You can build some real applications within a week of reading the book. The book is purely technical and you can go step-by-step to fully enjoy the book. The questions flow in an organized manner and help you understand each aspect of data science like data preparation, the importance of big data, the process of automation and how data science is the future of the digital world. The book keeps you motivated. The book covers in detail about machine learning models, NLP (Natural language processing) applications and recommender systems using PySpark. This book is for you if you are an architect. If you are from a math background in school, you might remember calculating the probability of getting a spade or heart from a pack of cards and so on. This book gently introduces big data and how it is important in today’s digitally competitive world. Data Science for the Layman: No Math Added” by Annalyn Ng and Kenneth Soo. The whole data analytics lifecycle is explained in detail along with case study and appealing visuals so that you can see the practical working of the entire system. If you read other books, you will realize how complex neural networks and probability are. Data science has a lot to do with math, which can make data science seem inaccessible and daunting. For updates follow @rafalab. The book covers a lot of statistics starting with descriptive statistics – mean, median, mode, standard deviation – and then go on to probability and inferential statistics like correlation, regression, etc… If you were a science or commerce student in school, you may have studied all of it, and the book is a great start to refresh everything you have already learned in a detailed manner. Signup to submit and upvote tutorials, follow topics, and more. The author also gives a lot of references in the book and points to useful resources that you will enjoy going through. You wouldn’t even realize how many concepts you can grasp in a day of reading the book – getting to know the context and audience, using the right graph for the right situation, recognizing and removing the clutter to get only the important information, utilize the most significant parts of the data and present them to users – all of these and more. You don’t have to read them all. If you have read Harry Potter, you will know what we are talking about. It is not a purely technical book but a quick reference as it contains information in the form of questions and answers from various leading data scientists. The book will help you through the process of setting up the required software until the creation, update, and monitoring of models. This is a good book for beginners and advanced level data scientists alike. You can find some good real-life examples to keep you hooked on to the book. This book helps you cover the basics of Machine Learning. View all posts by the Author. While the book explains the basics well, it will be good to have some prior knowledge of statistics with some of these courses, so that you can quickly get on with the book. The author approaches the topics with subtlety and presents many case studies that are easy to understand, comprehend and follow. So much so, that you need not be a computer science graduate to understand this book. This is an advanced book. The book also surprises one with a survey of ML models. Anything told as a story and shown as graphics fit into our mind easily and stays there permanently. Overall, a well-organized book with a thorough explanation of data analysis concepts. It clearly explains why you should learn data science and why it is the right choice for you. This book started out as the class notes used in the The book is written from a business perspective and offers a lot of insight into how all the technologies like cloud, big data, IT, mobility, infrastructure, and others are transforming the way businesses work today along with interesting stories and personal experiences to share. Overall a great book to begin your data science journey. A wonderful book that explains data mining from scratch. The book is quite impactful and deals with the fundamental concepts of data visualization for you to understand how to make the most of the huge chunks of data available in the real world. My passion for writing started with small diary entries and travel blogs, after which I have moved on to writing well-researched technical content. We make announcements related to the book on Twitter. The author has done an exceptional job in penning all the concepts in the form of stories that are easy to comprehend. It includes statistical and analytical tools, machine learning techniques and amalgamates basic and high-level concepts very well. Designing data-intensive applications, Head First Statistics: A Brain-Friendly Guide, Introduction to Machine Learning with Python: A Guide for Data Scientists, Business analytics – the science of data-driven decision making, Data Science Course: Complete Data Science Bootcamp, Top Data Science Interview Questions & Answers, Difference between Data Science vs Machine Learning, Difference Between Supervised vs Unsupervised learning. The structure and flow of the book are very good and well organized. This book covers all the topics that are needed for data science. However, reading this book alone won’t be sufficient as you get deeper into ML and coding. The book has everything from economics, statistics, finance and all you need to start learning data science. You will also learn about scholastic models and six sigma towards the end of the book. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. 1. You can easily understand the entire big picture of how analytics is done as each step is like one chapter in the book. Coming to the content, this is one book that covers machine learning inside out. Last, but not least, this book helps understand the architecture of today’s data systems and how they can be fit into applications that are data-driven and data-intensive. The book will help you understand how messy and raw real data is and how it is processed. This book is for all age groups, whether you are an undergraduate, graduate or advanced level researcher, there is something for everyone. This book introduces concepts and skills that can help you tackle real-world data analysis challenges. A great book to learn recommender systems using Spark – neat and simple. You will not get bored reading this book or feel the heaviness of math! This book makes it simple. Introduction. This is a medium level book, a good balance of basic principles and advanced data science principles. The book is like any other fiction book that keeps you hooked up till the last page. The book emphasizes on discovering new business cases rather than just processing and analyzing data. Purely business-oriented, this is one book to start with if you are not able to make up your mind into the field of data science. I find it fascinating to blend thoughts and research and shape them into something It covers what is called as CoNVO – context, needs, vision, and outcome. The tone is friendly and easy to understand. The explanations are pretty neat and resemble real-life problems. Get the international edition that has colorful pictures and graphs making your reading experience totally worth it. If you are planning to learn data science with R, this is the book for you. A hardcopy version of the book is available from CRC Press2. Overall, a great book for beginners as well as advanced users. The changing times and how we should cope with it are described beautifully in this book. The author explains all the concepts of statistics – basic and advanced with real-life examples. It starts with explaining about the digital age, data mining and then moves to explain the kinds of data that can be mined, the patterns that can be mined, for example, cluster analysis, predictive analysis, correlations, etc., and the technologies that are used – statistics, machine learning, and database. It is practical and gives you enough references to start with your technical journey too. For our other readers, there are some prerequisites for you to fully enjoy the book. Just like other books of Headfirst, the tone of this book is friendly and conversational and the best book for data science to start with. and how to plot the data, filter and clean it. Apart from the fact that Data Science is one of the highest-paid and most popular fields of date, it is also important to note that it will continue to be more innovative and challenging for another decade or more.