Collaborate, learn, and interact with an instructor that is teaching in a real-time, virtual environment. All participants will interact via video conference call. Price includes:
Course Goals and Overview:
This hands-on data science course is a sequel to the Introduction to Data Science for Finance workshop. Advanced Data Science for Finance will provide an overview of modern machine learning algorithms that analysts, portfolio managers, traders and chief investment officers should understand and in a context that goes beyond a broader level introductory class in data science. Classification methods are touched upon in the introduction course, but the Advanced Data Science for Finance course focuses exclusively on this highly demanded and rapidly adopted segment of data science and machine learning.
This course will explore advanced classification methods including neural networks and decision trees which are among the most effective data science techniques. This workshop also provides an introduction to deep learning, a technique which has significantly increased the performance of machine learning algorithms over the last years and is heavily used in the financial services industry. Deep learning utilizes algorithms and methods that perform in a similar manner to the human brain. According to Gartner, 80% of data scientists will be competent in deep learning and deep learning will be utilized in a much larger role in different forms of predictive analytics across all functional areas of business including finance and markets.
At the end of the workshop, participants will be comfortable applying the Python programming language to build common classification algorithms and evaluate & interpret their accuracies in the context of finance.
- An overview and specific focus on core classification methods and how to use them to solve real-world problems in the finance industry.
- Aims to provide attendees with a high level understanding and working knowledge of highly coveted artificial intelligence areas including deep learning and neural networks and their direct application to the field of financial analysis and capital markets.
- Provide attendees that work in the finance industry with the ability to evaluate and select from a variety of classification methods and tools as these techniques continue to be adapted and implemented at an ever increasing rate.
- Further and more advanced hands-on Python programming experience beyond the introduction course.
Who Should Attend:
- Individuals working with or needing to understand machine-learning algorithms, specifically classification methods.
- Graduates of the Introduction to Data Science for Finance course. The introduction course or equivalent is a prerequisite.