Data Science Frontiers – Purpose

We have rapidly entered the world of Artificial Intelligence (AI). From news articles to LinkedIn posts, it is hard to move within the technology realm or beyond without coming across an item discussing how AI is going to dramatically transform the world as we know it.

We are quickly seeing Artificial Intelligence and various complimentary models interwoven into many aspects of day-to-day life. And for our readers of this site, who are predominantly Business Technology focused, AI’s role in steering business process and interactions is becoming obviously apparent.

As we stand on the precipice of the data frontier there are many concerns and confusions that have appeared. Technologically, the democratisation of AI has been a game changer. Leveraging these incredibly powerful models, grounded on your own data, has never been more accessible. However, the very democratisation and out-of-the-box intelligence functionality that enables the users, admin, and technologists to easily use this complex AI in an effortless manner is what is also creating the concerns. This democratisation can lead to distance in understanding both on how these models work, as well as a lack of knowledge around basic principles of Data Science, Data, and can also lead to confusion on how best to enable and adopt these intelligent solutions for your business or clients.

This is where “Data Science Frontiers” comes in. Our goal is to bridge this gap, by ensuring a library of knowledge that demystifies and facilitates the understanding and application of Data Science. We aim to make Data Science more transparent and easily accessible. This blog is going to address these concerns – notably:

• Awareness: Bringing to the forefront the nuances and advancements in the field.
• Education: Shedding light on core principles and intricacies that govern AI.
• Enablement: Facilitating a smoother transition to AI-driven processes.
• Policies & Libraries: In time there will be a library of free downloadable policies and various models made available to ensure the collaboration from the community in this field.

The articles, videos, and podcast themselves will be created and guided by specialists within the AI and wider technology space.

What does the line-up look like?

We are going to start with bringing Data Science back to the foundational basics and unpacking what we mean by those two words “Data” and “Science” within this context. We will begin this journey by exploring the famous IBM Game of Checkers by Arthur Samuel. We will then move on to input vs command data and how this fits within the world of data science.

Once an introduction to Machine Learning has been provided we will then dive into the various learning categories including:

• Supervised
• Unsupervised
• Semi-Supervised
• Reinforcement
• Q- Learning

We will also explore various algorithms, these include:

• Linear Regression
• Logistic Regression
• K-Nearest Neighbours
• K-Means Clustering
• Bias & Variance
• Support Vector Machines
• Artificial Neural Networks
• Decision Trees
• Ensemble Modelling

Other:

• Best practices for training
• Ethics
• Accessibility
• Bias
• Policies

All this will be presented in an easy-to-understand format, enriched with illustrations to make your learning journey more engaging.

We’ll also explore vendor-specific models like Microsoft’s Copilot/GPT, Google’s BARD, and offerings from Meta.

Additionally, we will look into data structuring to ensure it is optimally configured for value extraction.

This is merely the beginning. The content will continually evolve based on feedback and growing understanding.

We are excited to take you on this enlightening journey and will be actively seeking your feedback for ongoing improvement.