Data Science for Investment Professionals Certificate
Gain practical understanding of data science and machine learning applications in the investment process.
About This Certificate
Are you ready for the machine learning revolution? The Data Science for Investment Professionals Certificate provides you with practical knowledge of data techniques and machine learning fundamentals and how they are used in the investment process. This certificate is designed to enable you to apply machine learning concepts to real-world investment problems and explain them clearly to a non-expert audience and clients. You will have the opportunity to gain practical experience through immersive code labs that draw on real-world scenarios.
The certificate is composed of five courses with practical application exercises and one final assessment.
The courses and final assessment are recommended to be completed in the order that they appear. Course descriptions are provided below or you can view the full syllabus (PDF).
Understand foundational data science techniques used in machine learning and ways to present data using visualizations in report writing.View Course Details
Examine modeling frameworks and get familiar with Python and R languages to improve investment decision making.View Course Details
Gain an understanding of machine learning along with the technical and soft skills needed to use it in the investment process.View Course Details
Learn to leverage NLP in sentiment analysis for use in investment valuation models and the decision-making process.View Course Details
Learn how to take apart a machine learning model, identify potential biases and take measures to mitigate them.View Course Details
Available May 2023. The final assessment will be an online, unproctored assessment consisting of 60 multiple choice questions. You will have 2 attempts to complete the assessment.
No prerequisites are required for this certificate.
What You’ll Learn
Upon completion of this certificate you will be able to:
- Describe and evaluate machine learning techniques
- Select and develop data visualizations using Python
- Apply machine learning to address investment problems
- Explain machine learning techniques to a non-expert audience
- Use natural language processing to make investment decisions
- Evaluate and mitigate machine learning biases
- Quantitative Methods