PyFi - Learn Python for Finance

Excel is No Longer Enough. Learn Python for Finance in 30 Days or Less. Guaranteed.

Increase the Efficiency, Effectiveness, and Accuracy of Your Work.

*These banks have used one or both of PyFi’s self-study courses to introduce their new employees to Python programming and its application in the world of finance.

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Instructor Support
Trusted by Top Global Banks
Award Winning Algorithms
No Coding Experience Necessary
30-day Money Back Guarantee
3k+ Professionals Trained
10k+ Training Hours Completed

*These banks have used one or both of PyFi’s self-study courses to introduce their new employees to Python programming and its application in the world of finance.

Lifetime access
Instructor Support
Trusted by Top Global Banks
Award Winning Algorithms
No Coding Experience Necessary
30-day Money Back Guarantee
3k+ Professionals Trained
10k+ Training Hours Completed

Excel is slowing you down. Python can help.

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Scalability Limitations
Excel struggles to handle large datasets, especially when they exceed a few hundred thousand rows. This can lead to performance slowdowns, crashes, or file corruption.
Error-Prone
Manual data entry and formula creation are prone to human errors, which can lead to significant miscalculations or inaccuracies in financial analysis and reporting.
Version Control
Collaboration can become chaotic as multiple team members work on different versions of a spreadsheet, leading to inconsistencies and confusion about the most up-to-date file.
Lack of Automation
While Excel has some automation capabilities (e.g., macros, VBA), it is limited compared to more advanced programming languages like Python, and setting up complex automation can be cumbersome and time-consuming.
Limited Integration
Excel doesn’t integrate seamlessly with more advanced systems, databases, or external APIs without additional tools or manual intervention. This can cause delays in getting real-time data.
Inefficient for Complex Calculations
As financial analysis becomes more complex, using Excel formulas and functions becomes inefficient and harder to debug. Python and other coding languages offer more efficient ways to perform complex modeling and calculations.

Scalability
Python, along with libraries like pandas and NumPy, is designed to handle large datasets efficiently. It can process millions of rows without the slowdowns typically seen in Excel.
Minimizing Errors
Python reduces the risk of manual errors by automating repetitive tasks. Once written, scripts can be reused and easily adjusted, ensuring consistency. Instead of manually typing formulas, Python’s logic and libraries eliminate human error and offer robust error-checking.
Testing and Debugging
Python has a variety of built-in tools (e.g., pytest, pdb) that help test, debug, and validate financial models and calculations.
Version Control
Python code can be version-controlled using tools like Git, making collaboration easier. Multiple team members can work on the same project without worrying about conflicting file versions, and they can track every change made to the codebase.
Collaboration
Tools like Jupyter Notebooks allow teams to share code, data, and visualizations interactively. These notebooks can include markdown cells for explanations and documentation, helping finance teams work together more smoothly.
Automating Workflows
Python can automate repetitive Excel tasks, such as updating reports, generating forecasts, or pulling data from APIs. Libraries like openpyxl and xlwings allow users to automate Excel directly from Python, reducing the need for complex and error-prone VBA macros.
Real-Time Data Access
Python can pull live data from APIs or databases, automate data cleaning, and transform it into meaningful reports. This eliminates the need for manual data entry and updates.
Structured Workflows
Python encourages modular, reusable code, which promotes good practices like code documentation and auditing. It’s easier to maintain transparency and track changes compared to Excel, where tracking changes in formulas or data can be opaque.
Complex Calculations
Python is better suited for complex financial modeling and calculations. Libraries like NumPy, SciPy, and SymPy allow for complex numerical methods, statistical analysis, and symbolic computation, far beyond Excel’s formula capabilities.
Monte Carlo Simulations and Optimizations
Python can handle advanced mathematical techniques such as Monte Carlo simulations, linear programming, and optimization with libraries like SciPy, PuLP, and CVXPY. These techniques are difficult to implement and inefficient in Excel.
Dynamic, Automated Reports
Python can generate fully automated reports that update in real-time, pulling data from external sources and producing results much faster than Excel. Reports can include interactive visualizations using Matplotlib, Seaborn, or Plotly.

