Clinton Brownley

If you're like many of Excel's 750 million users, you want to do more with your data-like repeating similar analyses over hundreds of files, or combining data in many files for analysis at one time. This practical guide shows ambitious non-programmers how to automate and scale the processing and analysis of data in different formats-by using Python. After author Clinton Brownley takes you through Python basics, you'll be able to write simple scripts for processing data in spreadsheets as well as databases. You'll also learn how to use several Python modules for parsing files, grouping data, and producing statistics. No programming experience is necessary. Create and run your own Python scripts by learning basic syntax Use Python's csv module to read and parse CSV files Read multiple Excel worksheets and workbooks with the xlrd module Perform database operations in MySQL or with the mysqlclient module Create Python applications to find specific records, group data, and parse text files Build statistical graphs and plots with matplotlib, pandas, ggplot, and seaborn Produce summary statistics, and estimate regression and classification models Schedule your scripts to run automatically in both Windows and Mac environments

Whether managing strategy, operations, or products, making the best decision in a complex uncertain business environment is challenging. One of the major difficulties facing decision makers is that they often have multiple, competing objectives, which means trade-offs will need to be made. To further complicate matters, uncertainty in the business environment makes it hard to explicitly understand how different objectives will impact potential outcomes. Fortunately, these problems can be solved with a structured framework for multi-objective decision analysis that measures trade-offs among objectives and incorporates uncertainties and risk preferences. This book is designed to help decision makers by providing such an analysis framework implemented as a simple spreadsheet tool. This framework helps structure the decision making process by identifying what information is needed for the decision, defining how that information should be combined and, finally, providing quantifiable evidence to clearly communicate and justify the final decision. With this process, decision making is made more efficient by focusing only on information and factors that are well-defined, measureable, and relevant to the decision at hand. Using this structured decision-making framework, anyone can effectively and consistently make better decisions to gain a competitive and strategic advantage.