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 for suitable model inputs, and finally interpret the model results into appropriate investment actions.
This course is designed for 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.
During this online course, you will have the opportunity to gain practical experience through immersive code labs that draw on real-world scenarios.
Course modules include:
- Module 1: Cleaning and Wrangling Text Data
- Module 2: Exploratory Data Analysis, Feature Selection and Feature Engineering with Text Data
- Module 3: Selecting, Training, Evaluating, and Tuning a Natural Language Processing Model
- Module 4: Developing an NLP Model for Predicting Sentiment of Financial Text
- Module 5: Applications of NLP in Investments
This course is part of the Data Science for Investment Professionals Certificate.
This course is part of a data science series. It is suggested, but not required, that learners complete the courses in the recommended order below to ensure uniform foundational knowledge.
- Data and Statistics Foundation for Investment Professionals
- Statistics for Machine Learning for Investment Professionals
- Machine Learning for Investment Professionals
- Natural Language Processing for Investment Professionals
- Mitigating Biases in the Data Science Pipeline for Investment Professionals
What You’ll Learn
Upon completion of this online course you will be able to:
- 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
- Quantitative Methods