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This guide gathers in one place the links to company financial datasets, industry ratios, background information, news on finance topics and academic research articles for corporate finance and financial markets.
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]
The financial industry has recently adopted Python at a tremendous rate, with some of the largest investment banks and hedge funds using it to build core trading and risk management systems. Updated for Python 3, the second edition of this hands-on book helps you get started with the language, guiding developers and quantitative analysts through Python libraries and tools for building financial applications and interactive financial analytics. Using practical examples throughout the book, learn how to develop a full-fledged framework for Monte Carlo simulation-based derivatives and risk analytics, based on a large, realistic case study. Much of the book uses interactive IPython Notebooks.
Explore the aspects of financial modeling with the help of clear and easy-to-follow instructions and a variety of Excel features, functions, and productivity tips Key Features.This book is a non-data professional’s guide to exploring Excel's financial functions and pivot tables. Learn to prepare various models for income and cash flow statements and balance sheets. Perform valuations and identify growth drivers using real-world case studies. This book will also help individuals that have and don't have any experience in data and stats, to get started with building financial models; it assumes a working knowledge with Excel, however.
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.
Perform data analysis with R quickly and efficiently with more than 275 practical recipes in this expanded second edition. These task-oriented recipes make you productive with R immediately. Solutions range from basic tasks to input and output, general statistics, graphics, and linear regression.
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.
Learn, by example, the fundamentals of data analysis as well as several intermediate to advanced methods and techniques ranging from classification and regression to Bayesian methods and MCMC, which can be put to immediate use
Business Analytics Using R by Umesh R. Hodeghatta; Umesh NayakThis book explains practical business analytics through examples, covers the steps involved in using it correctly, and shows you the context in which a particular technique does not make sense. This book will discuss and explore the following through examples and case studies: data management and R functions, business analytics project life cycle, descriptive analytics, data cleaning, data mining, predictive analytics, classification, association, clustering, and regression.
This ebook explains how developers can establish a blockchain network to handle business-to-business transactions while maintaining privacy and confidentiality. Content from members of IBM’s global blockchain team.