Part 1: Introduction To Data Science & Python for Finance
This is a Virtual Instructor - Led Training (VILT)
The amount of data available to organizations and individuals is unprecedented. Financial services sectors, including securities & investment services and banking, have the most digital data stored per firm on average. Finance companies that want to maximize use of this available data require professionals who have a keen understanding of data science and know how to use it to solve meaningful business challenges.
This two-day, hands-on course provides a structured teaching environment where attendees learn the Python programming language as a powerful tool to conduct robust data analyses on finance-related data sets. At the end of the workshop, course participants will have applied the Python programming language and essential data analysis techniques to practical programming exercises to gain experience solving challenging finance-related problems.
Specific area in finance where data science skills acquired from this course can be effectively applied include: sentiment analysis, advanced time series analysis, risk management, real-time pricing and economic data analysis, customer segmentation analysis, and machine learning algorithm creation for financial technologies.
No prior programming experience required.
- Learn basic foundations in programming and data science
- Receive an overview of state-of-the-art data science and machine learning methods
- Discover how finance professionals can use data science to solve real-world problems
- Understand the advantages of data science and specific analytical methods
- Obtain hands-on Python programming experience
- Understand effective data visualization techniques using Python
- Ability to hit the ground running with executing core data tasks by the end of the course
Who Should Attend?
- Investment professionals
- Quantitative traders
- Event-driven fund managers
- PE/VC investors
- Traditional asset managers
- Fintech entrepreneurs and/or product managers
- FP&A and strategy professionals at corporations
- PE portfolio company operators
- Private wealth managers
Yu is currently a Masters in Computer Science student at Syracuse University and a data scientist at Operam, a marketing technology firm, where he focuses on building AI-driven products for the entertainment industry. He has helped build machine learning attrition models, financial data ETL pipelines, and natural language processing features for technology startups, financial services companies, and large multinationals. Yu has also worked developing server-side data engineering technologies with Python, Java, and Node.JS for a variety of organizations.
Click here for more information on Introduction To Data Science & Python course curriculum
This Virtual Course is Instructed by our training partner, Cognitir