Python for Finance: Investment Fundamentals & Data Analytics
Data scientists and investment advisors alike appreciate Python as a powerful yet flexible langiage. If you’re eager to learn Python and level up your career in financial data, you need this course!
About this course
The Python programming language allows you to build complex mathematical applications. That’s why it’s ideal for investment advisors who need to perform precise analyses and predictions for financial portfolios. Developers and data scientists who want to break into the financial industry absolutely must know Python — and advisors can benefit from learning Python as well!
Learn the foundation skills of Python coding. No prior experience is required! Then, put that knowledge to work, building applications that predict asset growth, assess risk and reward, and balance portfolios. Everything is taught with an easy-to-understand overview, then you get a chance to practice your skills with hands-on projects.
All who wish to use Python to grow their careers, implement advanced analytics, and improve their financial predictions are invited to join the course to gain a working knowledge of this powerful programming language. You’ll leave with the confidence and competence to use Python across a broad range of financial use cases!
What you’ll learn
This course provides a complete overview of Python programming and how to apply your new coding skills in finance applications. You will also learn the core principles of data science in the finance industry, how to write Python applications, and the many ways Python can help you optimize investment portfolios.
Topics include:
- The foundations of Python: conditional statements, functions, sequences, and loops
- The mathematical concepts behind portfolio optimization and how Python puts them into action
- Calculating risk and reward of investment portfolios
- Performing regression analyses and Monte Carlo simulations using Python
- Analyzing data sets and plotting graphs
- Crucial financial formulas including Black Scholes and the Sharpe ratio, and how to use Python to calculate them
- Tactics for applying financial knowledge in data science or programming jobs, and vice versa
- Case studies illustrating all concepts and potential use cases of Python in finance
Who is it for?
This course’s scope is flexible enough to provide Python developers, data scientists, and investment experts with cross-functional knowledge and practical skills development. It’s perfect for:
- Aspiring and current data scientists who want to expand their expertise
- Budding programmers who need to develop their technical skills
- Programmers who want to enter the finance industry or become more competitive in their skillset
- Financial experts who are eager to apply their knowledge in practical applications
Frequently Asked Questions
What is a short course?
With a short course, you’ll typically deep-dive into a specific area of interest within a broader topic.
Short courses are typically between 1 and 12 hours in duration, and in the case of guided courses (i.e. with live instructors), are sometimes spread over a number of days or weeks. Most short courses are self-paced, which means you progress through a series of videos and projects at your own pace.
Depending on the institution and the platform, you may also get a certificate of completion that you can add to your LinkedIn profile.
Is this really 100% online?
This course is completely online, so you can study from anywhere! All you’ll need is a device with an internet connection, such as a computer or a smartphone. If the course has any live, instructor-led sessions, you may need a microphone and possibly a webcam to fully participate. The instructor will make you aware of this beforehand.