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Books on data analysis theory & techniques for business & economics
Featured ebook collections
O'Reilly ebooks/Safari Technical books online
1,700+ O'Reilly IT/computing books. Simultaneous access is limited to 30 UC users, so please click “Sign Out” when finished. Our Safari license only includes the O’Reilly titles. If you need access to titles from Cisco, IBM and the other publishers, Safari offers several personal subscription options. [1997 to present]
Taylor & Francis Collection
Includes the CRC Press and other e-book collections are primarily based in the sciences, and are great for getting up to speed quickly on a technology. Note: Not all CRC titles are licensed by UCSD.
Books on using specific software
This section provides a sample of the types of materials you can find by browsing the subject links on the right.
Data Science Using Python and R
Call Number: Wiley e-book
Publication Date: 2019
This book covers the two most widespread open-source platforms for data science: Python and R, so that readers may learn step-by-step how to produce hands-on solutions to real-world business problems, using state-of-the-art techniques. Written for the general reader with no previous analytics or programming experience, there are chapters dedicated to learning the basics and step-by-step instructions and walkthroughs for solving data science problems. Those with analytics experience will appreciate having a one-stop shop for learning how to do data science using Python and R. Topics covered include data preparation, exploratory data analysis, preparing to model the data, decision trees, model evaluation, misclassification costs, naïve Bayes classification, neural networks, clustering, regression modeling, dimension reduction, and association rules mining.
Python for Data Mining Quick Syntax Reference
Call Number: Springer e-book
Publication Date: 2019
Learn how to use Python and its structures, how to install Python, and which tools are best suited for data analyst work. This handy reference and tutorial covers topics ranging from basic Python concepts through to data mining, manipulating and importing datasets, and data analysis, including illustrative examples. The first part covers core Python including objects, lists, functions, modules, and error handling. The second part covers Python's most important data mining packages: NumPy and SciPy for mathematical functions and random data generation, pandas for dataframe management and data import, Matplotlib for drawing charts, and scikitlearn for machine learning.
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