Since the publication of the bestselling first edition, many advances have been made in exploratory data analysis (EDA). Covering innovative approaches for dimensionality reduction, clustering, and visualization, Exploratory Data Analysis with MATLAB^®, Second Edition uses numerous examples and applications to...

• Explore the social media APIs in R to capture data and tame it
• Employ the machine learning capabilities of R to gain optimal business value
• A hands-on guide with...

Full of real-world case studies and practical advice, Exploratory Multivariate Analysis by Example Using R focuses on four fundamental methods of multivariate exploratory data analysis that are most suitable for applications. It covers principal component analysis (PCA) when variables are quantitative, correspondence analysis...

The cost of statistical computing software has precluded many universities from installing these valuable computational and analytical tools. R, a powerful open-source software package, was created in response to this issue. It has enjoyed explosive growth since its introduction, owing to its coherence, flexibility, and free availability....

Software test automation has moved beyond a luxury to become a necessity. Applications and systems have grown ever larger and more complex, and manual testing simply cannot keep up. As technology changes, and more organizations move into agile development, testing must adapt—and...

• Tackle every step in the data science pipeline and use it to acquire, clean, analyze, and visualize your data
• Get beyond the theory and implement real-world projects in data science using...

Many enterprises regard system-level testing as the final piece of the development effort, rather than as a tool that should be integrated throughout the development process. As a consequence, test teams often execute critical test plans just before product launch, resulting in much of the corrective work being performed in a rush and at the...