What is quantitative finance?

Quantitative finance is the methodology of applying disciplines from mathematics, statistics, scientific computing, and finance with computer programming to financial trading and investment work. Quantitative finance allows you to build financial models that can be used for pricing a range of financial assets, including financial derivative products. You can also automate trading processes using quantitative finance. Some models even allow for solo trading without a great deal of oversight. This is achieved by setting up risk inputs, and the computer program makes trades within the limits of the risk inputs.

Why is it valuable to learn quantitative finance?

It's valuable to learn quantitative finance as you can become knowledgeable about how to analyze investment decisions while balancing asset allocation with risk management. Having this basic understanding can help you with finances in your professional and personal life. Learning quantitative finance could put you into the major leagues of financial risk management. This knowledge may help you understand the price of securities and financial derivatives. Using concepts of quantitative analysis and quantitative finance can help investors and investment clients develop and make wise decisions about their investment strategies.

What are typical careers that use quantitative finance?

Typical careers that use quantitative finance include financial engineer, quantitative financial analyst, derivatives trader, asset manager, fund manager, and financial risk manager. Learning about quantitative finance might help you find work in commercial banks, investment banks, securities industry companies, wealth management firms, and hedge funds. Experts in quantitative finance are also highly sought by accountancy firms, insurance companies, management consulting firms, and financial software companies.

How can taking online courses on Coursera help me learn quantitative finance?

When you take online courses about quantitative finance on Coursera, you could learn the fundamentals of statistical financial models, including insights into asset pricing, credit risk modeling, investment theory, algorithmic trading, and financial institutions and markets. Some of the online courses are heavily geared to learning derivatives pricing, while others spend more time and substance on machine learning, data science, and object-oriented programming.

검색
Quantitative Finance

필터링 기준

"quantitative finance"에 대한 46개의 결과

Columbia University
Financial Engineering and Risk Management
획득할 기술: Accounting, Algebra, Analysis, Applied Mathematics, Audit, BlockChain, Calculus, Communication, Euler'S Totient Function, FinTech, Finance, General Statistics, Investment Management, Linear Algebra, Machine Learning, Markov Model, Mathematical Theory & Analysis, Mathematics, Probability & Statistics, Probability Distribution, Risk Management
4.4
(67개의 검토)
Intermediate · Specialization
University of Pennsylvania
Finance & Quantitative Modeling for Analysts
획득할 기술: Accounting, Accounts Payable and Receivable, Analysis, Business Analysis, Data Analysis, Data Analysis Software, Design and Product, Entrepreneurship, Finance, Financial Accounting, Financial Analysis, Forecasting, General Accounting, General Statistics, Investment Management, Leadership and Management, Market Research, Marketing, Mathematics, Modeling, Probability & Statistics, Probability Distribution, Product Management, Product Marketing, Regression, Regression Analysis, Research and Design, Sales, Spreadsheet Software, Strategy, Strategy and Operations, Taxes
4.5
(14.4k개의 검토)
Beginner · Specialization
Google Cloud
Machine Learning for Trading
획득할 기술: Artificial Neural Networks, Business Psychology, Cloud Computing, Cloud Platforms, Computer Programming, Entrepreneurship, Finance, General Statistics, Investment Management, Leadership and Management, Machine Learning, Marketing, Mathematics, Modeling, Probability & Statistics, Python Programming, Reinforcement Learning, Risk Management, Sales, Statistical Programming, Strategy, Strategy and Operations, Trading
3.9
(939개의 검토)
Intermediate · Specialization
EDHEC Business School
Investment Management with Python and Machine Learning
획득할 기술: Accounting, Applied Machine Learning, Business Analysis, Computer Programming, Data Analysis, Data Mining, Finance, Financial Analysis, Investment, Investment Management, Leadership and Management, Machine Learning, Machine Learning Algorithms, Markov Model, Natural Language Processing, Probability & Statistics, Python Programming, Regression, Risk, Risk Management, Statistical Analysis, Statistical Programming
4.6
(1.5k개의 검토)
Beginner · Specialization
University of Pennsylvania
Fundamentals of Quantitative Modeling
획득할 기술: Marketing, Regression Analysis, Strategy, Sales, General Statistics, Leadership and Management, Mathematics, Modeling, Market Research, Entrepreneurship, Regression, Product Marketing, Linear Regression, Probability Distribution, Probability & Statistics, Analysis, Design and Product, Probability, Product Management, Research and Design, Strategy and Operations, Linearity
4.6
(8k개의 검토)
Mixed · Course
University of Pennsylvania
Decision-Making and Scenarios
획득할 기술: Present Value, Balance Sheet, Corporate Accouting, Flow Network, Financial Analysis, Project, Accounting, Entrepreneurship, Cash Management, Accounts Payable and Receivable, Business Analysis, Financial Accounting, Decision Making, Data Analysis, Analysis, Cash Flow, Financial Statement, Leadership and Management
4.6
(1.7k개의 검토)
Mixed · Course
무료
Erasmus University Rotterdam
Advanced Valuation and Strategy - M&A, Private Equity, and Venture Capital
획득할 기술: Data Analysis, Sales, Financial Analysis, Mathematics, Mergers & Acquisitions, Accounting, Strategy, Decision Making, Leadership and Management, Strategy and Operations, Entrepreneurship, Business Analysis, Finance, Investment Management, Marketing, Game Theory
4.6
(1k개의 검토)
Advanced · Course
University of Pennsylvania
Modeling Risk and Realities
획득할 기술: Basic Descriptive Statistics, Risk Management, Data Model, General Statistics, Statistical Tests, Probability, Data Analysis, Business Analysis, Finance, Research and Design, Correlation And Dependence, Leadership and Management, Problem Solving, Entrepreneurship, Microsoft Excel, Analysis, Mathematical Optimization, Data Management, Statistical Analysis, Probability & Statistics, Simulation, Probability Distribution, Risk, Modeling, Spreadsheet Software
4.6
(2.1k개의 검토)
Mixed · Course

