*These banks have used one or both of PyFi’s self-study courses to introduce their financiers to Python programming and its application in the world of finance.
Why Python for
Pri
Why are major companies like Amazon, Microsoft, Goldman Sachs, JP Morgan, Apollo,
Blackrock, etc., increasingly preferring or requiring Python for their finance employees?
Python outperforms Excel in three key areas.
Automations
Visualization
Predictive Analysis
Data Extraction & Transformation
Excel:
- Manually download financial data from external sources (e.g., Bloomberg, ERP systems).
- Use Excel to clean, transform, and organize data (e.g., currency conversions, date reformatting).
- Save processed files into standardized templates for further analysis.
Estimated Time: 6-10 hours per dataset.
Python:
- Use APIs (e.g., Bloomberg Terminal API, Alpha Vantage) or web scraping (e.g., "BeautifulSoup") to automate data extraction.
- Process data using "Pandas" for transformations, such as currency normalization and date conversions, programmatically.
- Export cleaned and transformed datasets directly to desired formats (e.g., CSV, SQL database).
Estimated Time: 1-2 hours for setup; ongoing runs take seconds.
Efficiency Gains:
- Reduces data extraction and transformation time by 80-90%.
- Ensured standardized outputs for all datasets greatly reducing the opportunity for human caused error.
- Handles lager datasets with ease, accommodating diverse data sources.
Financial Reporting & Dashboards
Excel:
- Compile data manually from various systems (e.g., accounting software, ERP).
- Build static financial reports (e.g., P&L, balance sheet) using formulas and pivot tables.
- Update reports periodically, requiring manual recalculations and formatting.
Estimated Time: 8-12 hours per reporting cycle.
Python:
- Automate data aggregating using Python scripts to pull from APIs or databases.
- Generate financial reports programmatically with libraries like "Matplotlib," "Plotly," or "OpenPyXL" for styled Excel outputs.
- Build interactive dashboards using "Dash" or "Streamlit" for real-time financial insights.
Estimated Time: 2-3 hours to set up; updates occur in real time.
Efficiency Gains:
- Reduces reporting time by 75-85%
- Provides live dashboards with automatically refreshed data.
- Delivers consistent and branded report formats.
Risk Management
Excel:
- Manually collect data for risk metrics (e.g., portfolio exposures, market data) and enter into Excel.
- Calculate key risk metrics (e.g., beta, standard deviation, VAR) using custom formulas.
- Compile static risk reports and update chats for board presentations.
Estimated Time: 10-15 hours per risk assessment.
Python:
- Automate data ingestion for market and portfolio data via APIs or database queries.
- Calculate risk metrics programmatically using libraries like "scipy" and "NumPy."
- Generate dynamic, interactive risk dashboards with "Plotly" or "Dash," including drill-down capabilities.
Estimated Time: 2-4 hours for setup; ongoing tasks occur in real time.
Efficiency Gains:
- Reduces risk analysis and reporting time by 75-85%.
- Automates complex calculations, minimizing errors.
- Allows for more granular insights and real-time tracking.
Report Consolidation
Excel:
- Manually collect individual reports from various teams or departments.
- Copy data into a single workbook, adjusting formats and aligning metrics.
- Use pivot tables or formulas to create a unified summary.
Estimated Time: 6-10 hours per reporting cycle.
Python:
- Use "Pandas" to merge and align departmental data programmatically.
- Automate formatting and metric alignment to ensure consistency.
- Generate a consolidated report in Excel, PDF, or dashboard format directly using libraries like "XlsxWriter" or "Plotly."
Estimated Time: 2-3 hours for initial setup; updates occur in minutes.
Efficiency Gains:
- Reduces reporting time by up to 70%
- Ensures unified formatting and structure across all reports.
- Handles more data sources and lager datasets efficiently.
Data Exploration and Trends
Excel:
- Import data into Excel and create static charts (e.g., line charts, bar charts) to visualize historical trends, such as revenue growth or portfolio returns.
- Use filters and slicers to explore subsets of data manually.
- Customize chart formatting for reports, adjusting axes, labels and colors.
Estimated Time: 4-6 hours for initial setup and formatting; additional time needed for updates.
