Natural Language Processing for Investment Professionals
Learn to leverage NLP in sentiment analysis for use in investment valuation models and the decision-making process.
About This Course
This course provides a conceptual introduction to natural language processing and its key techniques and terminology. Participants will learn to interact effectively with data science experts in applying natural language processing to their investment decision making. Investment practitioners can then articulate their investment problems to the data science team, use their domain knowledge to effectively source suitable model inputs, and finally interpret model results into appropriate investment actions.
This course is designed those who want to understand natural language processing and is especially suited for the translator role, which is those individuals working with and communicating between both investment and data science teams.
It is suggested, but not required, that learners complete the other three courses in the Data Science for Investment Professionals collection prior to taking this course, to ensure uniform foundational knowledge.
What You’ll Learn
- Describe cleaning and wrangling of text data to make it structured
- Explain data exploration, including exploratory data analysis, feature selection and feature engineering, using text data
- Explain training, evaluating, and tuning natural language processing–based classification models
- Evaluate the performance of such models
- Explain types of applications of natural language processing in investments
- Develop the ability to clean and wrangle text data
- Leave with an understanding of how to conduct data analysis, feature selection, and feature engineering with text data
- Master selecting, training, evaluating, and tuning an NLP model and applications of NLP in investments
- Financial Analysis
Learn data techniques used in machine learning and ways to tell the data story using visualizations in report writing.
Examine modeling frameworks and get familiar with Python and R languages to improve investment decision making.
Gain an understanding of machine learning along with the technical and soft skills needed to use it in the investment process.