Syllabus

Current Courses

Current projects + Game plan

• Analysis of Algorithms

  • All book’s Algorithms shall be recorded

  • Parallel Algorithmic visualizations are to be developed

• Anthropology and Genetics Algorthms

  • All Genetic Algorthms shall be recorded

• Data Science Tools

  • Books Content with Data Tools are to be recorded + Explained.

  • Together with Hands-On Machine learning

Future Courses

• Elementos de Matemática

  • Write Down and Study Discrete Math

• Tratamiento Digital de la Imagen I

  • Develop Digital Art Everyday with high quality Illustration Art.

• Animación 2D para Videojuegos

• lustración Conceptual

  • Concept Art Everyday.

• Lógica de la Programación

  • Document Unity Cookbook

• Introducción al Diseño de Juegos

  • Document Game Design Books

Strategy

This are some notes from my college courses. As I started this project I acknowledge that I am behind in the sense that I am writing this docs while coursing through my courses. I am starting this project since last semester studying Computer Science @ CUNY Brooklyn College. But Plan to slowly recover the content from the previous courses.

I am heavily focusing on Stem or Business Courses. If a class belongs to Non-stem or Business I will attempt to relate it to stem using the tools I have learned in my stem courses.

Common Strategies:

• Developing Simulations and Modeling

  • Can Inspire by playing a Steam Game and inspiring a development of it.

• Use a combination of:

  • A well respected book

  • A well recorded video playlist.

Lecture Notations Strategy

1. Add all Assigments.

2. Add a video explaining section by section the assigment.

3. Add your notes if possible

Traversal and Priority Queue.

1. 2024 Spring - Finish College Major in CS

2. 2024 Fall - 2027 Start Game Dev Argentina

Books

Introduction to Algorithms, 4rth Edition, Carmen, Leiserson, Rivest, Stein

Graph Algorithms for Data Science

• Graphs and Network Science

• Representing Network Structure

• Exploratory Graph Analysis

• Introduction to Social Network Analysis

• Projecting Monopartite Networks

• Inferring Co-Occurrence Networks Based on Bipartite Networks

• Constructing Nearest Neighbor Similarity Network

• Node Imbedding And Classification

• Link Prediction

Graph Machine Learning

• Machine Learning on Graphs

• Unsupervised Learning on Graphs

• Problems with Machine Learning on Graphs

• Social Network Graphs

• Text Analytics and Natural Language Using Graphs

• Graph Analysis for Credit Card Transactions

• Building a Data Driven Powered Application.

Basic Graph Theory

• Paths, Cycles and Connectivity

• Matching and Covering

• Planar Graphs

• Graph Coloring

• Diagraphs

• Special Cases of Graphs

AI Project

• Artificial Intelligence: A Modern Approach, Global Edition

• Natural Language Processing with Transformers, Revised Edition

• Deep Learning with Python, Second Edition

• Machine Learning Design Patterns: Solutions to Common Challenges in Data Preparation, Model Building, and MLOps

• Hands-On Machine Learning with Scikit-Learn, Keras, and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems

• Data Science on AWS: Implementing End-to-End, Continuous AI and Machine Learning Pipelines

• Probabilitic Deep Learning with Python

Finance

• Machine Learning and Data Science Blueprints for Finance: From Building Trading Strategies to Robo-Advisors Using Python

• Python for Algorithmic Trading: From Idea to Cloud Deployment

System Design Mastery

• Fundamentals of Data Engineering: Plan and Build Robust Data Systems

• Practical Linear Algebra for Data Science: From Core Concepts to Applications Using Python

• Generative AI on AWS: Building Context-Aware Multimodal Reasoning Applications

• Fundamentals of Software Architecture: An Engineering Approach

• Software Architecture: The Hard Parts

• Building Microservices: Designing Fine-Grained Systems.

• Building Event-Driven Microservices

• Microservices Pattern

• Building Evolutionary Architectures: Automated Software Governance

Intensive Data Engineering

• Designing Data-Intensive Applications

• Designing Distributed Systems: Patterns and Paradigms for Scalable, Reliable Services.

• Database Internals: A Deep Dive into How Distributed Data Systems Work

• Kafka The Definitive Guide: Real-Time Data and Stream Processing at Scale.

Game Dev

• AI For Game Developers

• Game Programming Patterns

• Theory of Fun for Game Design

• Game Design Workshop: A Playcentric Approach to Creating Innovative Games

• Foundations of Game Engine Development: Mathematics + Rendering.

• AI For Games

Philosophic

• The Staff Engineer’s Path: A Guide for Individual Contributors Navigating Growth and Change

• Observability Engineering: Achieving Production Excellence

• Learning Domain Driven Design: Aligning Software Architecutre and Business Strategy.

• Kubernetes Patterns.

• Software Engineering at Google: Lessons Learned from Programming Over Time

Cookbooks:

Cookbooks series, are but quick references: They could be used as for tutorial guides very interesting as they could also be used as practice interview prompts. Now my problem would be whats the difference from usign the web? I know at least with this a listing of common problems and the scope of work that can be done using each technolgoy I guess. Thats good enough for listing for Testing Generation. While the books above are great to learn theory. These are great for creating flaschard questions and testing your knowledge (Prepare for Interviews in an intensive way.). You cna see that is probably a combinatiion of documentation to achieve a specific short task in itself. I still have to view how the evlaution would work for this case.

Must Have (Money Makers)

• SQL Cookbook

• AWS Cookbook

• Machine Learning with Python Cookbook

• Deep Learning Cookbook

• Regular Expressions Cookbook

Very Important

• Flutter and Dart Cookbook.

• React Cookbook

• Unity Game Development Cookbook

• CSS Cookbook

• Angular Cookbook

important

• Kubernetes Cookbook

• Bash Cookbook

• Windows Powershell Cookbook

• Excel Scientific and Engineering Cookbook

Meh

• Electronics Cookbook: Practical Electronic recipes

• Arduino Cookbook: Recipes to Begin, Expand, and Enhance Your Projects

• Web Security Testing Cookbooks

• Raspberry Pi Cookbook

• Linux

• Linux Networking Cookbook

• Linux Security

Industrial Engineering.

• Inventory Optimization: Models and Simulations

• Data Science for Supply Chain Forecasting

• Demand Forecasting - Best Practices - Manning

• Data Science for Business: What You Need to Know about Data Mining and Data-Analytic Thinking

• Storytelling with data