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Learn Python for
Private Equity
in 30 Days or Less. Guaranteed.
4.8 Star Rating
Why Python for
Pri
Do you work with large datasets?
Do you spend time on repetitive tasks?
Python outperforms Excel in three key areas.
Automations
Visualization
Predictive Analysis
Data Extraction & Transformation
Automate data pulls from APIs, web scraping, or file systems, then clean and reformat it for analysis. Python's "pandas" library makes data wrangling efficient and scalable.
Financial Reporting & Dashboards
Generate and update reports of PowerPoint presentations automatically. Libraries like "openpyxl" and "pptx" are commonly used to automate Excel and PowerPoint reporting workflows.
Trade Execution & Monitoring
Python scripts can automate trading strategies, monitor positions, and set automated alerts for certain market conditions.
Risk Management
Automate credit scoring, risk calculations, or stress testing using financial models, reducing manual work, and ensuring timely risk assessments.
Data Exploration and Trends
Use libraries like "Matplotlib," "Seaborn," and "Plotly" to create detailed visuals like line charts, bar charts, and histograms, aiding you in spotting trends and anomalies in financial data.
Interactive Dashboards
Tools like "Plotly Dash" allow users to build web-based dashboards with interactive features, ideal for sharing live, data-driven insights with stakeholders or clients.
Portfolio and Risk Visualizations
Python makes it simple to visualize portfolio performance, risk exposure, and diversification, providing finance professionals with insights into asset correlations, return distributions, and value-at-risk (VaR).
Real-Time Financial Data
With integrations for APIs, Python can visualize real-time stock prices, bond yields, or other financial indicators, allowing users to analyze current market data.
Predictive Modeling
Build models to predict stock prices, default probabilities, or customer lifetime value. Libraries like "scikit-learn" and "TensorFlow" are popular for creating models that forecast financial outcomes.
Algorithmic Trading
Develop machine learning-based trading algorithms that adjust strategies based on market data. These can range from simple moving-average strategies to complex reinforcement learning models.
Risk Analysis and Credit Scoring
Machine learning models can be trained on historical data to evaluate creditworthiness or calculate expected default rates.
Customer Segmentation
Python helps identify customer segments based on transaction patterns or demographics, using techniques like clustering.
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.
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.
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.
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
Pricing Plan
Simplified pricing made to scale with your organization.