Meet Zach Washam,
PyFi Founder & Head of Instruction

While learning Python as an investment banker, Zach made an interesting observation: Python programming had a lot in common with the Excel models he made at work. By thinking of Python like Excel, Zach quickly learned the coding language and invented Wells Fargo Securities' first machine learning algorithm for investment banking and capital markets. ‍

After submitting two algorithms for patent protection and winning Wells Fargo's 2018 "Local Sphere Innovation Award," Zach left investment banking to launch PyFi. ‍

Now, Zach’s courses have been delivered to thousands of finance students and professionals around the world.

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The PyFi Guarantee

We offer a 100%, no questions asked, 30 day money-back guarantee to completely de-risk your investment. Enroll now, and if for any reason you aren't completely satisfied, send us an email and you'll get a full refund for up to one month after enrollment.

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Learn the same algorithms that were used to advise the following companies:

*Slight changes to the code made to protect IP

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Completion Certificates

After completing each course and passing the certification exam, students will be granted a PyFi Certification for that specific course. Use these certificates as a signal to employers that you have the technical skills to immediately add value to your team.

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What Our Customers Are Saying:

"An incredible strength of Machine Learning Edge is the real world examples it brings into play. These are not some hypothetical applications of the material that are taken out of a textbook; these are real world, concrete, and ready for application in the student’s career. Every piece of information you are given is valuable and ready to be put to use."

Gage C.

Mathematics Undergraduate Student

"Machine Learning Edge is a fantastic introduction to machine learning applications of finance. It provides you with an excellent road map all the way from python basics to advanced machine learning algorithms. I constantly see new ways to apply what I have learned to projects in the workplace every day.

Will N.

Investment Banking Analyst

"If you are in the finance industry, or even a student aspiring to break in, Machine Learning Edge is absolutely essential. It is no secret that coding is the literacy of today, and MLE goes above and beyond in equipping you with the coding and machine learning knowledge increasingly demanded in the finance industry."

Ralph R.

Investment Banking Analyst

"Machine Learning Edge content is carefully curated to maximize retention and effectively bridges the gap between developed financial and data science skill-sets. Wall Street, meet Silicon Valley."

Jack V.

Debt Capital Markets Analyst

"I was very impressed with the course. As a buy-side investment professional, I can definitely see how this course could be used to not only make oneself more marketable, but to actually improve investment decisions and performance."

Brad B.

Private Equity Associate

"Machine Learning Edge is a rare find because it is tailored for busy finance professionals.As an analyst at a major investment bank, I was able to take several Python and Machine Learning techniques from this course and use them to improve relevant projects."

Terrence Z.

Investment Banking Analyst

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*Strike price reflects relative value and not msrp.

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Excel is No Longer Enough. Learn Python for Finance in 30 Days or Less. Guaranteed.

Excel is slowing you down. Python can help.

Excel

Python

Meet Zach Washam,
PyFi Founder & Head of Instruction

The PyFi Guarantee

Learn the same algorithms that were used to advise the following companies:

Completion Certificates

What Our Customers Are Saying:

Pricing Plan

Python Fundamentals

Applied Machine Learning

Machine Learning Edge Professional Certification Bundle

Country/region

Excel is No Longer Enough. Learn Python for Finance in 30 Days or Less. Guaranteed.

Excel is slowing you down. Python can help.

Excel

Python

Meet Zach Washam, PyFi Founder & Head of Instruction

The PyFi Guarantee

Learn the same algorithms that were used to advise the following companies:

Completion Certificates

What Our Customers Are Saying:

Pricing Plan

Python Fundamentals

Applied Machine Learning

Machine Learning Edge Professional Certification Bundle

Meet Zach Washam,
PyFi Founder & Head of Instruction