Concept building is the first thing that a learner do while starting a career in Data Science. If you are a Professional also, then the time arises when you have to brush up your skills to grow in the field and gain in-depth knowledge about the subjects. Because the scope is very large, you can't relieve yourself from learning and growing everyday. The problem today, before every learner is not in the 'learning part' (as if everyone wants to learn), but in selecting the right resource for learning. There are a number of online resources that have emerged today to teach learners the various concepts and things related to Data Science that it is a bit confusing to get started with any. To help you resolve on that part, I have mentioned in the article, the best Books, that every learner should learn in Data Science. If you are a Professional, and have not gone through these books, then you also must try these books to brush up your skills and learn the underlying concepts in Data science.
You will learn in the book, the most fundamental question: What exactly data science is?. Most learners are confused over this part also. The book will unfold the concepts about how to use the versatile R programming language for data analysis. Other than this, the quality concepts provided in the book are:
Data Wrangling- how to manipulate data-sets to reveal new information
Data Visualization- how to create graphs and other visualizations
Exploratory Data Analysis- how to find evidence of relationships in your measurements
Modelling- how to derive insights and predictions from your data
Inference- how to avoid being fooled by data analyses that cannot provide full-proof results
The book is very good to make the readers understand about the core concepts in Data Science and providing learners the statistical worldview.
Often with real world examples we learn a lot. This is an amazing book which will dive you through 25 interviews of the world's best data scientists. These data scientists are from one of the most established companies such as Facebook, LinkedIn, Pandora, Intuit, and The New York Times. You will explore a in this book about perspectives of the best data scientists on data science and gain a meaningful life advice which will help you a lot in developing a career in Data Science. The book is packed with strategies, suggestions and recipes to launch and grow your career in data science.
Statistical knowledge is one of the key skill of Data Scientists. As a Data Scientists a lot of data is visualized and interpreted using the various statistical techniques. Whether you are someone who is a statistician or non statistician, the book will help you a lot in developing the key concepts in statistics, which are must for analyzing data. Some of the most important modeling and prediction techniques are presented in the book. A variety of topics including linear regression, classification, re-sampling methods, shrinkage approaches, tree-based methods, support vector machines, clustering, are discussed in the book to give you the clear cut concepts. The concepts are illustrated with the most amazing graphics using the real world examples. You must take the advantage of this book in developing your concepts. Some previous knowledge of linear regression is required before starting the book.
Big business decisions are taken by using the meaningful insights gathered from the data. The book uncovers the "data-analytic thinking" which is very necessary for extracting useful knowledge and business value from the data collected in business. You will be learning the most useful data-mining techniques from the book. The book provides real-world examples of business problems to make you understand of the various problems with their respected solutions. Not only the techniques but also, the book will help you in building your communication skills to work in a business environment where you will be dealing a lot with the stakeholders and the people. Some key concepts provided by the book are:
Approaching business problems data-analytically, using the data-mining process
And, general concepts for actually extracting knowledge from data.