Python and Statistics for Financial Analysis

所在平台: CourseraArchive

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大学或机构: CourseraNew

课程主页: https://www.coursera.org/archive/python-statistics-financial-analysis

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课程大纲

Visualizing and Munging Stock Data
Random variables and distribution
Sampling and Inference
Linear Regression Models for Financial Analysis

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Course Overview: https://youtu.be/JgFV5qzAYno Python is now becoming the number 1 programming language for data science. Due to python’s simplicity and high readability, it is gaining its importance in the financial industry. The course combines both python coding and statistical concepts and applies into analyzing financial data, such as stock data. By the end of the course, you can achieve the following using python: - Import, pre-process, save and visualize financial data into pandas Dataframe - Manipulate the existing financial data by generating new variables using multiple columns - Recall and apply the important statistical concepts (random variable, frequency, distribution, population and sample, confidence interval, linear regression, etc. ) into financial contexts - Build a trading model using multiple linear regression model - Evaluate the performance of the trading model using different investment indicators Jupyter Notebook environment is configured in the course platform for practicing python coding without installing any client applications.

用于财务分析的Python和统计学:课程概述:https://youtu.be/JgFV5qzAYno Python现在正成为数据科学中排名第一的编程语言。由于python的简单性和高可读性,它在金融行业中正变得越来越重要。该课程结合了python编码和统计概念,并适用于分析财务数据,例如股票数据。 在课程结束时,您可以使用python实现以下目的: -将财务数据导入,预处理,保存和可视化到熊猫数据框 -通过使用多列生成新变量来处理现有财务数据 -回顾重要的统计概念并将其应用于财务环境(随机变量,频率,分布,总体和样本,置信区间,线性回归等) -使用多重线性回归模型建立交易模型 -使用不同的投资指标评估交易模型的绩效 在课程平台中配置了Jupyter Notebook环境,可在不安装任何客户端应用程序的情况下练习python编码。

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