For those who want a career in data science, artificial intelligence, or big data analytics, this field guide is for you. From exploratory data analysis and predictive analytics to machine learning and deep learning techniques, this book will teach you everything you need to know about becoming a master of data science today.
Welcome to the Data Master’s Guide, a field guide to data science. This book is designed for people who want to learn how data science is used in real-world scenarios, by taking a hands-on approach and showing you how data scientists think about problems.
This book will provide an overview of what data science is and why it matters, as well as give you some practical advice on how best to get started on your journey towards becoming a data master!
Chapter 1. Data management today
Data management is essential to the success of data science. Data scientists often spend more time cleaning and preparing data than they do analyzing it, but that’s not all bad news–it means you can get started right away on making sense of your own datasets!
Data management is a multi-step process that includes cleaning and preparing data for analysis. The goal of this chapter is to help you understand how these steps work together as part of a larger effort involving multiple people from different departments within an organization or company:
- Data management involves defining standards for how information should be stored, who has access rights (like who can edit or delete), how long those records should be kept around before being archived/deleted off-site…etcetera ad nauseum (or however many nauseums there are).
- A team made up of people from across different disciplines will likely collaborate on creating these standards so everyone knows what needs doing when working with each other later on down the road when someone comes knocking asking questions like “hey can I see some stuff?”
Chapter 2. How to be a data master
You are a data master. You have mastered the science of data and can apply it to any situation. You can use your knowledge to help others understand how to best use their data, whether they’re looking into statistics or machine learning.
You have seen some pretty awesome things in your time as a master of data science, but there’s much more out there for you to discover! In this book we’ll take a look at how other people have used their mastery of this subject (and maybe even some tips) so that when you encounter them again–or even someone else entirely–you know exactly what they mean by “data science”.
Chapter 3. Getting started with data science
Data science is a broad field that involves using data to solve problems. It’s not just about machine learning, although that’s one of many subfields within data science. Data scientists need domain expertise and statistical knowledge in order to be effective.
To get started with your journey as a data scientist, we recommend first reading up on some general concepts:
- What is Data Science? https://www.datasciencecentral.com/profiles/blogs/what-is-data-science
- What are machine learning algorithms? https://www.datasciencecentral.com/profiles/blogs/what-are-machine-learning-algorithms
Chapter 4. From domain expertise to machine learning algorithms
In this chapter, we’re going to talk about the importance of domain expertise and machine learning algorithms. We’ll also discuss data science as a whole, including its relationship with data management and storage. Finally, we’ll look at some security measures you can implement when storing your data in order to keep it safe from prying eyes.
Chapter 5. From big data analytics to artificial intelligence predictions
Big data analytics is the process of analyzing large amounts of data to discover patterns and test hypotheses. The term “big” refers to the size of the data being analyzed, which may be too large or complex for traditional methods such as surveys or focus groups.
Artificial intelligence (AI) is a branch of computer science that studies how software can be made to act like a human brain, in terms of perception, reasoning, learning and problem solving. AI has been applied in many ways over the years: computer games use AI algorithms so players have opponents who behave realistically; robots use it for movement; online assistants like Siri offer human-like answers based on your voice commands; even self-driving cars rely on machine learning algorithms!
In this chapter we’ll cover how you can use AI predictions as part of your day-to-day job as a data scientist or analyst–or even if you’re just interested in learning more about these fascinating technologies!
Chapter 6. Using AI and machine learning to predict the future of healthcare and life sciences
The future of healthcare is here. It’s time to get your hands dirty and start digging into data!
In this chapter, we’ll discuss how AI and machine learning are revolutionizing the way we think about healthcare. This includes both predicting the future of life sciences (including pharmaceuticals) as well as improving patient care through predictive analytics.
This guide will teach you how to get started in data science, artificial intelligence, and big data analytics.
This guide is a field guide to data management. It’s a master’s guide to data management, and it will teach you how to be a data master.
You’ll learn how to get started in data science, artificial intelligence and big data analytics by reading this book!
We hope that this guide has helped you get started with data science, artificial intelligence, and big data analytics. As we said before, these are some of the most exciting fields in technology today and they’re also pretty complex! We know that it can be a little overwhelming at first, but if you keep learning new things every day and keep practicing what we’ve taught you here then eventually things will start falling into place. The most important thing is just keep going–don’t give up!