课程信息

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中级

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

完成时间大约为11 小时

建议:4 weeks of study, 2 hours per week...

英语(English)

字幕:英语(English)

您将学到的内容有

  • Learn what alternative data is and how it is used in financial market applications. 

  • Become immersed in current academic and practitioner state-of-the-art research pertaining to alternative data applications.

  • Perform data analysis of real-world alternative datasets using Python.

  • Gain an understanding and hands-on experience in data analytics, visualization and quantitative modeling applied to alternative data in finance

您将获得的技能

Advanced vizualisationBasics of consuption-based alternative dataText mining methodologiesWeb-scritpting tools

可分享的证书

完成后获得证书

100% 在线

立即开始,按照自己的计划学习。

可灵活调整截止日期

根据您的日程表重置截止日期。

中级

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

完成时间大约为11 小时

建议:4 weeks of study, 2 hours per week...

英语(English)

字幕:英语(English)

教学大纲 - 您将从这门课程中学到什么

1

1

完成时间为 5 小时

Consumption

完成时间为 5 小时
10 个视频 (总计 74 分钟), 5 个阅读材料, 1 个测验
10 个视频
What is consumption data?8分钟
Geolocation and foot-traffic5分钟
Lab session: Introduction to the Uber Dataset6分钟
Lab session: Points of Interest5分钟
Lab session: Mapping Data with Folium9分钟
Lab session: Testing Seasonality11分钟
Application: Consumption data and earning surprises7分钟
Application:Consumption-based proxies for private information and managers behavior7分钟
Application: Additional applications of consumption data7分钟
5 个阅读材料
Material at your disposal5分钟
Note about HeatMapWithTime2分钟
Extra materials on consumption1小时
Additional resources on the interest of real-time corporate sales'measures1小时
Additional resources on Predicting Performance using Consumer Big Data1小时
1 个练习
Graded Quiz on Consumption30分钟
2

2

完成时间为 3 小时

Textual Analysis for Financial Applications

完成时间为 3 小时
8 个视频 (总计 75 分钟), 2 个阅读材料, 1 个测验
8 个视频
Introduction to textual analysis3分钟
Processing text into vectors12分钟
Normalizing textual data5分钟
Lab session: Introduction to Webscraping11分钟
Lab session: Applied Text Data Processing11分钟
Lab session: Company Distances and Industry Distances15分钟
Application: applying similarity analysis on corporate filings to predict returns9分钟
2 个阅读材料
Extra materials on Textual Analysis for Financial Applications1 小时 10 分
Additional resources on textual analysis for financial applications1小时
1 个练习
Graded Quiz on Textual Analysis for Financial Applications
3

3

完成时间为 4 小时

Processing Corporate Filings

完成时间为 4 小时
8 个视频 (总计 69 分钟), 4 个阅读材料, 1 个测验
8 个视频
Lab session: Working with 10-K Data7分钟
Lab session: Applications of TF-IDF11分钟
Lab session: Risk Analysis9分钟
Lab session: Working with 13-F Data10分钟
Lab session: Comparing Holding Similarities11分钟
Application: network centrality, competition links and stock returns8分钟
Application: Using location data to measure home bias to predict returns4分钟
4 个阅读材料
Instructor's announcement2分钟
Extra materials on Processing Corporate Filings30分钟
Additional resources30分钟
Additional resources on processing corporate fillings1 小时 15 分
1 个练习
Graded Quiz on Processing Corporate Filings
4

4

完成时间为 7 小时

Using Media-Derived Data

完成时间为 7 小时
7 个视频 (总计 62 分钟), 4 个阅读材料, 1 个测验
7 个视频
Sentiment Analysis6分钟
Lab session: Twitter Dataset Introduction10分钟
Lab session: Network Visualization4分钟
Lab session: Replicating PageRank12分钟
Lab session: Applied Sentiment Analysis7分钟
Application: Using media to predict financial market variables10分钟
4 个阅读材料
Additional resources1小时
Additional resources1 小时 15 分
Extra materials on Using Media-Derived Data1 小时 10 分
Additional resources on using media derived-data2 小时 30 分
1 个练习
Graded Quiz on Using Media-Derived Data

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关于 Investment Management with Python and Machine Learning 专项课程

The Data Science and Machine Learning for Asset Management Specialization has been designed to deliver a broad and comprehensive introduction to modern methods in Investment Management, with a particular emphasis on the use of data science and machine learning techniques to improve investment decisions.By the end of this specialization, you will have acquired the tools required for making sound investment decisions, with an emphasis not only on the foundational theory and underlying concepts, but also on practical applications and implementation. Instead of merely explaining the science, we help you build on that foundation in a practical manner, with an emphasis on the hands-on implementation of those ideas in the Python programming language through a series of dedicated lab sessions....
Investment Management with Python and Machine Learning

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