*These banks have used one or both of PyFi’s self-study courses to introduce their employees to Python and its application in the world of finance.
Why is Python important in Finance?
These are just a few examples of how you can use Python in finance
Financial Reporting
Credit Risk Reporting
Port Company Performance
Python scripts integrate SAP Analytics Cloud with Tableau by leveraging the SAP Analytics Cloud OData API. Python fetches real-time financial data, cleans it, and sends it to Tableau for visualization. This integration reduces reporting cycle time by an estimated 40% while ensuring accurate and up-to-date dashboards.
Extract financial and bond data from Bloomberg and automatically calculate key financial ratios like debt-to-equity and interest coverage. Machine learning models predict default probabilities. The models are validated for accuracy and risk scores and probability metrics are exported to Excel for presentation. Save approximately 15 hours while improving the accuracy of your forecasts by 25% with this Python integration.
Python automates the consolidation of financial data from portfolio companies, reducing manual aggregation and saving approximately 6 hours per month. IT streamlines KPI calculations, eliminating Excel formula errors and improves accuracy by 30%. Automated benchmarking and real-time alerts highlight underperformers, cutting approx. 4 hours of manual comparisons. Dynamic dashboards in Tableau replace static Excel chats, saving an additional 3 hours per month. Overall, this integration saves 13 hours monthly and improves accuracy by 30%.
Unsure if Python, or PyFi is right for you?
Hear from PyFi students. Be Certain in your decision.
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.
PyFi's Unique Approach
Why Top Banks In North America Use PyFi
Python Fundamentals
Applied Machine Learning
Orientation
Get setup in your coding environment and start writing your first lines of code in under 30 minutes. Setup your environment and quickly understand how to command basic functions that you’ll build on throughout the course.
Build on your existing foundation with excel analogs
By relating lessons and techniques to concepts finance professionals are already familiar with, PyFi courses deliver higher retention rates and a more efficient learning process. You are already a logical thinker and problem solver which is demonstrated by your work in Excel. PyFi builds off this foundation.
Practical application
The Python programming language is an immense topic to cover. There are many things you can learn and even more ways you can apply Python which are not relevant to you as a finance professional. PyFi teaches you what you need to know to be effective in your work without needing to pursue a formal computer science degree.
Apply proven solutions to your work
Because Python is open source, you’re able to apply working libraries, or frameworks, to your work with simple commands. Imagine downloading sophisticated financial models you could simply plug your data into. That’s exactly what’s possible with the Python programming language and PyFi gets you started by plugging the NumPy and Pandas library directly into your financial analysis.
Prepare Your Data
If you want a great machine learning algorithm, you need great training data. Correct errors, eliminate sparse classes, and remove unwanted observations to provide your algorithm premium fuel.
Build Your Pipelines
Model pipelines tell your algorithm how to process your data. Construct pipelines that standardize your data to a common scale, define competing model classes, and specify random states.
Train & Tune Your Algorithm
Optimizing your machine learning algorithm is like tuning a race car. Train competing models and tune your hyperparameters using cross-validation to maintain the integrity of your testing data.
Select the Winning Model
Your algorithm competes against itself to produce optimal results. After training and tuning your models, you can select the winning model and use it to make superior predictions.
Objectives
Learn how to apply Real-World Algorithms in finance.
Python Fundamentals
Applied Machine Learning
Become familiar and comfortable with coding envrionments
- Use Jupyter Notebook to write and execute code
- Perform calculations
- Generate outputs with static text and dynamic values
- Create and manipulate variables
Identify common types of Python objects
- Create and manipulate important data structures
- Lists, Tuples, Sets, and Dictionaries
Use python arguments
- Create custom functions
- Repeat tasks through iterable objects using for loops
- Incorporate conditional logic using if statements
Become familiar with Python libraries starting with NumPy
- Create and manipulate NumPy arrays, a new type of Python object
- Perform special NumPy array math and aggregation functions
- Leverage NumPy's useful randomization tools
Become familiar with the Pandas library
- Create and manipulate Pandas DataFrames
- Create and manipulate Pandas Series
- Filter data using boolean masks
- Segment and summarize data by class with groupbys
Identify overfit regression models
Master the skill of distinguishing overfit models that promise more than they deliver. By understanding the nuances of overfitting, you will be equipped to refine your models to ensure they perform reliably in real-world financial scenarios, enhancing their predictability and your confidence in their forecasts.
