Key Information
Python for Finance Immersive
Python for Data Science Immersive
Python Immersive
$1,295 18 Hours
$1,495 30 Hours
$1,495 30 Hours
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Learn More
Learn More
Download Syllabus
Download Syllabus
Download Syllabus
Overview
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.
Learn Python and boost your career with data science. You’ll learn the world’s most popular programming language through real-world dataset analysis. Manipulate data, learn how to make predictions, and gain the foundational skills you need to use Python on the job or start your data science career.
Learn to code in this beginner hands-on Python programming course. This course is designed to give you a comprehensive understanding of Python programming fundamentals and get you coding on your own. You'll learn how to professionally structure your programs and solve real-world challenges using Object-Oriented Programming. You'll also work on your own coding projects for your portfolio, and learn the common questions asked in a Python job interview.
Prerequisite
Python / Data: Participants should be familiar with concepts from Python for Data Science Bootcamp or Python Programming 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 fundemantals. Those without a background in finance should contact us after registration to access a free on-demand supplemental guide. 
Open to Beginners
Open to Beginners
Location
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
185 Madison Ave, NYC or Live Online
Schedule
Next start date: November 1–3, Monday to Wednesday, 10–5pm
Other scheduling options Weekdays, weeknights, or weekends
View full schedule
Next start date: October 11–15, Monday to Friday, 10–5pm
Other scheduling options Weekdays, weeknights, or weekends
View full schedule
Next start date: November 10–16, Monday to Friday, 10–5pm
Other scheduling options Weekdays or weeknights
View full schedule
Certification
Receive a Certificate of Completion
Receive a Certificate of Completion
Receive a Certificate of Completion
Free Retake Within 1 Year See our class policies for more details
Workbook Included
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.
  • Shorter courses such as this are already affordably priced and are not eligible for discounts.
Payment Plan See our payment plan FAQ for more details
12-Month FinancingSee our Climb Credit FAQ for more details
Target Audience
Anyone

  • New programmers and people who have never coded before

  • Anyone who wants to break into data science, machine learning, and the tech industry 

  • Analysts who work with Excel and are ready to upgrade with Python's ecosystem and automation capabilities

  • Someone working on a solid foundation before diving into machine learning
Anyone
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
  • Programming foundations including objects, loops, and functions
  • The object-oriented programming paradigm 
  • How to work with different types of data such as strings, lists, and integers
  • Selectively alter the control flow of your programming with conditional statements 
  • Analyze tabular data using Python libraries NumPy and Pandas
  • Create data visualizations with Matplotlib 
  • Predict outcomes using linear regression with Scikit-Learn
  • Gain a fundamental understanding of the Python programming language
  • Become a proficient Python programmer by learning along with skilled mentors.
  • Learn how to search and navigate tech documentation and efficiently handle errors and exceptions
  • Get comfortable developing Python programs on your own through a series of coding exercises
  • Become familiar with industry standards and learn the best practices for writing code
  • Master your programming skills by working on real-life projects; perfect for resume building
  • Create two of your projects for your portfolio of code