Classes begin quarterly in New York
Become a FinTech Professional in 24 Weeks
New York Financial Technology Boot Camp
The field of finance is evolving. Financial services firms, insurance agencies, and investment banks are all increasingly at the intersection of data and technology, harnessing algorithms, machine learning, big data, and blockchain to conduct business.
- Through hands-on classes in a convenient part-time format, Columbia Engineering FinTech Boot Camp gives learners the knowledge they need to move toward the Financial Technology industry.
- With a project-based curriculum, participants gain ample experience with a host of popular tools and methods such as Python programming, financial libraries, machine learning algorithms, Ethereum, and blockchain.
- By tapping into a wide set of career services like resume and portfolio reviews, interview training, and 1:1 coaching, learners get equipped to become Employer Competitive.
Learn Financial Technology and Analysis
Learners will obtain marketable skills, learning how these fundamental concepts are leveraged within financial fields from financial planning to hedge funds, as well as best practices for using these skills to add value to an organization. The competitive curriculum covers*:
- Advanced Excel
- Time-Series Analysis
- Financial Ratios
- Financial Analysis
Blockchain and Cryptocurrency
- Smart Contracts
- Consensus Algorithms
- Distributed Ledger
- Truffle Suite
Machine Learning Applications in Finance
- Algorithmic Trading
- Random Forests
- k-Nearest Neighbors (kNN)
- Support Vector Machines (SVM)
- Linear Regression
- Financial Modeling
- Logistic Regression
- API Interactions
- Amazon Web Services
*The material covered is subject to change. Our academic team adjusts to the market demand.
Gain real-world skills:
Columbia Engineering FinTech Boot Camp takes a multidisciplinary approach to finance, fundamental programming, data analysis, and modern tools in cryptocurrency and blockchain. Learners who complete the boot camp can expect to be able to:
- Model future financial performance of a company using Python and financial fundamentals
- Build an Ethereum blockchain and understand how transactions are validated on a distributed ledger
- Understand both the uses and disadvantages of a variety of machine learning algorithms and their proper application within the field of finance
- Leverage machine learning to determine lending preferences and how effectively a cluster of customers would produce interest