Foundations For Analytics With Python From Non Programmer To Hacker
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Foundations for Analytics with Python
Author: Clinton W. Brownley
language: en
Publisher: "O'Reilly Media, Inc."
Release Date: 2016-08-16
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
Python程序设计
《Python程序設計》是面向大學計算機科學專業的教材。本書以Python語言為工具，採用相當傳統的方法，強調解決問題、設計和編程是計算機科學的核心技能。 全書共13章，此外，還包含兩個附錄。第 1章到第5章介紹計算機與程序、編寫簡單程序、數字計算、對象和圖形、字符串處理等基礎知識。第6章到第8章介紹函數、判斷結構、循環結構和布爾值等話題。第9章到第 13章著重介紹一些較為高檔的程序設計方法，包括模擬與設計、類、數據集合、面向對象設計、算法設計與遞歸等。附錄部分給出了Python快速參考和術語表。每一章的末尾配有豐富的練習，包括複習問題、討論和編程聯繫等多種形式，幫助讀者鞏固該章的知識和技能。 《Python程序設計》特色鮮明、示例生動有趣、內容易讀易學，適合Python入門程序員閱讀，也適合高校計算機專業的教師和學生參考。 More
Practical Data Analysis
Author: Hector Cuesta
language: en
Publisher: Packt Publishing Ltd
Release Date: 2016-09-30
A practical guide to obtaining, transforming, exploring, and analyzing data using Python, MongoDB, and Apache Spark About This Book Learn to use various data analysis tools and algorithms to classify, cluster, visualize, simulate, and forecast your data Apply Machine Learning algorithms to different kinds of data such as social networks, time series, and images A hands-on guide to understanding the nature of data and how to turn it into insight Who This Book Is For This book is for developers who want to implement data analysis and data-driven algorithms in a practical way. It is also suitable for those without a background in data analysis or data processing. Basic knowledge of Python programming, statistics, and linear algebra is assumed. What You Will Learn Acquire, format, and visualize your data Build an image-similarity search engine Generate meaningful visualizations anyone can understand Get started with analyzing social network graphs Find out how to implement sentiment text analysis Install data analysis tools such as Pandas, MongoDB, and Apache Spark Get to grips with Apache Spark Implement machine learning algorithms such as classification or forecasting In Detail Beyond buzzwords like Big Data or Data Science, there are a great opportunities to innovate in many businesses using data analysis to get data-driven products. Data analysis involves asking many questions about data in order to discover insights and generate value for a product or a service. This book explains the basic data algorithms without the theoretical jargon, and you'll get hands-on turning data into insights using machine learning techniques. We will perform data-driven innovation processing for several types of data such as text, Images, social network graphs, documents, and time series, showing you how to implement large data processing with MongoDB and Apache Spark. Style and approach This is a hands-on guide to data analysis and data processing. The concrete examples are explained with simple code and accessible data.