Python:
- Use "Matplotlib" or "Seaborn" to programmatically generate detailed trend chats (e.g., revenue trends, ROI over time).
- Enable exploration by building interactive widgets with "Plotly" or "Altair" for filtering and dynamic views.
- Automate chat updates directly from a live data sources, eliminating the need for manual adjustments.
Estimated Time: 1-2 hours for initial setup; updates occur automatically.
Efficiency Gains:
- Reduces chart creation and updates by 75-85%
- Provides tools for real-time data slicing and exploration, enhancing insight generation.
- Maintains professional, uniform visuals across datasets and reports.
Interactive Dashboards
Excel:
- Use pivot tables and slicers to create static dashboards for financial metrics (e.g., revenue breakdowns or cost trends).
- Manually adjust charts and layouts for each reporting period or aduience.
- Refresh data manually, risking errors in linked formulas or connections.
Estimated Time: 6-10 hours per dashboard.
Python:
- Build interactive dashboards using "Dash" or "Streamlit," allowing real-time filtering, chart updates, and user inputs.
- Integrate dashboards riectly with live data sources (e.g., APIs, databases) to ensure real-time accuracy.
- Include advanced features like drill-downs, trend forecasts, and customizable views for specific audiences.
Estimated Time: 3-5 hours for setup; updates occur in real time.
Efficiency Gains
- Reduces dashboard creation time by up to 50%, with no ongoing update requirements.
- Enables dynamic exploration and customization, far surpassing Excel's capabilities.
- Connects to live data, ensuring all metrics are current and error-free.
Portfolio and Risk Visualizations
Excel:
- Create static charts (e.g., pie charts for portfolio allocation, scatter plots for risk vs. return) manually, adjusting ranges and formatting repeatedly.
- Use formulas and conditional formatting to highlight outliers or key risk areas.
- Update charts and calculations manually as portfolio data changes.
Estimates Time: 5-8 hours per visualization set.
Python:
- Use "Plotly" or "Matplotlib" to generate interactive visuals, such as dynamic scatter plots of risk-return profiles or heatmaps of portfolio diversification.
- Automate visual updates directly from portfolio management systems or databases.
- Add interactive features like tooltips, zooming, and filtering for deeper analysis.
Estimated Time: 2-3 hours for setup; updates occur automatically.
Efficiency Gains
- Reduces manual effort by 60-70%, freeing time for deeper analysis.
- Offers richer visual detail with interactivity, enabling better portfolio decisions.
- Maintains up-to-date visuals as portfolio data evolves.
Waterfall Charts
Excel:
- Manually calculate step-by-step changes in values (e.g., revenue to net income) and input these into chart data ranges.
- Use Excel's waterfall chart feature, requiring manual adjustments for formatting and colors.
- Repeat the process for each new dataset, rebuilding charts from scratch.
Estimated Time: 6-8 hours per chat cycle.
Python:
- Use "Matplotlib" or "Plotly" to programmatically generate waterfall charts from data.
- Automate the calculation of cumulative changes and visual formatting for presentation-ready outputs.
- Build reusable scripts to create charts for multiple datasets or reporting periods without manual intervention.
Estimated Time: 2-3 hours to set up; new charts update in minutes.
Efficiency Gains:
- Cuts chart creation time by 70-80%.
- Reusable scripts eliminate repetitive work for future cycles.
- Allows precise control over aesthetics and data handling.
Predictive Modeling
Excel:
- Manually prepare datasets by importing data and cleaning it with formulas (e.g., "IF," "VLOOKUP," and "FILTER."
- Build regression models using Excel's built-in tools like "Data Analysis ToolPak" or "LINEST,: often requiring multiple iterations to refine.
- Validate models by creating separate sheets for testing and comparing predictions against actual results.
Estimated Time: 8-12 hours per model, including preparation, building, and testing.
Python:
- Use "Pandas" to clean and prepare datasets programmatically.
- Leverage libraries like "scikit-learn" to build and refine regression, classification, or clustering models efficiently.
- Automate model validation with cross-validation techniques and metrics like "RMSE" or accuracy scores.
