Popular Data Analysis & Visualization Books
The following 9 Data Analysis & Visualization books are the current best-sellers. If you are looking for books about Data Analysis & Visualization, then these books will certainly help! If you know more books about Data Analysis & Visualization which are missing from the list below, please let us know!
Popular Data Analysis & Visualization Books
Practical Statistics for Data Scientists: 50 Essential Concepts
Statistical methods are a key part of of data science, yet very few data scientists have any formal statistics training. Courses and books on basic statistics rarely cover the topic from a data science perspective. Many data science resources incorporate statistical methods but lack a deeper statistical perspective.
Hands-On Machine Learning with Scikit-Learn and TensorFlow: Concepts, Tools, and Techniques to Build Intelligent Systems
Through a series of recent breakthroughs, deep learning has boosted the entire field of machine learning. You’ll learn a range of techniques, starting with simple linear regression and progressing to deep neural networks. Explore the machine learning landscape, particularly neural nets Use scikit-learn to track an example machine-learning project end-to-end Explore several training models, including support vector machines, decision trees, random forests, and ensemble methods Use the TensorFlow library to build and train neural nets Dive into neural net architectures, including convolutional nets, recurrent nets, and deep reinforcement learning Learn techniques for training and scaling deep neural nets Apply practical code examples without acquiring excessive machine learning theory or algorithm details.
High Performance Spark: Best Practices for Scaling and Optimizing Apache Spark
Apache Spark is amazing when everything clicks. Authors Holden Karau and Rachel Warren demonstrate performance optimizations to help your Spark queries run faster and handle larger data sizes, while using fewer resources. With this book, you’ll explore: How Spark SQL’s new interfaces improve performance over SQL’s RDD data structure The choice between data joins in Core Spark and Spark SQL Techniques for getting the most out of standard RDD transformations How to work around performance issues in Spark’s key/value pair paradigm Writing high-performance Spark code without Scala or the JVM How to test for functionality and performance when applying suggested improvements Using Spark MLlib and Spark ML machine learning libraries Spark’s Streaming components and external community packages.
Kafka: The Definitive Guide: Real-Time Data and Stream Processing at Scale
Learn how to take full advantage of Apache Kafka, the distributed, publish-subscribe queue for handling real-time data feeds. Authors Neha Narkhede, Gwen Shapira, and Todd Palino show you how to deploy production Kafka clusters; secure, tune, and monitor them; write rock-solid applications that use Kafka; and build scalable stream-processing applications. Learn how Kafka compares to other queues, and where it fits in the big data ecosystem Dive into Kafka’s internal design Pick up best practices for developing applications that use Kafka Understand the best way to deploy Kafka in production monitoring, tuning, and maintenance tasks Learn how to secure a Kafka cluster Get detailed use-cases.
Spark: The Definitive Guide: Big data processing made simple
With an emphasis on improvements and new features in Spark 2. Developers and system administrators will learn the fundamentals of monitoring, tuning, and debugging Spark, and explore machine learning techniques and scenarios for employing MLlib, Spark’s scalable machine learning library. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Spark’s Structured Streaming and MLlib for machine learning tasks Explore the wider Spark ecosystem, including SparkR and Graph Analysis Examine Spark deployment, including coverage of Spark in the Cloud.
Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
Looking for complete instructions on manipulating, processing, cleaning, and crunching structured data in Python. Written by Wes McKinney, the main author of the pandas library, Python for Data Analysis also serves as a practical, modern introduction to scientific computing in Python for data-intensive applications. It’s ideal for analysts new to Python and for Python programmers new to scientific computing.
Data Science from Scratch: First Principles with Python
Data science libraries, frameworks, modules, and toolkits are great for doing data science, but they’re also a good way to dive into the discipline without actually understanding data science. In this book, you’ll learn how many of the most fundamental data science tools and algorithms work by implementing them from scratch. Today’s messy glut of data holds answers to questions no one’s even thought to ask.
Practical Tableau: 100 Tips, Tutorials, and Strategies from a Tableau Zen Master
Author Ryan Sleeper is one of the most qualified Tableau consultants in the world, having earned the titles of Tableau Zen Master (2016/17), Tableau Iron Viz Champion (2013), and author of the Tableau Public Visualization of the Year (2015). Practical Tableau takes you beyond web posts and videos to give you a firm understanding in Tableau for finding valuable insights in data. Practical Tableau is organized into five sections: Fundamentals: get started using Tableau from scratch Framework: explore the INSIGHT framework, a proprietary process for building Tableau dashboards Storytelling: learn tangible tactics for storytelling with data Chart Types: use step-by-step tutorials to build a variety of charts in Tableau Tips & Tricks: learn innovative uses of parameters, color theory, making your Tableau workbooks run efficiently, and more.