Hands-on with Git, Bash, shell scripting, testing, and the everyday toolchain of industry software development.

x86 assembly, processor design, and the memory hierarchy — builds intuition for performance and low-level debugging.

C++ and STL: balanced BSTs, graphs, priority queues, and hash tables with rigorous complexity analysis.

Agile methods, specifications, testing, CI, and team-based delivery — mirrors production engineering workflows.

Processes, scheduling, synchronization primitives, virtual memory, and file systems — essential for backend and infra roles.

Logic, proof techniques, sets, relations, and combinatorics — the mathematical backbone of algorithm reasoning.

Counting methods, recurrences, and asymptotic analysis for reasoning about algorithm performance.

Divide & conquer, dynamic programming, greedy algorithms, and graph algorithms — core technical interview material.

Finite automata, regular expressions, context-free grammars, and decidability — formal foundations of compilers and parsers.

Supervised and unsupervised learning: kNN, decision trees, boosting, perceptrons, and k-means from scratch.

Practical predictive analytics and recommendation engines on real-world datasets using ML and regression techniques.

A*, adversarial search, MCTS, constraint satisfaction, and reinforcement learning. (In Progress)

End-to-end data science in Python: wrangling, statistical analysis, visualization, and communicating findings.

Matrix operations, eigenvalues/vectors, and linear transformations — mathematical backbone of ML and graphics.

Classical ciphers, DES, RSA, and public-key systems — relevant to security engineering and protocol design.

Numerical methods for solving linear systems, least squares, and eigenvalue problems computationally. (In Progress)

Defined research problems, read and reviewed technical papers, and proposed an original CS research project.

User research, data-informed prototyping, and usability testing — relevant for product-minded engineering roles. (In Progress)