Computer Science

An Introduction to Big Data: NoSQL

An Introduction to Big Data: NoSQL

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.

Biologically-Inspired AI: Differential Evolution, Particle Swarm Optimization, and Firefly Algorithms

Biologically-Inspired AI: Differential Evolution, Particle Swarm Optimization, and Firefly Algorithms

There have been significant advances in recent years in the areas of neuroscience, cognitive science, and physiology related to how humans process information. This semester, I’m taking a graduate course called Bio-Inspired Intelligent Systems. It provides broad exposure to the current research in several disciplines that relate to computer science, including computational neuroscience, cognitive science, biology, and evolutionary-inspired computational methods. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having some knowledge of these models would allow you to develop algorithms that are inspired by nature to solve complex problems.

An Introduction to Big Data: Data Normalization

An Introduction to Big Data: Data Normalization

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.

Biologically-Inspired AI: Genetic Algorithms

Biologically-Inspired AI: Genetic Algorithms

There have been significant advances in recent years in the areas of neuroscience, cognitive science, and physiology related to how humans process information. This semester, I’m taking a graduate course called Bio-Inspired Intelligent Systems. It provides broad exposure to the current research in several disciplines that relate to computer science, including computational neuroscience, cognitive science, biology, and evolutionary-inspired computational methods.

Biologically-Inspired AI: Optimization and Local Search

Biologically-Inspired AI: Optimization and Local Search

There have been significant advances in recent years in the areas of neuroscience, cognitive science, and physiology related to how humans process information. This semester, I’m taking a graduate course called Bio-Inspired Intelligent Systems. It provides broad exposure to the current research in several disciplines that relate to computer science, including computational neuroscience, cognitive science, biology, and evolutionary-inspired computational methods.

An Introduction to Big Data: Data Querying

An Introduction to Big Data: Data Querying

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.

An Introduction to Big Data: Relational Database

An Introduction to Big Data: Relational Database

This semester, I’m taking a graduate course called Introduction to Big Data. It provides a broad introduction to the exploration and management of large datasets being generated and used in the modern world. In an effort to open-source this knowledge to the wider data science community, I will recap the materials I will learn from the class in Medium. Having a solid understanding of the basic concepts, policies, and mechanisms for big data exploration and data mining is crucial if you want to build end-to-end data science projects.

Demystifing Graph Algorithms

Demystifing Graph Algorithms

This is a complete guide to graph algorithms that you can rely on to practice for your next technical interview. No matter how complicated these concepts may seem, repeatedly getting familiar with different techniques and problems will make you more competent at solving them.

Greedy Algorithm and Dynamic Programming

Greedy Algorithm and Dynamic Programming

In an algorithm design there is no one 'silver bullet' that is a cure for all computation problems. Different problems require the use of different kinds of techniques. A good programmer uses all these techniques based on the type of problem. In this blog post, I am going to cover 2 fundamental algorithm design principles: greedy algorithms and dynamic programming. 

Divide and Conquer Algorithms

Divide and Conquer Algorithms

A very popular algorithmic paradigm, a typical Divide and Conquer algorithm solves a problem using following three steps:

  • Divide: Break the given problem into subproblems of same type.

  • Conquer: Recursively solve these subproblems

  • Combine: Appropriately combine the answers