| Intermediate | CPD: 14 hours | 2 days | Live |
Description
Python has recently become one of the most sought-after skills in finance. This highly practical course will get participants familiar with the Python language, and how it can be applied in finance.
Learning Outcomes
By attending this course, you will:
- Explore the Jupyter user interface
- Understand Python data types and naming conventions
- Gain familiarity with operators, expressions, statements, and flow control
- Learn how to format output and present information neatly
- Work with modules
- Examine data structures like collections, lists, dictionaries, and tuples
- Develop functions and classes for code reusability
- Learn how to gracefully handle errors and exceptions
- Read and write from files
- Explore a range of essential Python libraries, like NumPy, SciPy, Matplotlib, Pandas, and others
- Create a useful finance application
Who Should Attend
Anyone who needs to learn Python, especially for financial applications.
Prerequisites
It will be useful, though not essential, to have experience of another programming language like JavaScript, C#, or similar.
Book Now!Seminar Content
Introduction
- Introduction to Python
- computer Working with the Jupyter Notebook
- Language syntax
- Variables and data types
- Basic functions
- Operators
- computer Challenge – factorials
Input, Output, and Formatting
- Input and output
- Formatting numbers and strings
- computer Challenge – formatting
Modules
- What are modules?
- How to import modules
- computer Challenge – using the math module
Program Flow
- Flow control
- Iteration
- For vs. while
- Conditional statements
- computer Challenge – using loops
Collections
- Collections
- Lists
- Tuples
- computer Challenge – creating and manipulating lists
- List comprehension
- Dictionaries
- computer Challenge – creating a dictionary
Functions
- Built-in functions
- String functions
- Exporting functions
- Lambda functions
- computer Challenge – creating an option pricing function
Exception handling
- How to handle exceptions
- Common errors
Classes
- What is a class?
- How to write a class
- computer Challenge – creating a class
- Reading from a file
- Writing to a file
- computer Challenge – analyzing a text file
Python Libraries
- Statistics
- NumPy
- computer Challenge – working with the normal distribution
- SciPy
- computer Challenge – distribution fitting
- Matplotlib and Seaborn
- Basic plotting
- Formatting charts
- Multiple charts
- computer Challenge – data visualization
- NumPy vectorization
- computer Challenge – Monte Carlo simulation
- Pandas
- Working with pandas
- computer Challenge – analyzing stock data
When and Where
21 Jan 2025 - 22 Jan 2025
09:00-17:00
New York
Data Science
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Other Dates and Locations
Search for Introduction to Python in our course schedule for alternative dates and locations where this course is offered.