Course description
This course offers an intensive hands-on introduction to machine learning for financial data analysis, utilizing Python’s world-leading suite of open-source libraries. Practical case studies using real-world data from tickers to stock indices provide hands-on experience in the Python Jupyter notebook environment.
Delegates will be introduced to supervised and unsupervised statistical and machine learning algorithms in addition to model evaluation and selection techniques.
Upcoming start dates
Suitability - Who should attend?
This course is ideal for financial analysts, business analysts, portfolio analysts, quantitative analysts, risk managers, model validators, quantitative developers and information systems professionals. Attendees should have a good understanding of base Python along with packages such as Pandas.
Outcome / Qualification etc.
Key Learning Outcomes:
- Effectively use Python in a data analysis context to connect to APIs, import, clean, reshape and analyze data
- Describe the Python libraries that are relevant to data analytics and use them fluently
- Critically evaluate and deploy a range of advanced statistical and analytical techniques to extract insights and make predictions from data using the Python programming language
- Leverage modern machine learning techniques when analyzing data in the financial services sector
- Draw informed conclusions that facilitate data driven decisions.
Why choose Fitch Learning
9 in 10 would recommend us to a colleague
Over 1,300 clients worldwide
CPD recognized
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Fitch Learning
Part of the Fitch Group, Fitch Learning partners with clients to enhance knowledge, skills and conduct. With centers in London, New York, Singapore, Dubai and Hong Kong, we are committed to questioning and understanding client needs across the globe and...