DATA SCIENCE: 3 in 1- Beginner’s Guide+ Simple and Effective Tips and Tricks+ Advanced Guide to Learn the Realms of Data Science Effectively

This book appeals to the reader’s desire to explore the world of data science in a manner that is not too technical and not too plain, but somewhere in between. This book targets this sweet spot and provides comprehensive yet brief explanations to concepts that might be otherwise misunderstood or easily ignored by the reader due to their inherent complexity.
This book covers the very key and fundamental concepts towards systematically understanding data science by drawing a well-defined road map addressing each topic in such a way that every section of every chapter reinforces the concepts and information laid out in the previous chapters. The main focus of this book is to give the reader insight into the processes involved in data science projects and shed light onto some of the most common aspects of data science, including big data and how it impacts the world. This book attempts to build a solid foundation of the concepts pertaining to data science. It will prove to be the infrastructure that will lead you to one day become a data science expert. In short, this book has all the necessary information a beginner level data scientist would have along with setting up for future improvement by reinforcing this knowledge with the intermediate and expert level books of the data science series.

Are you interested in learning one of the sexiest jobs of the century? That’s what Harvard Business School terms data science as. Let’s face it; data scientists possess skills and qualities that are in more demand now than ever before and that’s not surprising, given the sheer amount of data the world produces.
So, what is data science?
It’s all data and it’s about transforming it into something that businesses can use, and data scientists do this with a great deal of skill and knowledge in math, statistics, algorithms and more. At first glance, you might dismiss data science as being far too hard to learn, but it’s like anything else – break it down into smaller parts and you’ll find it much easier to grasp.
That’s what I’ve done here – broken the subject down into each of its realms, to give you a better idea of what it is all about.

In this book, you will learn:

• What data science is
• How data science relates to and differs from artificial intelligence and machine learning
• Math and statistics
• Descriptive and inferential analysis
• What data engineering is
• What data visualization is
• The different types of visualization
• An introduction to Seaborn
• What machine learning is and how it relates to data science
• Different types of machine learning
• Different ML algorithms
• The steps required for successful machine learning

Along the way you will find plenty of practical examples and we finish off with a series of questions you might be asked at a data science interview, along with detailed answers, and a glossary of terms.