Some of the highest salaries in IT right now are in data science. Data scientists are also at the top of the demand list. And one of the highest earnings increases in the last six months — surprise-surprise — is also there.
So if you’re not already interested in data science, maybe it’s time to start? And if you’re already familiar with the topic, you can always go deeper. This selection includes the best books on Data Science for professionals of all levels. If you are a mature data scientist, come to one of our FP or AI projects, or keep reading.
One of them might be precisely what you are looking for. Nee
Head First Statistics: A Brain-Friendly Guide
As with other Head First books, the tone of this book is friendly and conversational, and it’s the best book to get started on data science. The book covers a lot of statistical data, starting with descriptive statistics – mean, median, modus, standard deviation – and then moves on to probabilistic and logical statistics such as correlation, regression, etc. If you studied natural sciences or commerce in school, you might have studied all this, and this book is a great start for a detailed update of everything you’ve already learned.
Practical Statistics for Data Scientists
The book is intended for Data Science professionals who have experience with the R programming language and have a preliminary understanding of mathematical statistics. It presents key concepts from statistics that relate to data science in a convenient and easily accessible form. It also explains which concepts are important and valuable in terms of data science, which are less critical, and why. Topics covered in detail include exploratory data analysis, data and sample distributions, statistical experiments and significance testing, regression and prediction, classification, statistical machine learning, and teacherless learning.
Introduction to Probability
This book by William Feller introduces probability theory with the right balance between mathematical precision, probabilistic intuition, and specific applications. An Introduction to Probability describes the material accurately while avoiding unnecessary technical details. After introducing the basic vocabulary of randomness, including events, probabilities, and random variables, the text invites the reader to familiarize themselves with the main theorems of the subject: the law of large numbers and the central limit theorem.
Introduction to Machine Learning with Python
This book by Sarah Guido and Andreas K. Mueller is perfect for people who want to learn more about building machine learning models with Python. Everything is explained in an accessible way for beginners, and there are lots of examples, so you won’t get lost while reading. After finishing the book, you’ll be able to create your own models quickly. What’s more, you don’t need any prior knowledge of math or programming languages to read this book.
Python Machine Learning By Example
As the name suggests, this book is the easiest way to get acquainted with machine learning. The book will introduce you in detail and interestingly to Python and machine learning with the help of some examples, such as spam detection using Bayes and predictions using regression and tree algorithms. The author shares his experience in different areas of machine learning, such as advertising optimization, conversion rate prediction, click fraud detection, etc., which perfectly complements the reading experience.
Pattern recognition and machine learning
This textbook from Christopher M. Bishop covers the latest developments, providing a thorough introduction to the field of machine learning and pattern recognition. It is intended for students as well as researchers and practitioners. No prior knowledge of the concepts described is assumed. However, knowledge of multivariate calculus and linear algebra is required, and some experience in the probabilistic theory implementation. The latter may not be necessary since the book includes a separate introduction to basic principles.
Python for data analysis
Get complete instructions on manipulating, processing, cleaning, and analyzing datasets in Python. Updated for Python 3.6, this practical guide is filled with practical examples that will show you how to solve a range of data analysis problems. While reading, you will learn the latest versions of pandas, NumPy, Python, and Jupyter. Wes McKinney writes this book, creator of the Python pandas project, this book is a practical, modern introduction to Python data science tools.
This book reveals the beauty of statistics and enlivens statistics. The tone is witty and conversational. You won’t be bored reading this book or feeling the weight of mathematics! The author explains all the concepts of statistics – basic and advanced – using real-life examples. The book starts with straightforward things, such as the normal distribution and the central theorem, and moves on to complex real-world problems, correlation data analysis, and machine learning.
Data Science and big data analytics
This book from EMC Education Services provides you with all the information: the process and how you can use Python to make it all work for you. Even if you have no idea how to code or know what to do with all the data you have collected, this book will give you all the tools you need. There are many different components in data science, and the ability to put them together can really help better understand customers, find new products, and more.
R for data science
Last but not least. R for data science explains the concepts of statistics and the type of data you will see in real life, how to transform it using concepts like median, mean, standard deviation, etc., AND how to create data, filter, and clean it up. The book will help you understand how confusing and raw data is and how it is processed. Converting data is one of the most time-consuming tasks. This book will help you learn about the different methods of converting data for processing to extract meaningful information from it.
If you are interested in data science, then these books are for you. Pick one that seems to be more on your level of knowledge and start learning! AI is among the most progressive and perspective areas, these blog collects more IT-related items about the most progressive topics. To gain knowledge of machine learning modeling, neural network principles, and artificial intelligence opportunities will be useful for both personal and business development.