems in various areas relevant to computer science:

Average case complexity analysis of data structures and algorithms Statistical analysis in sorting algorithms Randomized algorithms and their complexity analysis Problems in scheduling, especially in computer networks Probabilistic data structures

By tackling these problems, you will deepen your understanding of the nuances of statistical methods in CS applications.

Transition to Machine Learning and Data Science

Once you have a solid grasp of the basics, you can transition into more advanced topics like machine learning and data science. Here, the importance of statistics becomes even more apparent:

Machine learning is inherently statistical in nature. Theoretical aspects of machine learning, such as Computational Learning Theory, require a strong statistical foundation.

Start with the theoretical side of machine learning and build from there. The more you understand the underlying statistical principles, the better you will be equipped to tackle advanced topics.

Conclusion

Statistics is a valuable tool for computer science students, providing a foundation for understanding data analysis, machine learning, and algorithm design. By following the resources and steps outlined in this article, you can build a strong understanding of statistics that will serve you well in your CS education and beyond.

Best of luck on your learning journey!