python金融大数据分析源代码(python for finance source code)。将目录和文件名重新整理
2022-02-13 11:51:38 184KB python 数据分析 源代码
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本软件是湖北省普通高校招生志愿填报大数据分析和模拟填报专用软件。主要功能: 1.查看在湖北省有招生计划的院校代码、地址、电话、招生官网、办学层次、院校类型、重点学科等信息。 2.查看各批次、各院校的专业组代码、首选科目、再选科目、涵盖专业等信息。 3.查看历年各类别的一分一段表。 4.分批次、分院校查看历年的投档线、投档线排名位次和平均投档线、平均排名位次。 5.可以根据设定的考生类别、投档类别、排名范围等筛选符合条件的院校专业组。 6.可以根据办学层次(本科或专科)、办学性质(公办或民办)、院校类型(综合类、理工类、军事类等,是否985、211学校,是否有中外合作办学专业)等筛选符合条件的院校专业组。 7.可以根据指定的省份和城市类别、就读专业意向等筛选符合条件的院校专业组。 8.可以根据各专业组平均投档线排名和考生排名计算录取概率。 9.可以根据设定的冲稳保的录取概率范围和志愿数量随机在候选院校专业组中选择,生成志愿草表。 10.无需注册账户,可以任意试用本软件。
2022-01-30 09:04:45 74.3MB 高考 志愿 大数据分析 模拟填报
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玩转大数据分析!Spark2.X+Python精华实战课程.zip
2022-01-17 09:02:42 129KB 学习资源
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技术架构解决方案-交互式查询 通常的时间跨度在数十秒到数分钟之间 按数据维度进行统计、聚合 根据历史数据进行拟合和预测 计算数据之间的相关性和模式等 适合提供高速在线分析服 典型应用场景 政府各部门数据 证券交易 银行保险 企业ERP/CRM等 适用于数据量在GB到TB的高速数据分析
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C0127国家政务服务平台政务服务大数据分析模型指南
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包含到2-4章的答案
2021-12-27 17:18:36 1.28MB 习题答案
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保险IT数字化转型之大数据分析应用
2021-12-27 16:22:13 2.23MB 保险数字化
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实时大数据分析k-means算法 报告,源码+数据集 对超市购物记录集basket_row.csv数据集中的商品名称进行聚类,实现同一类商品,不同规格汇聚成一个大类的要求。
2021-12-26 23:16:51 465KB 实时大数据分析 Hadoop Python 广工
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Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications By 作者: Ashish Kumar – Joseph Babcock ISBN-10 书号: 1788992369 ISBN-13 书号: 9781788992367 Release 出版日期: 2017-12-27 pages 页数: (660 ) $99.99 Python: Advanced Predictive Analytics: Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Gain practical insights by exploiting data in your business to build advanced predictive modeling applications Social Media and the Internet of Things have resulted in an avalanche of data. Data is powerful but not in its raw form; it needs to be processed and modeled, and Python is one of the most robust tools out there to do so. It has an array of packages for predictive modeling and a suite of IDEs to choose from. Using the Python programming language, analysts can use these sophisticated methods to build scalable analytic applications. This book is your guide to getting started with predictive analytics using Python. You’ll balance both statistical and mathematical concepts, and implement them in Python using libraries such as pandas, scikit-learn, and NumPy. Through case studies and code examples using popular open-source Python libraries, this book illustrates the complete development process for analytic applications. Covering a wide range of algorithms for classification, regression, clustering, as well as cutting-edge techniques such as deep learning, this book illustrates explains how these methods work. You will learn to choose the right approach for your problem and how to develop engaging visualizations to bring to life the insights of predictive modeling. Finally, you will learn best practices in predictive modeling, as well as the different applications of predictive modeling in the modern world. The course provides you with highly practical content from the following Packt books: Learning Predictive Analytics with Python Mastering Predictive Analytics with Python What You Will Learn Understand the statistical and mathematical concepts behind predictive analytics algorithms and implement them using Python libraries Get to know various methods for importing, cleaning, sub-setting, merging, joining, concatenating, exploring, grouping, and plotting data with pandas and NumPy Master the use of Python notebooks for exploratory data analysis and rapid prototyping Get to grips with applying regression, classification, clustering, and deep learning algorithms Discover advanced methods to analyze structured and unstructured data Visualize the performance of models and the insights they produce Ensure the robustness of your analytic applications by mastering the best practices of predictive analysis
2021-12-25 22:49:17 20.59MB python 预测
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唯一一本详细讲解使用Python分析处理金融大数据的专业图书;金融应用开发领域从业人员必读。, Python凭借其简单、易读、可扩展性以及拥有巨大而活跃的科学计算社区,在需要分析、处理大量数据的金融行业得到了广泛而迅速的应用,并且成为该行业开发核心应用的首选编程语言。《Python金融大数据分析》提供了使用Python进行数据分析,以及开发相关应用程序的技巧和工具。, 《Python金融大数据分析》总计分为3部分,共19章,第1部分介绍了Python在金融学中的应用,其内容涵盖了Python用于金融行业的原因、Python的基础架构和工具,以及Python在计量金融学中的一些具体入门实例;第2部分介绍了金融分析和应用程序开发中最重要的Python库、技术和方法,其内容涵盖了Python的数据类型和结构、用matplotlib进行数据可视化、金融时间序列数据处理、高性能输入/输出操作、高性能的Python技术和库、金融学中需要的多种数学工具、随机数生成和随机过程模拟、Python统计学应用、Python和Excel的集成、Python面向对象编程和GUI的开发、Python与Web技术的集成,以及基于Web应用和Web服务的开发;第3部分关注的是蒙特卡洛模拟期权与衍生品定价实际应用的开发工作,其内容涵盖了估值框架的介绍、金融模型的模拟、衍生品的估值、投资组合的估值、波动率期权等知识。, 《Python金融大数据分析》适合对使用Python进行大数据分析、处理感兴趣的金融行业开发人员阅读。
2021-12-25 08:47:48 47.61MB 大数据 金融python
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