Data Series

Data Discovery: How data is used and analyzed in business 

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Automation with AI: Automating Office Tasks with ChatGPT and Robotic Process Automation 

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Data Toolkit: Python + Hands-On Math: Tools to help you get more out of data

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Learning AI with ChatGPT and Google Colab: The Easiest, Quickest Way to Start Learning AI

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D.A.T.A. Series Information (4 book series - 3 down, 1 to go)

Welcome to the D.A.T.A. Series: Discovery, Automation, Technology, and AI.

The series was created to cover four core areas of applied data science, and introduce readers with no prior background to various tools and techniques, with easy to understand language and hands-on exercises, and introducing related certifications where possible.

The series is meant to be read in order, with each book providing additional context and a foundation for the next.

(D) Discovery: How data is used and analyzed in business. Where data comes from, where it goes, what can you do with it. Tools covered include Google Spreadsheets, Tableau and Looker Data Studio (formerly Google Data Studio). Understanding how data is used in business is critical, and this book lays a foundation for the rest of the series.

(A) Automation: The increasing impact of automation in business, from basic automation to advanced robotic process automation. Several popular tools are discussed and the realities of job disruption and augmentation are also touched upon. The general approach follows the philosophy of exploring and embracing technologies in order to understand them.

(T) Technology Toolkit: This book serves as an opportunity to get a better sense of how coding and math are used in data science and provide a foundation for getting into machine learning and AI. The focus is on Python, as well as math libraries in Python used in data science. No prior experience is required.

(A) Artificial Intelligence: This book focuses on real world AI tools, including ChatGPT and other selected generative AI tools. These tools are easy to use and require no prior knowledge but are having a huge impact. After exploring these tools, the reader is introduced to working directly with machine learning and AI, using several popular Python libraries, including Tensorflow and Pytorch. Several areas of AI are mentioned, with a focus on Natural Language Processing, and Large Language Models, on which tools like ChatGPT and art tools like Dall-e and Midjourney are based.

By the end of the series, readers will get a solid hands on introduction to data science, coding and AI, with the opportunity to explore several certifications, and explore sources for further study.

The purpose of the series is to address a gap that exists: many AI/data courses assume a person will end up seeking to design algorithms and/or pursue advanced graduate study, and the pre-requisites are accordingly high, which makes it harder to start and complete the courses. But in applied data science and applied AI, when you consider how data science and AI are actually used in business, you don't need a PhD to use them - and there are tools and techniques such as using Python libraries, which can save a lot of time, and put you in a position to use algorithms, without necessarily requiring a person to know how to design them.

Part of the intent is to build confidence, help people get acquainted, and encourage learners to go further, but from a friendly, inclusive, hands-on approach.