quantitative finance과(와) 관련된 검색

finance & quantitative modeling for analysts

요약하자면, 여기에 가장 인기 있는 quantitative finance 강좌 10개가 있습니다.

Financial Engineering and Risk Management: Columbia University
Finance & Quantitative Modeling for Analysts: University of Pennsylvania
Machine Learning for Trading: Google Cloud
Investment Management with Python and Machine Learning: EDHEC Business School
Introduction to Financial Engineering and Risk Management: Columbia University
Fundamentals of Quantitative Modeling: University of Pennsylvania
Investment and Portfolio Management: Rice University
Compare Stock Returns with Google Sheets: Coursera Project Network
Using Machine Learning in Trading and Finance: New York Institute of Finance
Decision-Making and Scenarios: University of Pennsylvania

Finance에서 학습할 수 있는 스킬

양적 재무에 대한 자주 묻는 질문

Quantitative finance is the methodology of applying disciplines from mathematics, statistics, scientific computing, and finance with computer programming to financial trading and investment work. Quantitative finance allows you to build financial models that can be used for pricing a range of financial assets, including financial derivative products. You can also automate trading processes using quantitative finance. Some models even allow for solo trading without a great deal of oversight. This is achieved by setting up risk inputs, and the computer program makes trades within the limits of the risk inputs.‎
It's valuable to learn quantitative finance as you can become knowledgeable about how to analyze investment decisions while balancing asset allocation with risk management. Having this basic understanding can help you with finances in your professional and personal life. Learning quantitative finance could put you into the major leagues of financial risk management. This knowledge may help you understand the price of securities and financial derivatives. Using concepts of quantitative analysis and quantitative finance can help investors and investment clients develop and make wise decisions about their investment strategies.‎
Typical careers that use quantitative finance include financial engineer, quantitative financial analyst, derivatives trader, asset manager, fund manager, and financial risk manager. Learning about quantitative finance might help you find work in commercial banks, investment banks, securities industry companies, wealth management firms, and hedge funds. Experts in quantitative finance are also highly sought by accountancy firms, insurance companies, management consulting firms, and financial software companies.‎
When you take online courses about quantitative finance on Coursera, you could learn the fundamentals of statistical financial models, including insights into asset pricing, credit risk modeling, investment theory, algorithmic trading, and financial institutions and markets. Some of the online courses are heavily geared to learning derivatives pricing, while others spend more time and substance on machine learning, data science, and object-oriented programming.‎