Key Information
Python for Finance Immersive
Machine Learning & Automation for Finance
FinTech Bootcamp
$1,295 18 Hours
$1,295 18 Hours
$4,995 114 Hours
In this advanced course, you will cover the major Python financial libraries to gather and manipulate financial data. You will start by working with financial APIs to fetch financial, company, and economic data. We will analyze financial statements from the SEC website, including financial ratios derived from the income statement and balance sheet. You will build a risk management models using Python libraries to create VAR models and Monte Carlo simulation. We will learn how to apply statistical measures such as linear regression to financial uses such as stock prices.
This course will begin with advanced Python and statistic topics such as object-orientated programming and regression models. Students will learn how to apply these concepts using real-world financial data by building a predictive returns model using regression. We will then introduce students to important financial statements and ratios, and how to pull data from these statements and compute these important financial ratios using Python.
Learn Python for financial analysis, machine learning, data visualization, and SQL from experienced finance & engineering professionals in this immersive.
Python / Data: Participants should be familiar with concepts from Python for Data Science Bootcamp, including built-in data types, data structures, Pandas, and Matplotlib. Finance Background: Participants should be familiar with financial concepts such as NPV, IRR, financial statements, and stock fundamentals. Those without a background in finance should contact us after registration to access a free on-demand supplemental guide. 
This course requires students to be comfortable with Python and its data science libraries (NumPy and Pandas). If a student has not worked in Python before, we require a student to enroll in our Python for Data Science Bootcamp or Python for Finance Bootcamp before taking this course.
Open to Beginners
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
Not currently scheduled
Not currently scheduled
Next start date: Jan 9–Feb 7, Weekdays
Other scheduling options: Weekdays or weeknights
View full schedule
Receive a Certificate of Completion
Receive a Certificate of Completion
New York-licensed Certificate Program
Free Retake Within 1 Year See our class policies for more details
Workbook Included
Courses Included (Certificates & packages only)
  • 30 HoursPython for Data Science Immersive
  • 18 HoursSQL Bootcamp
  • 6 HoursPython for Automation
  • 30 HoursPython Machine Learning Immersive
  • 18 HoursPython for Finance Immersive
  • 30 HoursPython Data Visualization & Interactive Dashboards
Discounts See our discounts policies for more details
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
  • This is a discounted package of classes that is 15–25% off the individual class prices. Other discounts do not apply.
Payment Plan See our payment plan FAQ for more details
This program is eligible for our “pay-as-you-go” payment plan.
Financing See our Leif FAQ for more details
This program is eligible for 12-month financing through Leif.
Target Audience
What You’ll Learn
  • Gathering financial information with Python
  • Financial APIs
  • Analyzing 10k from the SEC website
  • Time value of money with Python
  • Risk management with Python
  • Calculating VAR
  • Financial Ratios
  • Fixed income with Python
  • Option strategies with Python
  • Portfolio management with Python
  • Regression analysis
See course details for more info
  • Python: Handling different types of data, such as integers, floats, and strings
  • Python: Analyze tabular data with Numpy and Pandas and graph the results using Matplotlib
  • Python: Learn how to pull data from multiple sources 
  • Machine Learning: Learn how to analyze a financial statement using Python 
  • Machine Learning: Learn financial topics such as WACC, NPV, IRR
  • Machine Learning: Learn how to build predictive models for internal budgeting
  • Data Visualization: Find data stories through exploratory data analysis
  • Data Visualization: Use advanced Python visualization libraries
  • Data Visualization: Build dashboards that utilize effective design
  • Data Visualization: Deploy dashboards and visualizations on a live server
  • SQL: Explore and alter data using a graphical user interface
  • SQL: Write queries to search through tables programmatically
  • SQL: Understand different data types and converting between them
  • SQL: Combine information across tables with join statements
  • SQL: Advanced techniques like subqueries and timestamp functions