Introduction to Data Analytics and AI

Published on
Embed video
Share video
Ask about this video

Scene 1 (0s)

Introduction to Data Analytics and AI. © Copyright New Zealand Skills and Education College 2024.

Scene 2 (12s)

Artificial Intelligence (A-I---) is a discipline focused on the simulation of human intelligence in machines that are designed to think, learn, and perform tasks autonomously. The primary objective of (A-I ) is to create systems capable of mimicking human cognitive functions, such as learning, problem-solving, perception, and language understanding. By leveraging algorithms and computational models, (A-I ) enables machines to analyze complex data, recognize patterns, make decisions, and even adapt to new information over time. This transformative technology has the potential to revolutionize numerous fields, from healthcare and finance to robotics and natural language processing, making it an essential component of modern innovation and research..

Scene 3 (56s)

[Audio] Machine Learning (M-L---) is a subset of Artificial Intelligence (A-I---) that focuses on a method of data analysis using algorithms and statistical models. These models enable machines to identify patterns and make decisions based on data. In essence, machine learning revolves around teaching computers to learn from data, allowing them to perform tasks such as prediction, classification, or clustering without direct human intervention. By leveraging historical data, machines can refine their accuracy and improve their performance over time, making ML a powerful tool in fields ranging from finance and healthcare to marketing and technology..

Scene 4 (1m 36s)

[Audio] According to Statista, the global data volume is projected to triple to 180 zettabytes by 2025. To put it in perspective, a single zettabyte is equivalent to a trillion gigabytes of data. The question arises: is it possible to extract meaningful insights from such an overwhelming amount of information? The answer is a resounding Yes, thanks to the tools and techniques developed in the field of data analysis. The ability to make sense of such massive volumes of data is highly valuable for various businesses. Within these seemingly endless repositories of data lie valuable insights that can help companies better understand their customers, design more effective marketing campaigns, and outmaneuver competitors. It is the responsibility of data analysts to uncover these insights and enable data-driven decisions that drive business success..