Readers' Corner

8 Best Books for Data Science Majors

Data science is everywhere. Big data analysis requires good command of mathematical statistics and machine learning methods. For all that, better start with math. In particular, such elements as derivatives, differentials, and matrix determinants. All major aspects of this field appear in educational programs. But still, some extra knowledge from books for data science can ease trampling through the course materials.

Books and lecture courses will help master the essential disciplines. Though, sometimes students have little time for extracurricular reading and various paperwork. If this is the case, you may rely on essay writers service to cover for the rest of the tasks. Here are some ideas on what books to read in the lookout for applied knowledge and data science.


Data Science from Scratch: First Principles with Python by J. Grus

This guide contains tips for those with programming experience and very little knowledge of data analysis.

The author presents the most essential info for a deep dive into data science. This book requires no preliminary knowledge of analytics. The readers will learn Python, algebra, calculus, and statistics, machine learning, and more. So, it will appeal to experienced coders as well as beginners.

Extra emphasis highlights social media analysis techniques, database fundamentals, and SQL.


Practical Statistics for Data Scientists: 50 Essential Concepts by P. Bruce and E. Bruce

The book is relevant for practitioners working with R-language. Crucial note: it requires basic knowledge of statistics from its readers. It is beginner-friendly and easy to read. The authors explain the main statistics concepts related to DS in layman’s terms.

It covers exploratory analysis, sample distributions, statistical experiments, significance testing, regression, prediction, classification, unsupervised and statistical machine learning. All relevant technical terms are highlighted and explained. The style is a little tight, but it also has no excessive info. Due to the lack of eloquent rhetoric, the abstracts from this work can be solid arguments in research papers.


Java Data Science Cookbook by R. Shams

Java means everything in building scientific models for manufacturing processes. With full-fledged libraries like MLlib, Weka, and DL4j, a skilled pro can complete all the information processing tasks at hand.

The book begins with tips on retrieving, indexing, and searching compilations. Only after the introduction of all key concepts, the author moves on to various methods for data analysis and extraction. The most advanced aspects covered by the book are big data processing, deep learning, and visualization.


Python for Data Analysis by W. McKinney

The author of this book created Pandas library that revolutionized the world of DS.

Pandas is analogous to the SQL query language that allows working with tables directly in RAM. Numpy, in its turn, is suitable for calculations involving matrices. So it is convenient to use for linear algebra. The compilations processed in Pandas can be used in machine learning models.

The text is lengthy and can serve as a complete manual for Numpy and Pandas software libraries. It includes code examples for preparing information. Along with it, the author provides tips on the libraries’ syntax. The book contains a lot of useful info on applied DA and shows examples of Pandas’ implementation in financial and economic sectors


TensorFlow for Deep Learning by B. Ramsundar & R. Bosag Zadeh

TensorFlow is a neural network library developed by Google. It aims to simplify the writing of neural networks, but it is not so easy to master.

This book discusses kinds of neural networks that could be created with TensorFlow. It also features an overview of the general principles of fully connected, convolutional, recurrent layers, and Long Short-Term Memory (LSTM). As a bonus, it has info on the architectures of deep networks: LeNet, AlexNet, ResNet, AlphaGo, and others. Acquaintance with the library begins with simple models – linear and logistic regression.


Bayesian Methods for Hackers by C. Davidson-Pilon

This work is an introduction to Bayesian inference. It will help readers understand the main ideas of A/B testing, fraud detection, and others by uncovering the role of Bayesian inference. The author reaches out to enthusiasts without a solid background in math willing to learn how to use Bayesian methods. It is also a great resource for learning PyMC, a probabilistic Python-based programming language.


Jupyter for Data Science by D. Toomey

Jupyter for data science helps those already familiar with the Jupyter Notebook. It also can be informational for specialists learning how to use this software for various tasks in DS. This book describes steps of data pipeline implementation with Jupyter, including visualization.

It covers the Jupyter’s interface and all its key features in detail. The author shows how to integrate Python 3, R, and Julia into this Notebook for information processing.


Principles of Strategic Data Science by P. Prevos

Knowledge is pivotal but practical application is what matters. This guide will be helpful for those who already know about DS. It will help them narrow down their knowledge to real-world applications. Prevos explains what DS is and how management can use it to optimize workflows. He also gives reliability criteria for the information products and statistics visualization methods.

This book helps to explore the five-step framework for adding value to extracted data. The readers discover the strategic aspects of this process. The guide also reveals the role of the in-house data analyst in the integration of the DS approach to business processes.


Final Thoughts

The internet resources suggest a great number of manuals for coders and analysts. But the majority of them are not as informative for data analysts as these eight books for data science.

This selection of topic-related issues suits data scientists and comprises all the necessary information. The knowledge will create a secure foothold in systemic learning.

Machine learning algorithms are now at their height. Predictions become more accurate and new areas of application appear every day. Knowledge of methods is important but it’s not the main marketable skill. The essential one is being able to apply them to solve practical problems. Practice begins with theory, so first get what books for data science have to offer and then move to practice.

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