Compare different Machine Learning models
Dive deep into the world of regularized regression and decision tree ensemble algorithms. This course highlights the strengths and limitations of each, guiding you to make informed decisions on which algorithm to deploy for your financial modeling tasks, ensuring optimal balance and performance.
Explain the confusion matrix and its relation to the ROC curve
Gain clarity on evaluating model performance with an in-depth understanding of the confusion matrix and its critical role in financial predictions. Learn how it intertwines with the ROC curve to give you a comprehensive view of your model's accuracy, allowing you to enhance predictive models with precision.
Construct training data sets, testing data sets, and model pipelines
Learn to construct robust training and testing datasets and streamline your model development process with efficient pipelines. This skill set is crucial for developing models that are not only accurate but also scalable and adaptable to the fast-paced changes in the finance industry.
Perform advanced data cleaning, exploration, and visualization
Elevate your data preparation skills with advanced techniques in cleaning, exploring, and visualizing financial datasets. These foundational steps are key to uncovering insights and trends that drive successful machine learning models, setting the stage for impactful financial analysis and predictions.
Engineer features based on conditional relationships between existing features
Unlock the potential of your financial datasets by learning how to engineer features that reveal deeper insights. Understand the conditional relationships between features to create new, powerful predictors that enhance the performance of your machine learning models in predicting market movements and trends.
Build and finalize a machine learning classifier, and so much more…
Bring your financial prediction models across the finish line by mastering the art of building and finalizing machine learning classifiers. This course empowers you to not only construct but also fine-tune classifiers that stand up to the rigors of the finance world, ensuring your models are both accurate and resilient under market pressures.
Learn the same algorithms that were used to advise the following companies:
*Slight changes to the code made to protect IP
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.
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.
*These banks have used one or both of PyFi’s self-study courses to introduce their employees to Python and its application in the world of finance.
How Can We Help You?
What makes PyFi courses unique?
PyFi's Python training is custom-tailored for finance professionals with minimal to no coding experience.
We teach Python by building off your existing Excel skill set and focus on concepts relevant to you. We're not building rocketships, we're teaching you how to work through finance workflows.
With instructor support, and the most relevant concepts, PyFi is the market's best choice for someone in finance, who has decided to learn Python.
How will learning Python help my finance career?
Python is customizable and works with your current tech stack, and your next positions tech stack.
Because its free to use, and the most popular coding language in finance, the long tail effects of learning Python are only going to compound the return on your investment.
In the immediate term, you can plug into your current workflows involving Excel, BI tools, Data providers, etc., to eliminate redundant steps while also improving the accuracy of your financial models.
Imagine cutting down your hours by 30, or 50%, and bringing your stakeholders models that are twice as accurate as what your contemporaries can produce in Excel.
So what happens to the trajectory of your finance career when you're that much more productive than your colleagues?
Whats included in the Professional Certification Bundle?
Both PyFi self study courses, Python Fundamentals, and Applied Machine Learning are included. You will also receive access to The Machine Learning Edge Blueprint, Vol I and Vol II which are the complimentary PDF versions of each course.
As you progress through both courses you can receive instructor support via the comment section under each video lesson.
At the end of each course, you'll be invited to take a rigorous certification exam to test your knowledge and ensure retention.
How long does it take to complete a course?
Both Python Fundamentals and Applied Machine Learning are approximately 3.5 hours in length each. A student can generally complete each course in 6-10 hours when working through all of the exercises. Your enrollment comes with lifetime access and support within the course via a comment section under each video lesson.
What if I don't like the course?
PyFi offers a no questions asked, 30 day money-back guarantee. If you aren't completely satisfied with the course, send us an email and we will refund your money up to one month after you enroll.
How can we afford to do this? Because fewer than 1% of our customers ask for refunds. PyFi provides the best Python training on the market, and we know you are going to love it.
Do I need to work from a specific OS?
No. You can work through PyFi content from any OS.
Do I need to understand investing and portfolios to take the courses?
The finance specific topics that are discussed in the courses can be picked up as you work through each topic.
Have any questions? We’re ready to help!