Estimated Time: 2-4 hours per model, depending on complexity.
Efficiency Gains:
- Reduces time spent by 60-70%.
- Handles lager datasets and more complex models with ease.
- Automates testing and validation for higher reliability.
Risk Analysis and Credit Scoring
Excel:
- Import and preprocess borrower data manually using formulas to alculate credit metrics like DSCR (Debt-Service Coverage Ratio) or credit utilization rates.
- Develop scoring models using weighted averages or ranking methods.
- Manually update credit scores when new data becomes available.
Estimated Time: 6-10 hours per analysis, including updates.
Python:
- Automate data preprocessing with "Pandas," applying transformations and aggregations for key metrics.
- Use machine learning models (e.g., decision trees or logistic regression in "scikit-learn") to predict default probabilities or assign credit scores dynamically.
- Integrate scripts with real-time data feeds to update credit scoring automatically.
Estimated Time: 3-5 hours for setup; updates occur automatically.
Efficiency Gains:
- Reduces analysis time by up to 50%.
- Produces more robust scoring models through machine learning.
- Eliminates manual recalculations for data updates greatly reducing the opportunity for human-error.
Debt Sustainability
Excel:
- Create a spreadsheet for debt metrics, such as interest coverage ratio and leverage ratio, and manually input assumptions for scenarios.
- Use Excel formulas and goal-seek tools to test sustainability under different conditions (e.g., interest rate hikes or revenue drops).
- Manually adjust and record results for different scenarios, risking errors in formula and references.
Estimated Time: 8-12 hours per analysis.
Python:
- Build automated models using "NumPy" or "Pandas" to calculate debt metrics and simulate sustainability scenarios programmatically.
- Use Monte Carlo simulations with libraries like "SciPy" to assess risk under various assumptions.
- Output results into structured reports or interactive dashboards for better decision-making.
Estimated Time: 2-4 hours for setup; scalability for additional scenarios is instantaneous.
Efficiency Gains:
- Reduces effort by 70-80%
- Tests thousands of scenarios quickly, enhancing depth of analysis.
- Minimizes errors and ensures consistent results.
Revenue Projections
Excel:
- Use historical data to calculate growth rates manually and build projection models with basic formulas (CAGR, TREND).
- Apply static growth scenarios and manually adjust forecasts for variables like seasonality or market conditions.
- Test sensitivity by duplicating sheets and running "what-if" scenarios manually.
Estimated Time: 10-15 hours for detailed projections with sensitivity analysis.
Python:
- Automate data preprocessing with "Pandas" to clean and organize historical revenue data.
- Apply advanced predictive techniques like "time-series forecasting" (e.g., ARIMA, Prophet) to generate accurate projections.
- Conduct sensitivity analysis programmatically, varying multiple inputs and visualizing impacts dynamically.
Estimated Time: 3-5 hours for setup and analysis.
Efficiency Gains:
- Reduces effort by 70-80%
- Improves forecasts with advanced models like time-series analysis.
- Simplifies running multiple scenarios without added manual effort.
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 financiers to Python programming and its application in the world of finance.
How Can We Help You?
What makes PyFi courses unique?
If you're looking to learn general Python this might not be the right class for you.
But if you'd rather understand how Python can be used in the world of finance, instead of building some irrelevant game, and if you'd like to learn the skills needed to build your own tools, then you're in the right place.
PyFi is built specifically for professionals in the world of finance and teaches you by relating concepts to excel so that you can learn faster and retain more of what you learn.
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.
How will learning Python help my finance career?
Firms are in the business of making profit, whether thats a bank, a fund, a corporation, etc. They hire you based on your ability to provide disproportionate value to them and their goals, a value that exceeds the cost of your salary. As technology changes, the level of demand of employee output continues to rise. In a pool of many talented candidates, having a competitive advantage that can make you more efficient and effective in your workflows and can make your predictive models more accurate means the value you are able to provide to your firm will be significantly greater than your contemporaries. Python is a competitive advantage in finance. In fact, more and more positions are preferring or requiring this skill to even be considered for the role in the first place.
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.
Whats included in the Bundle Offer?
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 full text PDF versions of each course.
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!
