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The academic discipline of computer science emerged in the 1960s, with a focus on programming languages, compilers, operating systems, and the mathematical theories supporting these areas. Theoretical computer science courses covered finite automata, regular expressions, context-free languages, and computability. In the 1970s, algorithm studies became a significant component of the field, emphasizing the practical applications of computers.

Today, there is a fundamental shift towards a wide range of applications. Numerous factors contribute to this change, including the convergence of computing and communication technologies, the increased capacity to observe, collect, and store data in various fields, and the rise of the internet and social networks as integral parts of daily life. These developments present both opportunities and challenges for theoretical computer science. While traditional areas remain crucial, future researchers will increasingly focus on utilizing computers to extract valuable information from massive datasets generated by applications, rather than solely on solving well-defined problems. Consequently, we are developing a repertoire of techniques to address the theoretical knowledge expected to be relevant in the next 40 years, just as the understanding of automata theory, algorithms, and related topics provided an advantage in the past 40 years. One notable shift is the heightened emphasis on probability, statistics, and numerical methods.

This repository aims to explore enduring concepts and ideas that have withstood the test of time. We investigate intriguing and relatively straightforward notions that form the foundation for creating data products. These concepts are, to some extent, non-obvious, even to well-trained computer scientists, as they are not typically found in traditional computer science textbooks. We will explore mathematically rigorous definitions for some of the central problems, ideas, and algorithms for building data products.

Covers the following:

1. Philosophy of Computer Science
2. Set and Matrice
3. Algorithmic Graph problems and theory
   1. Leet Code
4. Probability
5. Data Science Problems
   1. Julia
   2. CS168
   3. Advanced Spark
6. Machine Learning
   1. predictive maintenance
7. Statistics
8. Sequential Decision Making and RL
9. Multi-agent systems

• Essential Data Literacy: A Primer
• Understanding Statistics and Probability from DS Prespective

• matrices
• stencils
• univariate/multivariate functions
• vector calculus
• matrix calculus
• sequence convergence
• principal component analysis
• dimensionality reduction
• matrix rank
• projections, rotations
• eigenvalues
• singular value decomposition
• outliers, noise
• correlation
• random sampling
• probability density function
• monte carlo methods
• random walks
• continuous limit
• ordinary differential equations
• curve fitting
• partial differential equations
• finite differences
• discretization
• bifurcations
• hysteresis
• optimization
• gradient descent
• global optimization
• constrained optimization
• nonlinear optimization
• inverse modeling

• CS168
• Foundations of data sceince

• The Data Scientist's Field Guide: Navigating the Landscape of Data Science
• A/B Testing
• Product Analytics
• Process-Oriented Data Science