課程信息

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中級

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

完成時間大約為19 小時
英語(English)
字幕:英語(English)

您將學到的內容有

  • 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

您將獲得的技能

Advanced vizualisationBasics of consuption-based alternative dataText mining methodologiesWeb-scritpting tools
可分享的證書
完成後獲得證書
100% 在線
立即開始,按照自己的計劃學習。
可靈活調整截止日期
根據您的日程表重置截止日期。
中級

Python programming (beginners)

Investment theory (recommended)

Statistics (recommended)

完成時間大約為19 小時
英語(English)
字幕:英語(English)

提供方

北方高等商学院 徽標

北方高等商学院

教學大綱 - 您將從這門課程中學到什麼

1

1

完成時間為 5 小時

Consumption

完成時間為 5 小時
10 個視頻 (總計 74 分鐘), 5 個閱讀材料, 1 個測驗
10 個視頻
What is consumption data?8分鐘
Geolocation and foot-traffic5分鐘
Lab session: Introduction to the Uber Dataset6分鐘
Lab session: Points of Interest5分鐘
Lab session: Mapping Data with Folium9分鐘
Lab session: Testing Seasonality11分鐘
Application: Consumption data and earning surprises7分鐘
Application:Consumption-based proxies for private information and managers behavior7分鐘
Application: Additional applications of consumption data7分鐘
5 個閱讀材料
Material at your disposal5分鐘
Note about HeatMapWithTime2分鐘
Extra materials on consumption1小時
Additional resources on the interest of real-time corporate sales'measures1小時
Additional resources on Predicting Performance using Consumer Big Data1小時
1 個練習
Graded Quiz on Consumption30分鐘
2

2

完成時間為 3 小時

Textual Analysis for Financial Applications

完成時間為 3 小時
8 個視頻 (總計 75 分鐘), 2 個閱讀材料, 1 個測驗
8 個視頻
Introduction to textual analysis3分鐘
Processing text into vectors12分鐘
Normalizing textual data5分鐘
Lab session: Introduction to Webscraping11分鐘
Lab session: Applied Text Data Processing11分鐘
Lab session: Company Distances and Industry Distances15分鐘
Application: applying similarity analysis on corporate filings to predict returns9分鐘
2 個閱讀材料
Extra materials on Textual Analysis for Financial Applications1 小時 10 分
Additional resources on textual analysis for financial applications1小時
1 個練習
Graded Quiz on Textual Analysis for Financial Applications
3

3

完成時間為 4 小時

Processing Corporate Filings

完成時間為 4 小時
8 個視頻 (總計 69 分鐘), 4 個閱讀材料, 1 個測驗
8 個視頻
Lab session: Working with 10-K Data7分鐘
Lab session: Applications of TF-IDF11分鐘
Lab session: Risk Analysis9分鐘
Lab session: Working with 13-F Data10分鐘
Lab session: Comparing Holding Similarities11分鐘
Application: network centrality, competition links and stock returns8分鐘
Application: Using location data to measure home bias to predict returns4分鐘
4 個閱讀材料
Instructor's announcement2分鐘
Extra materials on Processing Corporate Filings30分鐘
Additional resources30分鐘
Additional resources on processing corporate fillings1 小時 15 分
1 個練習
Graded Quiz on Processing Corporate Filings
4

4

完成時間為 7 小時

Using Media-Derived Data

完成時間為 7 小時
7 個視頻 (總計 62 分鐘), 5 個閱讀材料, 1 個測驗
7 個視頻
Sentiment Analysis6分鐘
Lab session: Twitter Dataset Introduction10分鐘
Lab session: Network Visualization4分鐘
Lab session: Replicating PageRank12分鐘
Lab session: Applied Sentiment Analysis7分鐘
Application: Using media to predict financial market variables10分鐘
5 個閱讀材料
Additional resources1小時
Additional resources1 小時 15 分
Extra materials on Using Media-Derived Data1 小時 10 分
Additional resources on using media derived-data2 小時 30 分
Data recap10分鐘
1 個練習
Graded Quiz on Using Media-Derived Data

審閱

來自PYTHON AND MACHINE-LEARNING FOR ASSET MANAGEMENT WITH ALTERNATIVE DATA SETS的熱門評論

查看所有評論

關於 Investment Management with Python and Machine Learning 專項課程

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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